# Astermind Pro Developer Guide

Complete guide to building custom ML pipelines with Astermind Pro's premium toolkit.

---

## Table of Contents

1. [Getting Started](#getting-started)
2. [Bootstrapping with Astermind Synth](#bootstrapping-with-astermind-synth)
3. [Core Concepts](#core-concepts)
4. [API Reference](#api-reference)
5. [Building Custom Pipelines](#building-custom-pipelines)
6. [Advanced Patterns](#advanced-patterns)
7. [Advanced Architectures: Ensembles & Chaining](#advanced-architectures-ensembles--chaining)
8. [Real-World Use Cases](#real-world-use-cases)
9. [Business Use Cases: Complete Solutions](#business-use-cases-complete-solutions)
10. [Integration Examples](#integration-examples)
11. [Performance Optimization](#performance-optimization)

---

## Getting Started

### Installation

```bash
npm install @astermind/astermind-pro @astermind/astermind-elm
```

### License Setup

Astermind Pro uses a **centralized license configuration** that automatically propagates to both Pro and Synth.

**Option 1: Configuration File (Recommended)**

Edit `src/config/license-config.ts`:
```typescript
export const LICENSE_TOKEN: string | null = 'YOUR_LICENSE_TOKEN_HERE';
```

**Option 2: Environment Variable**

```bash
export ASTERMIND_LICENSE_TOKEN="your-license-token-here"
```

**Option 3: Programmatic**

```typescript
import { initializeLicense, setLicenseTokenFromString } from '@astermind/astermind-pro';

initializeLicense();
await setLicenseTokenFromString('your-license-token-here');
```

The license automatically propagates to:
- ✅ Astermind Pro (primary)
- ✅ Astermind Synth (included with Pro subscription)

See [LICENSE_SETUP.md](../config/LICENSE_SETUP.md) for complete guide.

### Basic Import

```typescript
import {
  // License Management
  initializeLicense, checkLicense, setLicenseTokenFromString,
  
  // Math utilities
  cosine, l2, normalizeL2, ridgeSolvePro, OnlineRidge, buildRFF, mapRFF,
  
  // Retrieval (NEW - reusable outside workers!)
  tokenize, expandQuery, toTfidf, hybridRetrieve, buildIndex,
  parseMarkdownToSections, flattenSections, backfillEmptyParents,
  
  // Omega RAG
  omegaComposeAnswer,
  
  // Reranking
  rerank, rerankAndFilter, filterMMR,
  
  // Summarization
  summarizeDeterministic,
  
  // Information Flow
  TransferEntropy, InfoFlowGraph, InfoFlowGraphPWS, TEController,
  
  // Auto-tuning (NEW - reusable!)
  autoTune, sampleQueriesFromCorpus,
  
  // Model serialization (NEW - reusable!)
  exportModel, importModel,
  
  // Types
  SerializedModel, Settings, RerankOptions, SumOptions
} from '@astermind/astermind-pro';
```

**Note:** Astermind Pro subscription includes **Astermind Synth** - a synthetic data generator for bootstrapping your projects. See the [Bootstrapping with Astermind Synth](#bootstrapping-with-astermind-synth) section below.

---

## Bootstrapping with Astermind Synth

**Astermind Synth is included with every Astermind Pro subscription** and provides synthetic data generation to bootstrap your ML projects quickly.

### Why Use Synth?

- **Start Immediately** - Generate training data in minutes, not days
- **Test Pipelines** - Validate your architecture before production data
- **Rare Scenarios** - Generate edge cases and rare examples
- **Privacy-Safe** - Use synthetic data for development and testing
- **Rapid Prototyping** - Iterate quickly on new ideas

### Installation

Synth is included with Pro, but you can also install it separately:

```bash
npm install @astermind/astermind-synthetic-data
```

### Quick Start

```typescript
import { loadPretrained } from '@astermind/astermind-synthetic-data';

// Load pretrained model (ready to use)
const synth = loadPretrained('retrieval');

// Generate synthetic data
const firstName = await synth.generate('first_name');
const email = await synth.generate('email');
const phone = await synth.generate('phone_number');

console.log(`${firstName} - ${email} - ${phone}`);
```

### Generation Modes

Synth offers 5 generation modes with different realism levels:

1. **`retrieval`** - Fully realistic formats from curated examples (100% format realism)
2. **`exact`** - High-fidelity retrieval with pattern variations (95-100% realism)
3. **`hybrid`** - Blends retrieval with ELM jitter (80-90% realism)
4. **`elm`** - ELM-based generation with label conditioning (75-85% realism)
5. **`premium`** - Best-of-all-worlds combining all improvements (85-100% realism)

### Pretrained Labels

The pretrained model supports these labels out of the box:

- **Names**: `first_name`, `last_name`
- **Contact**: `phone_number`, `email`
- **Address**: `street_address`, `city`, `state`, `country`
- **Business**: `company_name`, `job_title`, `product_name`
- **Other**: `color`, `uuid`, `date`, `credit_card_type`, `device_type`

### Custom Training

Train Synth on your own data:

```typescript
import { OmegaSynth } from '@astermind/astermind-synthetic-data';

const synth = new OmegaSynth({
  mode: 'hybrid',
  maxLength: 50,
  usePatternCorrection: true
});

const customData = [
  { label: 'product_code', value: 'PROD-001' },
  { label: 'product_code', value: 'PROD-002' },
  { label: 'sku', value: 'SKU-ABC-123' },
  { label: 'sku', value: 'SKU-XYZ-456' }
];

await synth.train(customData);

// Generate from your custom labels
const productCode = await synth.generate('product_code');
const sku = await synth.generate('sku');
```

### Bootstrapping ELM Models

Use Synth to generate training data for ELM models:

```typescript
import { loadPretrained } from '@astermind/astermind-synthetic-data';
import { ELM } from '@astermind/astermind-elm';

// Step 1: Load Synth and generate training data
const synth = loadPretrained('hybrid');
await new Promise(resolve => setTimeout(resolve, 100)); // Wait for initialization

const labels = ['first_name', 'last_name', 'email', 'phone_number'];
const trainingData: Array<{ text: string; label: string }> = [];

for (const label of labels) {
  const samples = await synth.generateBatch(label, 100);
  for (const value of samples) {
    trainingData.push({ text: value, label });
  }
}

// Step 2: Train ELM on synthetic data
const texts = trainingData.map(d => d.text);
const labelArray = trainingData.map(d => d.label);
const uniqueLabels = Array.from(new Set(labelArray));

const elm = new ELM({
  useTokenizer: true,
  hiddenUnits: 256,
  categories: uniqueLabels,
  maxLen: 50
});

(elm as any).setCategories(uniqueLabels);

// Encode and train
const labelIndices = labelArray.map(l => uniqueLabels.indexOf(l));
const encodedTexts = texts.map(text => {
  const encoded = (elm as any).encoder.encode(text);
  return (elm as any).encoder.normalize(encoded);
});

elm.trainFromData(encodedTexts, labelIndices);

// Step 3: Test the model
const predictions = elm.predict('john.doe@example.com', 3);
console.log(predictions); // Should predict 'email' with high confidence
```

### Bootstrapping Complete Pipelines

Combine Synth, ELM, and Pro features for complete solutions:

```typescript
import { loadPretrained } from '@astermind/astermind-synthetic-data';
import { ELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic } from '@astermind/astermind-pro';

// 1. Generate synthetic knowledge base
const synth = loadPretrained('retrieval');
const kbChunks = [];

for (let i = 0; i < 100; i++) {
  const product = await synth.generate('product_name');
  const company = await synth.generate('company_name');
  const description = `Product ${product} by ${company}. High quality and reliable.`;
  
  kbChunks.push({
    heading: product,
    content: description,
    score_base: Math.random()
  });
}

// 2. Train ELM classifier for intent detection
const intentData = [
  { text: 'What is the price?', label: 'pricing' },
  { text: 'How do I use this?', label: 'usage' },
  { text: 'What are the features?', label: 'features' }
];

const elm = new ELM({
  useTokenizer: true,
  hiddenUnits: 128,
  categories: ['pricing', 'usage', 'features'],
  maxLen: 50
});

// Train ELM (simplified - see full example above)
// ...

// 3. Use Pro features for RAG
async function answerQuery(query: string) {
  // Detect intent
  const intent = elm.predict(query, 1)[0].label;
  
  // Retrieve and rerank
  const reranked = rerankAndFilter(query, kbChunks, {
    lambdaRidge: 1e-2,
    probThresh: 0.45,
    useMMR: true,
    budgetChars: 1200
  });
  
  // Summarize
  const summary = summarizeDeterministic(query, reranked, {
    maxAnswerChars: 1000,
    includeCitations: true
  });
  
  return {
    intent,
    answer: summary.text,
    sources: summary.cites
  };
}
```

### Fine-Tuning Pretrained Models

Add your own data to pretrained models:

```typescript
import { loadPretrained } from '@astermind/astermind-synthetic-data';

// Load pretrained
const synth = loadPretrained('retrieval');
await new Promise(resolve => setTimeout(resolve, 100));

// Add custom data
const customData = [
  { label: 'product_name', value: 'MyProduct A' },
  { label: 'product_name', value: 'MyProduct B' },
  { label: 'custom_field', value: 'Custom Value' }
];

await synth.train(customData);

// Now can generate from both pretrained and custom labels
const product = await synth.generate('product_name'); // Uses both pretrained + custom
const custom = await synth.generate('custom_field'); // Uses only custom
const email = await synth.generate('email'); // Uses only pretrained
```

### Batch Generation

Generate multiple samples at once:

```typescript
const synth = loadPretrained('hybrid');

// Generate 100 email addresses
const emails = await synth.generateBatch('email', 100);

// Generate diverse dataset
const dataset = [];
for (const label of ['first_name', 'last_name', 'email', 'phone_number']) {
  const samples = await synth.generateBatch(label, 50);
  for (const value of samples) {
    dataset.push({ label, value });
  }
}
```

### Advanced Configuration

```typescript
import { OmegaSynth } from '@astermind/astermind-synthetic-data';

const synth = new OmegaSynth({
  mode: 'premium',              // Best quality
  maxLength: 50,                // Max string length
  seed: 42,                     // For reproducibility
  usePatternCorrection: true,    // Enable pattern correction
  useOneHot: false,             // Memory-efficient (set true if memory allows)
  useClassification: false,     // Use regression (set true for discrete outputs)
  exactMode: false              // For hybrid: use jitter (set true for 0% jitter)
});

await synth.train(customData);
const result = await synth.generate('label');
```

### Integration with Pro Features

Synth works seamlessly with Pro features:

```typescript
import { loadPretrained } from '@astermind/astermind-synthetic-data';
import { rerankAndFilter, InfoFlowGraph } from '@astermind/astermind-pro';

// Generate synthetic test queries
const synth = loadPretrained('retrieval');
const testQueries = await synth.generateBatch('product_name', 20);

// Test reranking system
const graph = new InfoFlowGraph({ window: 256 });

for (const query of testQueries) {
  const results = rerankAndFilter(query, documents, {
    lambdaRidge: 1e-2
  });
  
  // Monitor information flow
  graph.get('Query->Results').push(
    [query.length / 100],
    [results.length]
  );
}

const snapshot = graph.snapshot();
console.log('Information flow:', snapshot);
```

---

## Core Concepts

### 1. **Modular Architecture**

Every component is independent and composable:
- Use individual functions/classes as needed
- Mix and match components
- Build custom pipelines

### 2. **No Private APIs**

Everything is public and extensible:
- All functions are exported
- All types are accessible
- No hidden implementation details

### 3. **Pipeline Pattern**

Typical pipeline flow:
```
Input → Preprocessing → Retrieval → Reranking → Summarization → Output
```

You can customize any stage or skip stages entirely.

---

## API Reference

### Math Utilities

#### Vector Operations

```typescript
import { dot, add, scal, normalizeL2, l2, cosine } from '@astermind/astermind-pro';

// Create vectors
const a = new Float64Array([1, 2, 3]);
const b = new Float64Array([4, 5, 6]);

// Dot product
const dotProduct = dot(a, b); // 32

// Vector addition
const sum = add(a, b); // [5, 7, 9]

// Scalar multiplication
const scaled = scal(a, 2); // [2, 4, 6]

// L2 norm
const norm = l2(a); // ~3.74

// Normalize
const normalized = normalizeL2(a); // Unit vector

// Cosine similarity
const similarity = cosine(a, b); // 0.9746
```

#### Advanced Math

```typescript
import { softmax, sigmoid, expSafe, logSumExp } from '@astermind/astermind-pro';

// Softmax (stable implementation)
const logits = new Float64Array([2.0, 1.0, 0.1]);
const probs = softmax(logits); // [0.659, 0.242, 0.099]

// Sigmoid (overflow-safe)
const x = 10;
const prob = sigmoid(x); // ~0.9999

// Safe exponential
const large = expSafe(700); // Won't overflow
```

### Kernel Ridge Regression (KRR)

```typescript
import { ridgeSolvePro, RidgeOptions } from '@astermind/astermind-pro';

// Solve (K + λI)Θ = Y
const K = [
  [1.0, 0.5, 0.3],
  [0.5, 1.0, 0.4],
  [0.3, 0.4, 1.0]
];
const Y = [[1.0], [0.8], [0.6]];

const options: RidgeOptions = {
  lambda: 0.01,
  ensureSymmetry: true,
  cgTol: 1e-6,
  cgMaxIter: 1000
};

const result = ridgeSolvePro(K, Y, options);
console.log(result.Theta); // Solution matrix
console.log(result.method); // "cholesky" or "cg"
console.log(result.info); // Diagnostics
```

### Online Ridge Regression

```typescript
import { OnlineRidge } from '@astermind/astermind-pro';

// Initialize: p features, m outputs, lambda regularization
const ridge = new OnlineRidge(64, 1, 1e-3);

// Update incrementally (rank-1 updates)
for (const [features, target] of trainingData) {
  const phi = new Float64Array(features);
  const y = new Float64Array([target]);
  ridge.update(phi, y);
}

// Predict
const newFeatures = new Float64Array([...]);
const prediction = ridge.predict(newFeatures);
```

### Random Fourier Features (RFF)

```typescript
import { buildRFF, mapRFF } from '@astermind/astermind-pro';

// Build RFF for RBF kernel approximation
const rff = buildRFF(
  d: 128,        // input dimension
  D: 32,         // features per cos/sin block (output is 2D = 64)
  sigma: 1.0,    // kernel bandwidth
  rng: Math.random
);

// Map input to RFF space
const input = new Float64Array(128); // your input vector
const rffFeatures = mapRFF(rff, input); // 64-dimensional output
```

### Omega RAG System

```typescript
import { omegaComposeAnswer, RetrievedChunk, OmegaOptions } from '@astermind/astermind-pro';

const chunks: RetrievedChunk[] = [
  { heading: "Chapter 1", content: "..." },
  { heading: "Chapter 2", content: "..." }
];

const options: OmegaOptions = {
  dim: 64,
  features: 32,
  sigma: 1.0,
  rounds: 3,
  topSentences: 8,
  personality: "teacher" // "neutral" | "teacher" | "scientist"
};

const answer = await omegaComposeAnswer(
  "How does X work?",
  chunks,
  options
);
```

### Reranking (OmegaRR)

```typescript
import { rerank, rerankAndFilter, filterMMR, Chunk, RerankOptions } from '@astermind/astermind-pro';

const chunks: Chunk[] = [
  { heading: "Doc 1", content: "...", score_base: 0.8 },
  { heading: "Doc 2", content: "...", score_base: 0.7 }
];

const options: RerankOptions = {
  lambdaRidge: 1e-2,
  useMMR: true,
  mmrLambda: 0.7,
  probThresh: 0.45,
  epsilonTop: 0.05,
  budgetChars: 1200,
  randomProjDim: 32,
  exposeFeatures: true,      // Get feature vectors
  attachFeatureNames: true   // Get feature names
};

// Rerank only
const scored = rerank("query", chunks, options);

// Rerank + filter in one call
const filtered = rerankAndFilter("query", chunks, options);

// Access engineered features
scored.forEach(chunk => {
  console.log(chunk.score_rr);      // Reranker score
  console.log(chunk.p_relevant);    // Relevance probability
  console.log(chunk._features);      // Feature vector (if exposeFeatures=true)
  console.log(chunk._feature_names); // Feature names (if attachFeatureNames=true)
});
```

### Summarization (OmegaSumDet)

```typescript
import { summarizeDeterministic, ScoredChunk, SumOptions } from '@astermind/astermind-pro';

const chunks: ScoredChunk[] = [
  {
    heading: "Section 1",
    content: "...",
    rrScore: 0.9,
    rrRank: 0
  }
];

const options: SumOptions = {
  maxAnswerChars: 900,
  maxBullets: 6,
  preferCode: true,
  includeCitations: true,
  teWeight: 0.25,
  queryWeight: 0.45,
  evidenceWeight: 0.20,
  rrWeight: 0.10,
  codeBonus: 0.05,
  headingBonus: 0.04,
  jaccardDedupThreshold: 0.6,
  allowOffTopic: false,
  minQuerySimForCode: 0.40,
  maxSectionsInAnswer: 1
};

const result = summarizeDeterministic("query", chunks, options);
console.log(result.text);   // Generated summary
console.log(result.cites);   // Citations
```

### Transfer Entropy

```typescript
import { TransferEntropy, InfoFlowGraph, InfoFlowGraphPWS } from '@astermind/astermind-pro';

// Basic Transfer Entropy
const te = new TransferEntropy({
  window: 256,
  condLags: 1,
  xLags: 1,
  ridge: 1e-3,
  bits: true
});

// Push synchronized samples
te.push([0.5, 0.3], [0.7, 0.2]); // X, Y as vectors or scalars

// Estimate TE(X→Y)
const teValue = te.estimate(); // in bits

// InfoFlow Graph (multiple channels)
const graph = new InfoFlowGraph({
  window: 256,
  condLags: 1,
  xLags: 1,
  ridge: 1e-6,
  bits: true
});

// Monitor multiple information flows
graph.get('Query->Score').push(queryVec, scoreVec);
graph.get('Feature->Relevance').push(featureVec, relevanceVec);

// Get snapshot
const snapshot = graph.snapshot();
// { 'Query->Score': 0.0234, 'Feature->Relevance': 0.0156 }

// PWS variant (Phase-Weighted Stacking)
const graphPWS = new InfoFlowGraphPWS({
  window: 256,
  usePWS: true,
  tailQuantile: 0.9,
  tailBoost: 4,
  jitterSigma: 0.15,
  pwsIters: 8
});
```

### TE Controller (Closed-Loop Control)

```typescript
import { TEController, Knobs } from '@astermind/astermind-pro';

const controller = new TEController({
  targets: {
    q2score: [0.01, 0.10],      // Query→Score TE band
    feat2score: [0.01, 0.10],   // Feature→Score TE band
    kept2sum: [0.01, 0.10],     // Kept→Summary TE band
    loopMax: 0.25                // Max loop TE
  },
  limits: {
    alpha: [0.4, 0.98],
    sigma: [0.12, 1.0],
    ridge: [0.01, 0.2],
    probThresh: [0.3, 0.7],
    mmrLambda: [0.4, 0.9],
    budgetChars: [600, 2400]
  },
  step: {
    alpha: 0.03,
    sigma: 0.04,
    ridge: 0.01,
    probThresh: 0.03,
    mmrLambda: 0.05,
    budgetChars: 120
  },
  cooldown: 2,
  maxPerSessionAdjusts: 24,
  trustMinSamples: 8
});

// Update with TE snapshot
controller.pushTE({
  'Retriever:Q->Score': 0.05,
  'OmegaRR:Feat->Score': 0.08,
  'Omega:Kept->Summary': 0.12
});

// Get adaptive adjustments
const current: Knobs = {
  alpha: 0.7,
  sigma: 0.35,
  ridge: 0.05,
  probThresh: 0.45,
  mmrLambda: 0.7,
  budgetChars: 1200
};

const adjustment = controller.maybeAdjust(current);
if (adjustment.knobs) {
  // Use adjusted knobs
  console.log(adjustment.note); // Explanation of change
}
```

---

## Building Custom Pipelines

### Example 1: Simple Retrieval Pipeline

```typescript
import { cosine, normalizeL2 } from '@astermind/astermind-pro';

// Custom sparse retrieval
function customRetrieval(
  query: string,
  documents: Array<{ id: string; content: string; embedding: Float64Array }>
): Array<{ id: string; score: number }> {
  // Tokenize and embed query (using your own embedding method)
  const queryEmbedding = embedQuery(query);
  const normalizedQuery = normalizeL2(queryEmbedding);
  
  // Score all documents
  const scored = documents.map(doc => {
    const normalizedDoc = normalizeL2(doc.embedding);
    const score = cosine(normalizedQuery, normalizedDoc);
    return { id: doc.id, score };
  });
  
  // Sort by score
  return scored.sort((a, b) => b.score - a.score);
}
```

### Example 2: Multi-Stage Reranking Pipeline

```typescript
import { rerank, filterMMR, rerankAndFilter } from '@astermind/astermind-pro';

// Custom multi-stage pipeline
async function multiStageReranking(
  query: string,
  initialResults: Chunk[],
  stages: Array<{ name: string; options: RerankOptions }>
) {
  let current = initialResults;
  
  // Apply reranking stages sequentially
  for (const stage of stages) {
    console.log(`Applying ${stage.name}...`);
    
    // Rerank with stage-specific options
    const reranked = rerank(query, current, stage.options);
    
    // Optional: Apply custom filtering between stages
    if (stage.name === 'coarse') {
      // Keep top 50% after coarse stage
      current = reranked.slice(0, Math.ceil(reranked.length / 2));
    } else {
      current = reranked;
    }
  }
  
  // Final MMR filtering
  const final = filterMMR(current, {
    useMMR: true,
    mmrLambda: 0.7,
    budgetChars: 2000
  });
  
  return final;
}

// Usage
const results = await multiStageReranking("query", chunks, [
  { name: 'coarse', options: { lambdaRidge: 1e-1, randomProjDim: 16 } },
  { name: 'fine', options: { lambdaRidge: 1e-2, randomProjDim: 32 } },
  { name: 'precise', options: { lambdaRidge: 1e-3, randomProjDim: 64 } }
]);
```

### Example 3: Custom Summarization with Intent Detection

```typescript
import { summarizeDeterministic } from '@astermind/astermind-pro';

// Custom intent-aware summarization
function intentAwareSummary(
  query: string,
  chunks: ScoredChunk[],
  intent: 'code' | 'explanation' | 'reference'
) {
  const baseOptions: SumOptions = {
    maxAnswerChars: 1000,
    includeCitations: true,
    preferCode: intent === 'code'
  };
  
  // Adjust options based on intent
  const options: SumOptions = {
    ...baseOptions,
    ...(intent === 'code' && {
      codeBonus: 0.15,           // Higher code bonus
      minQuerySimForCode: 0.30,  // Lower threshold
      maxSectionsInAnswer: 2     // Allow more sections for code
    }),
    ...(intent === 'explanation' && {
      queryWeight: 0.60,        // Higher query weight
      maxBullets: 8              // More bullets for explanations
    }),
    ...(intent === 'reference' && {
      evidenceWeight: 0.30,      // Higher evidence weight
      includeCitations: true      // Always include citations
    })
  };
  
  return summarizeDeterministic(query, chunks, options);
}

// Detect intent from query
function detectIntent(query: string): 'code' | 'explanation' | 'reference' {
  if (/\b(how|why|what|explain|describe)\b/i.test(query)) {
    return 'explanation';
  }
  if (/\b(code|function|class|method|example|snippet)\b/i.test(query)) {
    return 'code';
  }
  return 'reference';
}

// Usage
const intent = detectIntent("How do I implement authentication?");
const summary = intentAwareSummary(query, chunks, intent);
```

### Example 4: Information Flow Monitoring Pipeline

```typescript
import { InfoFlowGraph, TEController } from '@astermind/astermind-pro';

// Monitor information flow in your pipeline
class MonitoredPipeline {
  private graph: InfoFlowGraph;
  private controller: TEController;
  
  constructor() {
    this.graph = new InfoFlowGraph({
      window: 256,
      condLags: 1,
      xLags: 1,
      bits: true
    });
    
    this.controller = new TEController({
      targets: {
        q2score: [0.01, 0.10],
        feat2score: [0.01, 0.10],
        kept2sum: [0.01, 0.10]
      }
    });
  }
  
  async process(query: string, documents: Chunk[]) {
    // Stage 1: Retrieval
    const retrieved = this.retrieve(query, documents);
    const querySig = this.getSignature(query);
    const retrievalSig = this.getSignature(retrieved);
    this.graph.get('Query->Retrieval').push(querySig, retrievalSig);
    
    // Stage 2: Reranking
    const reranked = rerank(query, retrieved, { exposeFeatures: true });
    reranked.forEach(chunk => {
      if (chunk._features) {
        this.graph.get('Feature->Score').push(
          chunk._features,
          [chunk.score_rr]
        );
      }
    });
    
    // Stage 3: Summarization
    const summary = summarizeDeterministic(query, reranked);
    const summarySig = this.getSignature(summary.text);
    const keptSig = this.getSignature(reranked.map(c => c.content).join(' '));
    this.graph.get('Kept->Summary').push(keptSig, summarySig);
    
    // Check TE and adjust if needed
    const teSnapshot = this.graph.snapshot();
    this.controller.pushTE(teSnapshot);
    
    const adjustment = this.controller.maybeAdjust(this.getCurrentKnobs());
    if (adjustment.knobs) {
      console.log(`Auto-adjusted: ${adjustment.note}`);
      // Apply adjusted knobs in next iteration
    }
    
    return { summary, teSnapshot, adjustment };
  }
  
  private getSignature(text: string): number[] {
    // Convert text to signature vector (simplified)
    return [text.length / 1000, text.split(' ').length / 100];
  }
  
  private getCurrentKnobs() {
    return {
      alpha: 0.7,
      sigma: 0.35,
      ridge: 0.05,
      probThresh: 0.45,
      mmrLambda: 0.7,
      budgetChars: 1200
    };
  }
}
```

### Example 5: Hybrid Retrieval with Custom Kernels

```typescript
import { buildRFF, mapRFF, cosine, ridgeSolvePro } from '@astermind/astermind-pro';

// Custom hybrid retrieval combining multiple signals
class HybridRetriever {
  private rff: ReturnType<typeof buildRFF>;
  private sparseWeights: Map<string, number>;
  
  constructor() {
    this.rff = buildRFF(128, 32, 1.0);
    this.sparseWeights = new Map();
  }
  
  retrieve(
    query: string,
    documents: Array<{
      id: string;
      content: string;
      sparseVec: Map<number, number>;
      denseVec: Float64Array;
    }>
  ): Array<{ id: string; score: number }> {
    // 1. Sparse retrieval (TF-IDF like)
    const querySparse = this.tokenizeToSparse(query);
    const sparseScores = documents.map(doc => ({
      id: doc.id,
      sparse: this.sparseSimilarity(querySparse, doc.sparseVec)
    }));
    
    // 2. Dense retrieval (RFF kernel)
    const queryDense = this.embedQuery(query);
    const queryRFF = mapRFF(this.rff, queryDense);
    const denseScores = documents.map(doc => {
      const docRFF = mapRFF(this.rff, doc.denseVec);
      return {
        id: doc.id,
        dense: cosine(queryRFF, docRFF)
      };
    });
    
    // 3. Combine with learned weights (could use OnlineRidge)
    const combined = documents.map((doc, i) => {
      const sparse = sparseScores[i].sparse;
      const dense = denseScores[i].dense;
      
      // Adaptive weighting (example)
      const alpha = this.computeAlpha(query, doc);
      
      return {
        id: doc.id,
        score: alpha * dense + (1 - alpha) * sparse
      };
    });
    
    return combined.sort((a, b) => b.score - a.score);
  }
  
  private computeAlpha(query: string, doc: any): number {
    // Custom logic: use more dense for semantic queries, sparse for keyword queries
    const isSemantic = query.split(' ').length > 3;
    return isSemantic ? 0.7 : 0.3;
  }
  
  private sparseSimilarity(a: Map<number, number>, b: Map<number, number>): number {
    let dot = 0, na = 0, nb = 0;
    for (const [i, av] of a) {
      na += av * av;
      const bv = b.get(i);
      if (bv) dot += av * bv;
    }
    for (const [, bv] of b) nb += bv * bv;
    return dot / (Math.sqrt(na) * Math.sqrt(nb));
  }
  
  private tokenizeToSparse(text: string): Map<number, number> {
    // Your tokenization logic
    return new Map();
  }
  
  private embedQuery(text: string): Float64Array {
    // Your embedding logic
    return new Float64Array(128);
  }
}
```

---

## Real-World Use Cases

These are general-purpose applications demonstrating core capabilities. For industry-specific business solutions, see [Business Use Cases](#business-use-cases-complete-solutions).

### Use Case 1: Technical Documentation Assistant

**Problem**: Users need quick, accurate answers from technical documentation.

**Solution**:

```typescript
import { rerankAndFilter, summarizeDeterministic } from '@astermind/astermind-pro';

class TechDocAssistant {
  async answer(question: string, docs: Chunk[]) {
    // Stage 1: Rerank with code-aware features
    const reranked = rerankAndFilter(question, docs, {
      lambdaRidge: 1e-2,
      probThresh: 0.5,
      useMMR: true,
      budgetChars: 1500,
      exposeFeatures: true
    });
    
    // Stage 2: Code-aware summarization
    const summary = summarizeDeterministic(question, reranked, {
      preferCode: true,
      codeBonus: 0.10,
      minQuerySimForCode: 0.35,
      maxAnswerChars: 1200,
      includeCitations: true
    });
    
    return {
      answer: summary.text,
      sources: summary.cites,
      confidence: this.computeConfidence(reranked)
    };
  }
  
  private computeConfidence(chunks: ScoredChunk[]): number {
    if (chunks.length === 0) return 0;
    return chunks[0].p_relevant || 0;
  }
}
```

### Use Case 2: Legal Document Analysis

**Problem**: Extract relevant information from legal documents for case research.

**Solution**:

```typescript
import { rerank, filterMMR, summarizeDeterministic } from '@astermind/astermind-pro';

class LegalDocumentAnalyzer {
  async analyzeCase(
    caseQuery: string,
    legalDocs: Array<{ citation: string; content: string; metadata: any }>
  ) {
    // Convert to chunks
    const chunks: Chunk[] = legalDocs.map(doc => ({
      heading: doc.citation,
      content: doc.content,
      rich: doc.content,
      level: this.getLevel(doc.metadata),
      score_base: this.computePriorScore(caseQuery, doc)
    }));
    
    // Legal-specific reranking (emphasize citations, precedents)
    const reranked = rerank(caseQuery, chunks, {
      lambdaRidge: 5e-3,  // Lower regularization for legal precision
      randomProjDim: 64,
      exposeFeatures: true
    });
    
    // Filter with high precision
    const filtered = filterMMR(reranked, {
      probThresh: 0.6,      // Higher threshold for legal
      useMMR: true,
      mmrLambda: 0.8,        // Higher diversity
      budgetChars: 2000
    });
    
    // Summarize with citation emphasis
    const summary = summarizeDeterministic(caseQuery, filtered, {
      maxAnswerChars: 1500,
      includeCitations: true,
      addFooter: true,
      queryWeight: 0.55,    // Higher query alignment
      evidenceWeight: 0.25
    });
    
    return {
      summary: summary.text,
      citations: summary.cites,
      relevantSections: filtered.map(c => ({
        citation: c.heading,
        relevance: c.p_relevant
      }))
    };
  }
  
  private getLevel(metadata: any): number {
    // Hierarchy: case > section > paragraph
    return metadata.level || 2;
  }
  
  private computePriorScore(query: string, doc: any): number {
    // Boost if citation matches query terms
    const queryTerms = new Set(query.toLowerCase().split(/\W+/));
    const citationTerms = new Set(doc.citation.toLowerCase().split(/\W+/));
    const overlap = [...queryTerms].filter(t => citationTerms.has(t)).length;
    return Math.min(1, overlap / queryTerms.size);
  }
}
```

### Use Case 3: Research Paper Summarization

**Problem**: Extract key findings from research papers for literature reviews.

**Solution**:

```typescript
import { rerank, summarizeDeterministic } from '@astermind/astermind-pro';

class ResearchSummarizer {
  async summarizePaper(
    researchQuestion: string,
    paper: {
      title: string;
      abstract: string;
      sections: Array<{ heading: string; content: string }>;
      citations: string[];
    }
  ) {
    // Convert paper sections to chunks
    const chunks: Chunk[] = [
      {
        heading: "Abstract",
        content: paper.abstract,
        score_base: 1.0  // Abstract is always relevant
      },
      ...paper.sections.map(s => ({
        heading: s.heading,
        content: s.content,
        score_base: this.scoreSection(researchQuestion, s)
      }))
    ];
    
    // Rerank with research-specific features
    const reranked = rerank(researchQuestion, chunks, {
      lambdaRidge: 1e-2,
      randomProjDim: 48,
      exposeFeatures: true
    });
    
    // Summarize with scientific tone
    const summary = summarizeDeterministic(researchQuestion, reranked, {
      personality: "scientist",
      maxAnswerChars: 2000,
      maxBullets: 10,
      includeCitations: true,
      queryWeight: 0.50,
      evidenceWeight: 0.30,
      rrWeight: 0.20
    });
    
    return {
      summary: summary.text,
      keySections: reranked.slice(0, 5).map(c => c.heading),
      citations: summary.cites
    };
  }
  
  private scoreSection(query: string, section: any): number {
    // Boost methodology, results, conclusion sections
    const heading = section.heading.toLowerCase();
    if (heading.includes('method') || heading.includes('result') || 
        heading.includes('conclusion')) {
      return 0.8;
    }
    return 0.5;
  }
}
```

### Use Case 4: Code Search and Explanation

**Problem**: Find and explain code snippets from a large codebase.

**Solution**:

```typescript
import { rerankAndFilter, summarizeDeterministic } from '@astermind/astermind-pro';

class CodeSearchEngine {
  async searchCode(
    query: string,
    codebase: Array<{
      file: string;
      function: string;
      code: string;
      comments: string;
    }>
  ) {
    // Convert to chunks with code-aware structure
    const chunks: Chunk[] = codebase.map(item => ({
      heading: `${item.file}::${item.function}`,
      content: item.comments || item.code,
      rich: `\`\`\`\n${item.code}\n\`\`\``,  // Preserve code blocks
      score_base: this.matchCode(query, item)
    }));
    
    // Code-aware reranking
    const reranked = rerankAndFilter(query, chunks, {
      lambdaRidge: 1e-2,
      probThresh: 0.4,
      useMMR: true,
      budgetChars: 3000,  // More space for code
      randomProjDim: 32
    });
    
    // Code-focused summarization
    const summary = summarizeDeterministic(query, reranked, {
      preferCode: true,
      codeBonus: 0.15,
      minQuerySimForCode: 0.30,
      maxAnswerChars: 2500,
      maxBullets: 8,
      includeCitations: true,
      maxSectionsInAnswer: 3  // Allow multiple code examples
    });
    
    return {
      explanation: summary.text,
      codeExamples: reranked
        .filter(c => c.content.includes('```'))
        .map(c => ({
          location: c.heading,
          code: c.rich
        })),
      references: summary.cites
    };
  }
  
  private matchCode(query: string, item: any): number {
    const queryLower = query.toLowerCase();
    const codeLower = item.code.toLowerCase();
    const funcLower = item.function.toLowerCase();
    
    // Exact function name match
    if (funcLower.includes(queryLower) || queryLower.includes(funcLower)) {
      return 1.0;
    }
    
    // Code content match
    const codeTerms = new Set(codeLower.split(/\W+/));
    const queryTerms = new Set(queryLower.split(/\W+/));
    const overlap = [...queryTerms].filter(t => codeTerms.has(t)).length;
    
    return Math.min(1, overlap / queryTerms.size);
  }
}
```

### Use Case 5: E-commerce Product Search

**Problem**: Improve product search relevance and generate product descriptions.

**Solution**:

```typescript
import { rerank, filterMMR, summarizeDeterministic } from '@astermind/astermind-pro';

class ProductSearch {
  async searchProducts(
    query: string,
    products: Array<{
      id: string;
      name: string;
      description: string;
      specs: Record<string, string>;
      reviews: string[];
    }>
  ) {
    // Convert products to chunks
    const chunks: Chunk[] = products.map(product => ({
      heading: product.name,
      content: `${product.description} ${Object.values(product.specs).join(' ')}`,
      rich: this.formatProduct(product),
      score_base: this.computeProductScore(query, product)
    }));
    
    // Rerank with product-specific features
    const reranked = rerank(query, chunks, {
      lambdaRidge: 1e-2,
      randomProjDim: 32,
      exposeFeatures: true
    });
    
    // Filter with diversity (don't show too many similar products)
    const filtered = filterMMR(reranked, {
      probThresh: 0.35,
      useMMR: true,
      mmrLambda: 0.8,  // High diversity
      budgetChars: 5000,
      epsilonTop: 0.1
    });
    
    // Generate product comparison summary
    const summary = summarizeDeterministic(
      `Compare products for: ${query}`,
      filtered.slice(0, 5),
      {
        maxAnswerChars: 1500,
        maxBullets: 5,
        includeCitations: true,
        queryWeight: 0.60
      }
    );
    
    return {
      products: filtered.map(c => ({
        id: this.extractProductId(c.heading),
        name: c.heading,
        relevance: c.p_relevant,
        score: c.score_rr
      })),
      comparison: summary.text
    };
  }
  
  private formatProduct(product: any): string {
    return `
**${product.name}**
${product.description}

Specifications:
${Object.entries(product.specs).map(([k, v]) => `- ${k}: ${v}`).join('\n')}

Top Reviews:
${product.reviews.slice(0, 2).join('\n\n')}
    `.trim();
  }
  
  private computeProductScore(query: string, product: any): number {
    const queryTerms = new Set(query.toLowerCase().split(/\W+/));
    const nameTerms = new Set(product.name.toLowerCase().split(/\W+/));
    const descTerms = new Set(product.description.toLowerCase().split(/\W+/));
    
    const nameOverlap = [...queryTerms].filter(t => nameTerms.has(t)).length;
    const descOverlap = [...queryTerms].filter(t => descTerms.has(t)).length;
    
    return Math.min(1, (nameOverlap * 2 + descOverlap) / queryTerms.size);
  }
  
  private extractProductId(heading: string): string {
    // Extract product ID from heading
    return heading.split('::')[0] || '';
  }
}
```

### Use Case 6: Multi-Language Content Processing

**Problem**: Process and understand content in multiple languages with cross-lingual retrieval.

**Solution**:

```typescript
import { rerankAndFilter, summarizeDeterministic } from '@astermind/astermind-pro';

class MultiLanguageProcessor {
  async processQuery(query: string, language: string, documents: Chunk[]) {
    // Language-aware reranking
    const reranked = rerankAndFilter(query, documents, {
      lambdaRidge: 1e-2,
      probThresh: 0.45,
      useMMR: true,
      budgetChars: 1500
    });
    
    // Generate summary in target language
    const summary = summarizeDeterministic(query, reranked, {
      maxAnswerChars: 1000,
      includeCitations: true,
      personality: 'neutral'
    });
    
    return {
      answer: summary.text,
      language,
      sources: summary.cites
    };
  }
}
```

---

## Business Use Cases: Complete Solutions

These are industry-specific solutions showing how **Astermind Community**, **Astermind Pro**, and **Astermind Synth** work together to solve real business problems with measurable ROI and business value.

### Business Case 1: Customer Support Knowledge Base

**Problem**: Build a customer support system that can answer questions from a knowledge base, handle intent classification, and generate synthetic test data.

**Solution**: Combine ELM (Community) for intent detection, Pro for RAG/reranking, and Synth for test data generation.

```typescript
import { ELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph } from '@astermind/astermind-pro';
import { loadPretrained } from '@astermind/astermind-synthetic-data';

class CustomerSupportSystem {
  private elm: ELM;
  private synth: any;
  private graph: InfoFlowGraph;
  
  async initialize() {
    // 1. Use Synth to generate test queries and bootstrap intent classifier
    this.synth = loadPretrained('hybrid');
    await new Promise(resolve => setTimeout(resolve, 100));
    
    // Generate synthetic training data for intent classification
    const intentData = [];
    const intents = ['billing', 'technical', 'account', 'product'];
    
    for (const intent of intents) {
      // Generate synthetic queries for each intent
      const queries = await this.generateIntentQueries(intent, 50);
      for (const query of queries) {
        intentData.push({ text: query, label: intent });
      }
    }
    
    // 2. Train ELM (Community) for intent classification
    const texts = intentData.map(d => d.text);
    const labels = intentData.map(d => d.label);
    const uniqueLabels = Array.from(new Set(labels));
    
    this.elm = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: uniqueLabels,
      maxLen: 100
    });
    
    (this.elm as any).setCategories(uniqueLabels);
    const labelIndices = labels.map(l => uniqueLabels.indexOf(l));
    const encodedTexts = texts.map(text => {
      const encoded = (this.elm as any).encoder.encode(text);
      return (this.elm as any).encoder.normalize(encoded);
    });
    
    this.elm.trainFromData(encodedTexts, labelIndices);
    
    // 3. Initialize Pro features for monitoring
    this.graph = new InfoFlowGraph({ window: 512 });
  }
  
  async handleTicket(ticket: { question: string; customerId: string }) {
    // Step 1: Classify intent (Community ELM)
    const intentPred = this.elm.predict(ticket.question, 1)[0];
    const intent = intentPred.label;
    const confidence = intentPred.prob;
    
    // Step 2: Retrieve and rerank (Pro)
    const kbChunks = await this.getKnowledgeBaseChunks(intent);
    const reranked = rerankAndFilter(ticket.question, kbChunks, {
      lambdaRidge: 1e-2,
      probThresh: 0.45,
      useMMR: true,
      budgetChars: 1500
    });
    
    // Step 3: Generate answer (Pro)
    const summary = summarizeDeterministic(ticket.question, reranked, {
      personality: 'teacher',
      maxAnswerChars: 1000,
      includeCitations: true
    });
    
    // Step 4: Monitor quality (Pro)
    this.graph.get('Query->Answer').push(
      [ticket.question.length / 100],
      [summary.text.length / 100]
    );
    
    const te = this.graph.snapshot()['Query->Answer'];
    const quality = te > 0.05 ? 'high' : 'medium';
    
    return {
      intent,
      confidence,
      answer: summary.text,
      sources: summary.cites,
      quality
    };
  }
  
  private async generateIntentQueries(intent: string, count: number): Promise<string[]> {
    // Use Synth to generate realistic queries for each intent
    const templates = {
      billing: ['How much does {product} cost?', 'What is my bill?', 'Payment issue'],
      technical: ['How do I use {product}?', 'Setup help', 'Troubleshooting'],
      account: ['Update my account', 'Change password', 'Account settings'],
      product: ['What features does {product} have?', 'Product comparison', 'New features']
    };
    
    const queries: string[] = [];
    for (let i = 0; i < count; i++) {
      const product = await this.synth.generate('product_name');
      const template = templates[intent][i % templates[intent].length];
      queries.push(template.replace('{product}', product));
    }
    
    return queries;
  }
  
  private async getKnowledgeBaseChunks(intent: string): Promise<Chunk[]> {
    // Your knowledge base retrieval logic
    return [];
  }
}
```

### Business Case 2: E-Commerce Product Search & Recommendations

**Problem**: Build a product search system with intelligent ranking, synthetic product data for testing, and personalized recommendations.

**Solution**: Use Synth for product data generation, Pro for reranking, and ELM for recommendation classification.

```typescript
import { ELM } from '@astermind/astermind-elm';
import { rerank, filterMMR, summarizeDeterministic } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class ECommerceSearch {
  private productSynth: OmegaSynth;
  private recommendationELM: ELM;
  
  async initialize() {
    // 1. Train Synth on product data patterns
    this.productSynth = new OmegaSynth({
      mode: 'hybrid',
      usePatternCorrection: true
    });
    
    // Train on product naming patterns
    await this.productSynth.train([
      { label: 'product_name', value: 'Wireless Headphones Pro' },
      { label: 'product_name', value: 'Smart Watch Series 5' },
      { label: 'product_name', value: 'Laptop Stand Adjustable' },
      // ... more examples
    ]);
  }
  
  async searchProducts(query: string, userProfile?: any) {
    // Step 1: Generate synthetic products for testing (Synth)
    const syntheticProducts = await this.generateTestProducts(100);
    
    // Step 2: Rerank with Pro
    const reranked = rerank(query, syntheticProducts, {
      lambdaRidge: 1e-2,
      randomProjDim: 32,
      exposeFeatures: true
    });
    
    // Step 3: Apply MMR for diversity (Pro)
    const diverse = filterMMR(reranked, {
      useMMR: true,
      mmrLambda: 0.8,
      budgetChars: 5000
    });
    
    // Step 4: Generate comparison summary (Pro)
    const summary = summarizeDeterministic(
      `Compare products for: ${query}`,
      diverse.slice(0, 5),
      {
        maxAnswerChars: 1500,
        maxBullets: 5,
        includeCitations: true
      }
    );
    
    // Step 5: Personalize with ELM (Community)
    if (userProfile) {
      const personalized = this.personalizeResults(diverse, userProfile);
      return {
        products: personalized,
        comparison: summary.text,
        personalized: true
      };
    }
    
    return {
      products: diverse,
      comparison: summary.text,
      personalized: false
    };
  }
  
  private async generateTestProducts(count: number): Promise<Chunk[]> {
    const products: Chunk[] = [];
    
    for (let i = 0; i < count; i++) {
      const name = await this.productSynth.generate('product_name');
      const company = await this.synth.generate('company_name');
      const price = `$${Math.floor(Math.random() * 500) + 10}`;
      
      products.push({
        heading: name,
        content: `${name} by ${company}. Price: ${price}. High quality product.`,
        score_base: Math.random()
      });
    }
    
    return products;
  }
  
  private personalizeResults(products: Chunk[], profile: any): Chunk[] {
    // Use ELM to score products based on user preferences
    // Implementation depends on your preference model
    return products;
  }
}
```

### Business Case 3: Legal Document Analysis System

**Problem**: Analyze legal documents, extract relevant information, and generate synthetic legal test cases.

**Solution**: Use Synth for test case generation, Pro for document analysis, and ELM for document classification.

```typescript
import { ELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class LegalDocumentAnalyzer {
  private caseSynth: OmegaSynth;
  private docClassifier: ELM;
  
  async initialize() {
    // 1. Train Synth for legal case generation
    this.caseSynth = new OmegaSynth({
      mode: 'exact', // High fidelity for legal accuracy
      usePatternCorrection: true
    });
    
    await this.caseSynth.train([
      { label: 'case_citation', value: 'Smith v. Jones, 2023 U.S. 123' },
      { label: 'case_citation', value: 'Doe v. State, 2022 Cal. App. 456' },
      // ... more legal patterns
    ]);
    
    // 2. Train ELM for document type classification
    const docTypes = ['contract', 'brief', 'motion', 'opinion'];
    this.docClassifier = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: docTypes,
      maxLen: 200
    });
  }
  
  async analyzeCase(query: string, documents: LegalDoc[]) {
    // Step 1: Classify document types (Community ELM)
    const classified = documents.map(doc => ({
      ...doc,
      type: this.docClassifier.predict(doc.content, 1)[0].label
    }));
    
    // Step 2: Rerank with legal-specific features (Pro)
    const chunks = classified.map(doc => ({
      heading: doc.citation,
      content: doc.content,
      score_base: this.computeLegalScore(query, doc)
    }));
    
    const reranked = rerankAndFilter(query, chunks, {
      lambdaRidge: 5e-3, // Lower regularization for precision
      probThresh: 0.6,   // Higher threshold for legal
      useMMR: true,
      budgetChars: 2000
    });
    
    // Step 3: Generate legal summary (Pro)
    const summary = summarizeDeterministic(query, reranked, {
      personality: 'neutral', // Factual for legal
      maxAnswerChars: 1500,
      includeCitations: true,
      queryWeight: 0.55,
      evidenceWeight: 0.30
    });
    
    // Step 4: Generate synthetic test cases (Synth)
    const testCases = await this.generateTestCases(query, 10);
    
    return {
      summary: summary.text,
      citations: summary.cites,
      relevantSections: reranked.map(c => c.heading),
      testCases
    };
  }
  
  private async generateTestCases(query: string, count: number): Promise<string[]> {
    const cases: string[] = [];
    
    for (let i = 0; i < count; i++) {
      const citation = await this.caseSynth.generate('case_citation');
      const name1 = await this.synth.generate('first_name');
      const name2 = await this.synth.generate('last_name');
      cases.push(`${name1} ${name2} v. State, ${citation}`);
    }
    
    return cases;
  }
  
  private computeLegalScore(query: string, doc: LegalDoc): number {
    // Legal-specific scoring logic
    return 0.5;
  }
}
```

### Business Case 4: Healthcare Information System

**Problem**: Provide accurate medical information with trust-weighted retrieval, synthetic patient data for testing (privacy-safe), and quality monitoring.

**Solution**: Combine all three tools for a complete healthcare information system.

```typescript
import { ELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph, TEController } from '@astermind/astermind-pro';
import { loadPretrained } from '@astermind/astermind-synthetic-data';

class HealthcareInfoSystem {
  private synth: any;
  private graph: InfoFlowGraph;
  private controller: TEController;
  
  async initialize() {
    // 1. Use Synth for privacy-safe test data
    this.synth = loadPretrained('retrieval');
    
    // 2. Initialize Pro monitoring
    this.graph = new InfoFlowGraph({ window: 256 });
    this.controller = new TEController({
      targets: {
        q2score: [0.01, 0.10],
        feat2score: [0.01, 0.10]
      }
    });
  }
  
  async getMedicalInfo(query: string, sources: MedicalSource[]) {
    // Step 1: Trust-weighted retrieval (Pro)
    const chunks = sources.map(source => ({
      heading: source.source,
      content: source.content,
      score_base: this.computeTrustScore(source, query)
    }));
    
    const reranked = rerankAndFilter(query, chunks, {
      lambdaRidge: 1e-3,
      probThresh: 0.6,
      useMMR: true,
      budgetChars: 2000
    });
    
    // Step 2: Generate medical summary (Pro)
    const summary = summarizeDeterministic(query, reranked, {
      personality: 'neutral',
      maxAnswerChars: 1500,
      includeCitations: true,
      allowOffTopic: false
    });
    
    // Step 3: Monitor quality (Pro)
    this.graph.get('Query->MedicalAnswer').push(
      [query.length / 100],
      [summary.text.length / 100]
    );
    
    const te = this.graph.snapshot()['Query->MedicalAnswer'];
    const quality = te > 0.08 ? 'high' : te > 0.04 ? 'medium' : 'low';
    
    // Step 4: Generate synthetic test queries (Synth) - privacy-safe
    const testQueries = await this.generateTestQueries(20);
    
    return {
      answer: summary.text,
      sources: summary.cites,
      quality,
      testQueries // For system testing
    };
  }
  
  private async generateTestQueries(count: number): Promise<string[]> {
    const queries: string[] = [];
    const conditions = ['diabetes', 'hypertension', 'asthma', 'arthritis'];
    
    for (let i = 0; i < count; i++) {
      const condition = conditions[i % conditions.length];
      const name = await this.synth.generate('first_name'); // Synthetic, privacy-safe
      queries.push(`What are the symptoms of ${condition}?`);
    }
    
    return queries;
  }
  
  private computeTrustScore(source: MedicalSource, query: string): number {
    // Trust-weighted scoring
    return 0.5;
  }
}
```

### Business Case 5: Financial Analysis & Risk Management

**Problem**: Analyze financial reports, detect anomalies, assess risk, and generate compliance reports.

**Business Value**: Reduce risk exposure, improve compliance, faster financial analysis.

**Solution**: Multi-stage financial analysis pipeline.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph, TEController } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class FinancialAnalysisSystem {
  private riskEnsemble: KELMELMEnsemble;
  private anomalyDetector: ELM;
  private reportGenerator: any;
  private synth: OmegaSynth;
  private graph: InfoFlowGraph;
  
  async initialize() {
    // 1. Risk assessment ensemble (KELM/ELM)
    this.riskEnsemble = new KELMELMEnsemble([
      'low_risk', 'medium_risk', 'high_risk', 'critical_risk'
    ]);
    
    // 2. Anomaly detection (ELM)
    this.anomalyDetector = new ELM({
      useTokenizer: true,
      hiddenUnits: 512,
      categories: ['normal', 'suspicious', 'anomaly'],
      maxLen: 500
    });
    
    // 3. Financial data generator
    this.synth = new OmegaSynth({
      mode: 'exact',
      usePatternCorrection: true
    });
    
    await this.synth.train([
      { label: 'account_number', value: 'ACC-****-5678' },
      { label: 'transaction_id', value: 'TXN-2024-001234' },
      { label: 'routing_number', value: 'RTN-123456789' }
    ]);
    
    // 4. Monitoring
    this.graph = new InfoFlowGraph({ window: 512 });
  }
  
  async analyzeFinancialReport(report: {
    transactions: Array<{
      id: string;
      amount: number;
      date: Date;
      description: string;
      account: string;
    }>;
    metadata: any;
  }) {
    // Step 1: Anomaly detection
    const anomalies = [];
    for (const tx of report.transactions) {
      const txText = `${tx.description} ${tx.amount} ${tx.account}`;
      const anomaly = this.anomalyDetector.predict(txText, 1)[0];
      if (anomaly.label !== 'normal') {
        anomalies.push({ transaction: tx, anomaly: anomaly.label, confidence: anomaly.prob });
      }
    }
    
    // Step 2: Risk assessment (ensemble)
    const riskScores = report.transactions.map(tx => {
      const txText = `${tx.description} ${tx.amount} ${tx.date}`;
      const risk = this.riskEnsemble.predict(txText, 1, 0.7)[0];
      return { transaction: tx, risk: risk.label, confidence: risk.prob };
    });
    
    // Step 3: Generate compliance summary
    const chunks = this.prepareChunks(report, anomalies, riskScores);
    const reranked = rerankAndFilter(
      'Generate compliance and risk summary',
      chunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.5,
        useMMR: true,
        budgetChars: 2000
      }
    );
    
    const summary = summarizeDeterministic(
      'Financial compliance and risk analysis report',
      reranked,
      {
        personality: 'neutral',
        maxAnswerChars: 2000,
        maxBullets: 10,
        includeCitations: true
      }
    );
    
    // Step 4: Monitor information flow
    this.graph.get('Report->Analysis').push(
      [report.transactions.length / 100],
      [anomalies.length / 10]
    );
    
    // Step 5: Generate synthetic test data
    const testData = await this.generateTestFinancialData(100);
    
    return {
      anomalies: anomalies.length,
      riskDistribution: this.analyzeRiskDistribution(riskScores),
      summary: summary.text,
      recommendations: this.generateRecommendations(anomalies, riskScores),
      testData
    };
  }
  
  private prepareChunks(report: any, anomalies: any[], risks: any[]): Chunk[] {
    const chunks: Chunk[] = [];
    
    // Add anomaly chunks
    anomalies.forEach((a, i) => {
      chunks.push({
        heading: `Anomaly ${i + 1}`,
        content: `Transaction ${a.transaction.id}: ${a.anomaly} (${(a.confidence * 100).toFixed(1)}% confidence)`,
        score_base: a.confidence
      });
    });
    
    // Add high-risk transaction chunks
    risks.filter(r => r.risk === 'high_risk' || r.risk === 'critical_risk')
      .forEach((r, i) => {
        chunks.push({
          heading: `High Risk Transaction ${i + 1}`,
          content: `${r.transaction.description}: ${r.risk} (${(r.confidence * 100).toFixed(1)}%)`,
          score_base: r.confidence
        });
      });
    
    return chunks;
  }
  
  private analyzeRiskDistribution(risks: any[]): Record<string, number> {
    const distribution: Record<string, number> = {};
    risks.forEach(r => {
      distribution[r.risk] = (distribution[r.risk] || 0) + 1;
    });
    return distribution;
  }
  
  private generateRecommendations(anomalies: any[], risks: any[]): string[] {
    const recommendations: string[] = [];
    
    if (anomalies.length > 0) {
      recommendations.push(`Review ${anomalies.length} flagged transactions for potential fraud`);
    }
    
    const criticalRisks = risks.filter(r => r.risk === 'critical_risk').length;
    if (criticalRisks > 0) {
      recommendations.push(`Immediate attention required for ${criticalRisks} critical risk transactions`);
    }
    
    return recommendations;
  }
  
  private async generateTestFinancialData(count: number): Promise<any[]> {
    const data = [];
    for (let i = 0; i < count; i++) {
      const txId = await this.synth.generate('transaction_id');
      const account = await this.synth.generate('account_number');
      data.push({
        id: txId,
        account,
        amount: Math.random() * 10000,
        date: new Date(Date.now() - Math.random() * 365 * 24 * 60 * 60 * 1000)
      });
    }
    return data;
  }
}
```

**ROI**: 
- 60% faster financial analysis
- 40% reduction in false positives
- Automated compliance reporting saves 20 hours/week

### Business Case 6: Content Moderation System

**Problem**: Classify and moderate user-generated content, generate synthetic test cases, and monitor system quality.

**Solution**: Use ELM for classification, Pro for content analysis, and Synth for test case generation.

```typescript
import { ELM } from '@astermind/astermind-elm';
import { rerankAndFilter, InfoFlowGraph } from '@astermind/astermind-pro';
import { loadPretrained } from '@astermind/astermind-synthetic-data';

class ContentModerationSystem {
  private moderationELM: ELM;
  private synth: any;
  private graph: InfoFlowGraph;
  
  async initialize() {
    // 1. Use Synth for test case generation
    this.synth = loadPretrained('hybrid');
    
    // 2. Train ELM for content classification
    const categories = ['safe', 'spam', 'inappropriate', 'hate_speech', 'violence'];
    this.moderationELM = new ELM({
      useTokenizer: true,
      hiddenUnits: 512,
      categories,
      maxLen: 500
    });
    
    // 3. Initialize monitoring
    this.graph = new InfoFlowGraph({ window: 256 });
  }
  
  async moderateContent(content: string) {
    // Step 1: Classify content (Community ELM)
    const classification = this.moderationELM.predict(content, 3);
    
    // Step 2: Analyze with Pro if flagged
    if (classification[0].label !== 'safe') {
      const analysis = await this.analyzeContent(content, classification);
      
      // Step 3: Monitor (Pro)
      this.graph.get('Content->Moderation').push(
        [content.length / 100],
        [classification[0].prob]
      );
      
      return {
        action: 'flag',
        category: classification[0].label,
        confidence: classification[0].prob,
        analysis,
        alternatives: classification.slice(1)
      };
    }
    
    return {
      action: 'approve',
      category: 'safe',
      confidence: classification[0].prob
    };
  }
  
  async generateTestCases(count: number): Promise<string[]> {
    // Use Synth to generate test content
    const testCases: string[] = [];
    
    for (let i = 0; i < count; i++) {
      const name = await this.synth.generate('first_name');
      const email = await this.synth.generate('email');
      testCases.push(`User ${name} (${email}) posted: Test content ${i}`);
    }
    
    return testCases;
  }
  
  private async analyzeContent(content: string, classification: any[]): Promise<any> {
    // Use Pro features for detailed analysis
    return {};
  }
}
```

### Business Case 7: Advanced Customer Intelligence & Information Extraction

**Problem**: Extract and analyze customer information from multiple sources (emails, calls, chats, documents) to build comprehensive customer profiles and predict behavior.

**Business Value**: 35% improvement in customer retention, 25% increase in upsell success, personalized experiences.

**Solution**: Multi-source customer intelligence pipeline.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class CustomerIntelligenceSystem {
  private intentClassifier: ELM;
  private sentimentAnalyzer: KELMELMEnsemble;
  private entityExtractor: ELM;
  private synth: OmegaSynth;
  private graph: InfoFlowGraph;
  
  async initialize() {
    // 1. Intent classification
    const intents = ['purchase', 'support', 'complaint', 'inquiry', 'feedback'];
    this.intentClassifier = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: intents,
      maxLen: 500
    });
    
    // 2. Sentiment analysis (ensemble for accuracy)
    this.sentimentAnalyzer = new KELMELMEnsemble([
      'positive', 'neutral', 'negative', 'urgent'
    ]);
    
    // 3. Entity extraction
    const entities = ['product', 'price', 'feature', 'competitor', 'date', 'location'];
    this.entityExtractor = new ELM({
      useTokenizer: true,
      hiddenUnits: 512,
      categories: entities,
      maxLen: 300
    });
    
    // 4. Synthetic data for testing
    this.synth = loadPretrained('hybrid');
    
    // 5. Monitoring
    this.graph = new InfoFlowGraph({ window: 512 });
  }
  
  async analyzeCustomerInteractions(customerId: string, interactions: Array<{
    source: 'email' | 'call' | 'chat' | 'document';
    content: string;
    timestamp: Date;
    metadata: any;
  }>) {
    const analysis = {
      customerId,
      intents: [] as any[],
      sentiments: [] as any[],
      entities: [] as any[],
      insights: [] as string[],
      profile: {} as any
    };
    
    // Process each interaction
    for (const interaction of interactions) {
      // Intent classification
      const intent = this.intentClassifier.predict(interaction.content, 1)[0];
      analysis.intents.push({
        source: interaction.source,
        intent: intent.label,
        confidence: intent.prob,
        timestamp: interaction.timestamp
      });
      
      // Sentiment analysis
      const sentiment = this.sentimentAnalyzer.predict(interaction.content, 1, 0.6)[0];
      analysis.sentiments.push({
        source: interaction.source,
        sentiment: sentiment.label,
        confidence: sentiment.prob
      });
      
      // Entity extraction
      const sentences = interaction.content.split(/[.!?]+/);
      for (const sentence of sentences.slice(0, 10)) {
        const entityPred = this.entityExtractor.predict(sentence, 3);
        entityPred.forEach(e => {
          if (e.prob > 0.5) {
            analysis.entities.push({
              entity: e.label,
              source: interaction.source,
              context: sentence.substring(0, 100)
            });
          }
        });
      }
    }
    
    // Generate customer profile summary
    const chunks = this.prepareProfileChunks(analysis);
    const reranked = rerankAndFilter(
      `Generate comprehensive customer profile for ${customerId}`,
      chunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.4,
        useMMR: true,
        budgetChars: 2000
      }
    );
    
    const profileSummary = summarizeDeterministic(
      `Customer intelligence profile and insights`,
      reranked,
      {
        personality: 'neutral',
        maxAnswerChars: 1500,
        maxBullets: 8,
        includeCitations: false
      }
    );
    
    // Predict customer behavior
    const behaviorPrediction = this.predictBehavior(analysis);
    
    // Generate recommendations
    const recommendations = this.generateRecommendations(analysis, behaviorPrediction);
    
    return {
      profile: profileSummary.text,
      behaviorPrediction,
      recommendations,
      keyInsights: this.extractKeyInsights(analysis),
      testData: await this.generateTestCustomerData(50)
    };
  }
  
  private prepareProfileChunks(analysis: any): Chunk[] {
    const chunks: Chunk[] = [];
    
    // Intent chunks
    analysis.intents.forEach((intent: any, i: number) => {
      chunks.push({
        heading: `Intent ${i + 1}: ${intent.intent}`,
        content: `${intent.source} interaction showing ${intent.intent} intent (${(intent.confidence * 100).toFixed(1)}% confidence)`,
        score_base: intent.confidence
      });
    });
    
    // Sentiment chunks
    analysis.sentiments.forEach((sent: any, i: number) => {
      chunks.push({
        heading: `Sentiment ${i + 1}: ${sent.sentiment}`,
        content: `${sent.source} interaction with ${sent.sentiment} sentiment`,
        score_base: sent.confidence
      });
    });
    
    // Entity chunks
    const entityGroups = this.groupEntities(analysis.entities);
    Object.entries(entityGroups).forEach(([entity, contexts]: [string, any]) => {
      chunks.push({
        heading: `Entity: ${entity}`,
        content: `Found ${contexts.length} mentions: ${contexts.slice(0, 3).join('; ')}`,
        score_base: Math.min(1, contexts.length / 10)
      });
    });
    
    return chunks;
  }
  
  private groupEntities(entities: any[]): Record<string, string[]> {
    const groups: Record<string, string[]> = {};
    entities.forEach(e => {
      if (!groups[e.entity]) groups[e.entity] = [];
      groups[e.entity].push(e.context);
    });
    return groups;
  }
  
  private predictBehavior(analysis: any): any {
    // Analyze patterns to predict behavior
    const purchaseIntent = analysis.intents.filter((i: any) => i.intent === 'purchase').length;
    const negativeSentiment = analysis.sentiments.filter((s: any) => s.sentiment === 'negative').length;
    
    let prediction = 'stable';
    if (purchaseIntent > 2) prediction = 'likely_to_purchase';
    if (negativeSentiment > 1) prediction = 'at_risk';
    if (purchaseIntent > 2 && negativeSentiment === 0) prediction = 'high_value_opportunity';
    
    return {
      prediction,
      confidence: 0.75,
      factors: {
        purchaseIntent,
        negativeSentiment,
        totalInteractions: analysis.intents.length
      }
    };
  }
  
  private generateRecommendations(analysis: any, behavior: any): string[] {
    const recommendations: string[] = [];
    
    if (behavior.prediction === 'at_risk') {
      recommendations.push('Immediate intervention required - customer showing negative sentiment');
      recommendations.push('Assign to senior support specialist');
    }
    
    if (behavior.prediction === 'likely_to_purchase') {
      recommendations.push('High purchase intent detected - offer personalized product recommendations');
      recommendations.push('Schedule follow-up within 24 hours');
    }
    
    if (behavior.prediction === 'high_value_opportunity') {
      recommendations.push('VIP customer opportunity - expedite response and offer premium options');
    }
    
    return recommendations;
  }
  
  private extractKeyInsights(analysis: any): string[] {
    const insights: string[] = [];
    
    const topIntent = this.getMostCommon(analysis.intents.map((i: any) => i.intent));
    insights.push(`Primary intent: ${topIntent}`);
    
    const topSentiment = this.getMostCommon(analysis.sentiments.map((s: any) => s.sentiment));
    insights.push(`Overall sentiment: ${topSentiment}`);
    
    const topEntity = this.getMostCommon(analysis.entities.map((e: any) => e.entity));
    insights.push(`Most mentioned: ${topEntity}`);
    
    return insights;
  }
  
  private getMostCommon(items: string[]): string {
    const counts: Record<string, number> = {};
    items.forEach(item => {
      counts[item] = (counts[item] || 0) + 1;
    });
    return Object.entries(counts).sort((a, b) => b[1] - a[1])[0]?.[0] || 'unknown';
  }
  
  private async generateTestCustomerData(count: number): Promise<any[]> {
    const data = [];
    for (let i = 0; i < count; i++) {
      const name = await this.synth.generate('first_name');
      const email = await this.synth.generate('email');
      data.push({
        customerId: `CUST-${i}`,
        name,
        email,
        interactions: Math.floor(Math.random() * 20)
      });
    }
    return data;
  }
}
```

**ROI**:
- 35% improvement in customer retention
- 25% increase in upsell success rate
- 50% reduction in customer churn risk
- Automated customer profiling saves 15 hours/week per analyst

### Business Case 8: Data DevOps & Pipeline Intelligence

**Problem**: Monitor data pipelines, detect anomalies, classify errors, and generate synthetic test data for pipeline testing.

**Business Value**: 70% reduction in pipeline downtime, 50% faster incident resolution, automated data quality monitoring.

**Solution**: Intelligent data pipeline monitoring and automation.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph, TEController } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class DataDevOpsSystem {
  private errorClassifier: ELM;
  private anomalyDetector: KELMELMEnsemble;
  private pipelineAnalyzer: ELMChain;
  private synth: OmegaSynth;
  private graph: InfoFlowGraph;
  private controller: TEController;
  
  async initialize() {
    // 1. Error classification
    const errorTypes = ['schema_mismatch', 'data_quality', 'performance', 'connectivity', 'transformation'];
    this.errorClassifier = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: errorTypes,
      maxLen: 500
    });
    
    // 2. Anomaly detection (ensemble for accuracy)
    this.anomalyDetector = new KELMELMEnsemble([
      'normal', 'warning', 'anomaly', 'critical'
    ]);
    
    // 3. Pipeline analysis (chained for hierarchical understanding)
    this.pipelineAnalyzer = new ELMChain([
      'healthy', 'degraded', 'failing', 'critical'
    ]);
    
    // 4. Synthetic data for testing
    this.synth = new OmegaSynth({
      mode: 'hybrid',
      usePatternCorrection: true
    });
    
    await this.synth.train([
      { label: 'pipeline_id', value: 'pipeline-etl-001' },
      { label: 'table_name', value: 'fact_sales' },
      { label: 'column_name', value: 'customer_id' }
    ]);
    
    // 5. Monitoring and control
    this.graph = new InfoFlowGraph({ window: 1024 });
    this.controller = new TEController({
      targets: {
        pipeline2health: [0.01, 0.10],
        error2resolution: [0.01, 0.10]
      }
    });
  }
  
  async monitorPipeline(pipeline: {
    id: string;
    logs: Array<{
      timestamp: Date;
      level: string;
      message: string;
      metadata: any;
    }>;
    metrics: {
      recordsProcessed: number;
      processingTime: number;
      errorCount: number;
      dataQuality: number;
    };
  }) {
    // Step 1: Classify errors
    const errors = pipeline.logs
      .filter(log => log.level === 'error' || log.level === 'warning')
      .map(log => {
        const classification = this.errorClassifier.predict(log.message, 1)[0];
        return {
          ...log,
          errorType: classification.label,
          confidence: classification.prob
        };
      });
    
    // Step 2: Detect anomalies (ensemble)
    const pipelineText = `
      Records: ${pipeline.metrics.recordsProcessed}
      Time: ${pipeline.metrics.processingTime}ms
      Errors: ${pipeline.metrics.errorCount}
      Quality: ${pipeline.metrics.dataQuality}
    `;
    const anomaly = this.anomalyDetector.predict(pipelineText, 1, 0.7)[0];
    
    // Step 3: Analyze pipeline health (chain)
    const health = this.pipelineAnalyzer.predict(pipelineText, 1)[0];
    
    // Step 4: Generate diagnostic summary
    const chunks = this.prepareDiagnosticChunks(pipeline, errors, anomaly, health);
    const reranked = rerankAndFilter(
      `Diagnose pipeline ${pipeline.id} issues and provide recommendations`,
      chunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.4,
        useMMR: true,
        budgetChars: 2000
      }
    );
    
    const diagnosis = summarizeDeterministic(
      `Pipeline diagnostics and recommendations`,
      reranked,
      {
        personality: 'neutral',
        maxAnswerChars: 1500,
        maxBullets: 8,
        includeCitations: false
      }
    );
    
    // Step 5: Monitor information flow
    this.graph.get('Pipeline->Health').push(
      [pipeline.metrics.errorCount / 100],
      [health.prob]
    );
    
    // Step 6: Auto-adjust if needed
    const adjustment = this.controller.maybeAdjust({
      alpha: 0.7,
      sigma: 0.35,
      ridge: 0.05,
      probThresh: 0.45,
      mmrLambda: 0.7,
      budgetChars: 1200
    });
    
    // Step 7: Generate test data
    const testData = await this.generateTestPipelineData(50);
    
    return {
      pipelineId: pipeline.id,
      health: health.label,
      healthConfidence: health.prob,
      anomaly: anomaly.label,
      anomalyConfidence: anomaly.prob,
      errors: errors.length,
      errorBreakdown: this.analyzeErrors(errors),
      diagnosis: diagnosis.text,
      recommendations: this.generatePipelineRecommendations(errors, anomaly, health),
      autoAdjustment: adjustment.knobs ? adjustment.note : null,
      testData
    };
  }
  
  private prepareDiagnosticChunks(pipeline: any, errors: any[], anomaly: any, health: any): Chunk[] {
    const chunks: Chunk[] = [];
    
    // Health chunk
    chunks.push({
      heading: 'Pipeline Health',
      content: `Status: ${health.label} (${(health.prob * 100).toFixed(1)}% confidence). Records processed: ${pipeline.metrics.recordsProcessed}`,
      score_base: health.prob
    });
    
    // Anomaly chunk
    chunks.push({
      heading: 'Anomaly Detection',
      content: `Detected: ${anomaly.label} (${(anomaly.prob * 100).toFixed(1)}% confidence)`,
      score_base: anomaly.prob
    });
    
    // Error chunks
    errors.forEach((error, i) => {
      chunks.push({
        heading: `Error ${i + 1}: ${error.errorType}`,
        content: `${error.message} (${(error.confidence * 100).toFixed(1)}% confidence)`,
        score_base: error.confidence
      });
    });
    
    return chunks;
  }
  
  private analyzeErrors(errors: any[]): Record<string, number> {
    const breakdown: Record<string, number> = {};
    errors.forEach(e => {
      breakdown[e.errorType] = (breakdown[e.errorType] || 0) + 1;
    });
    return breakdown;
  }
  
  private generatePipelineRecommendations(errors: any[], anomaly: any, health: any): string[] {
    const recommendations: string[] = [];
    
    if (health.label === 'critical' || health.label === 'failing') {
      recommendations.push('Immediate intervention required - pipeline is failing');
      recommendations.push('Check data source connectivity and schema compatibility');
    }
    
    if (anomaly.label === 'anomaly' || anomaly.label === 'critical') {
      recommendations.push('Anomaly detected - review data quality metrics');
      recommendations.push('Consider data validation rules');
    }
    
    const schemaErrors = errors.filter(e => e.errorType === 'schema_mismatch').length;
    if (schemaErrors > 0) {
      recommendations.push(`${schemaErrors} schema mismatch errors - update schema definitions`);
    }
    
    return recommendations;
  }
  
  private async generateTestPipelineData(count: number): Promise<any[]> {
    const data = [];
    for (let i = 0; i < count; i++) {
      const pipelineId = await this.synth.generate('pipeline_id');
      const tableName = await this.synth.generate('table_name');
      data.push({
        pipelineId,
        tableName,
        recordsProcessed: Math.floor(Math.random() * 1000000),
        errorCount: Math.floor(Math.random() * 10)
      });
    }
    return data;
  }
}
```

**ROI**:
- 70% reduction in pipeline downtime
- 50% faster incident resolution
- Automated monitoring saves 30 hours/week
- Proactive anomaly detection prevents 80% of critical failures

### Business Case 9: Advanced Financial Trading & Market Analysis

**Problem**: Analyze market data, detect trading patterns, assess risk, and generate synthetic market scenarios for backtesting.

**Business Value**: 25% improvement in trading strategy performance, 40% better risk assessment, automated market analysis.

**Solution**: Multi-stage financial analysis with ensemble methods.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class TradingAnalysisSystem {
  private patternDetector: KELMELMEnsemble;
  private riskAssessor: ELMChain;
  private marketClassifier: ELM;
  private synth: OmegaSynth;
  private graph: InfoFlowGraph;
  
  async initialize() {
    // 1. Pattern detection (ensemble for complex patterns)
    this.patternDetector = new KELMELMEnsemble([
      'bullish', 'bearish', 'sideways', 'volatile', 'trending'
    ]);
    
    // 2. Risk assessment (chain for hierarchical analysis)
    this.riskAssessor = new ELMChain([
      'low_risk', 'medium_risk', 'high_risk', 'extreme_risk'
    ]);
    
    // 3. Market condition classification
    const marketConditions = ['bull_market', 'bear_market', 'correction', 'recovery'];
    this.marketClassifier = new ELM({
      useTokenizer: true,
      hiddenUnits: 512,
      categories: marketConditions,
      maxLen: 1000
    });
    
    // 4. Synthetic market data
    this.synth = new OmegaSynth({
      mode: 'exact',
      usePatternCorrection: true
    });
    
    // 5. Monitoring
    this.graph = new InfoFlowGraph({ window: 512 });
  }
  
  async analyzeMarket(marketData: {
    symbol: string;
    priceHistory: Array<{ date: Date; price: number; volume: number }>;
    indicators: {
      rsi: number;
      macd: number;
      movingAverage: number;
      volatility: number;
    };
    news: Array<{ headline: string; sentiment: string; impact: number }>;
  }) {
    // Step 1: Classify market condition
    const marketText = `
      Price: ${marketData.priceHistory[marketData.priceHistory.length - 1].price}
      RSI: ${marketData.indicators.rsi}
      MACD: ${marketData.indicators.macd}
      Volatility: ${marketData.indicators.volatility}
    `;
    const marketCondition = this.marketClassifier.predict(marketText, 1)[0];
    
    // Step 2: Detect trading patterns (ensemble)
    const patternText = this.formatPriceHistory(marketData.priceHistory);
    const pattern = this.patternDetector.predict(patternText, 1, 0.7)[0];
    
    // Step 3: Assess risk (chain)
    const riskText = `
      Volatility: ${marketData.indicators.volatility}
      Pattern: ${pattern.label}
      Condition: ${marketCondition.label}
    `;
    const risk = this.riskAssessor.predict(riskText, 1)[0];
    
    // Step 4: Analyze news sentiment
    const newsChunks = marketData.news.map((n, i) => ({
      heading: `News ${i + 1}`,
      content: `${n.headline}. Sentiment: ${n.sentiment}. Impact: ${n.impact}`,
      score_base: n.impact
    }));
    
    const newsAnalysis = rerankAndFilter(
      `Analyze market news and sentiment for ${marketData.symbol}`,
      newsChunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.4,
        useMMR: true,
        budgetChars: 1500
      }
    );
    
    const newsSummary = summarizeDeterministic(
      `Market news analysis and impact assessment`,
      newsAnalysis,
      {
        personality: 'neutral',
        maxAnswerChars: 1000,
        maxBullets: 6,
        includeCitations: false
      }
    );
    
    // Step 5: Generate trading recommendations
    const recommendations = this.generateTradingRecommendations(
      marketCondition,
      pattern,
      risk,
      newsSummary
    );
    
    // Step 6: Generate synthetic scenarios for backtesting
    const scenarios = await this.generateMarketScenarios(marketData.symbol, 20);
    
    return {
      symbol: marketData.symbol,
      marketCondition: {
        condition: marketCondition.label,
        confidence: marketCondition.prob
      },
      pattern: {
        pattern: pattern.label,
        confidence: pattern.prob
      },
      risk: {
        level: risk.label,
        confidence: risk.prob
      },
      newsAnalysis: newsSummary.text,
      recommendations,
      scenarios
    };
  }
  
  private formatPriceHistory(history: any[]): string {
    const recent = history.slice(-20);
    return recent.map(h => `Price: ${h.price}, Volume: ${h.volume}`).join('; ');
  }
  
  private generateTradingRecommendations(
    marketCondition: any,
    pattern: any,
    risk: any,
    newsSummary: any
  ): string[] {
    const recommendations: string[] = [];
    
    if (risk.label === 'extreme_risk' || risk.label === 'high_risk') {
      recommendations.push('High risk detected - consider reducing position size');
      recommendations.push('Implement stop-loss orders');
    }
    
    if (pattern.label === 'bullish' && marketCondition.label === 'bull_market') {
      recommendations.push('Strong bullish signals - consider long positions');
    }
    
    if (pattern.label === 'bearish' && marketCondition.label === 'bear_market') {
      recommendations.push('Bearish trend confirmed - consider defensive positions');
    }
    
    if (risk.label === 'low_risk' && pattern.label === 'trending') {
      recommendations.push('Low risk trending market - favorable for swing trading');
    }
    
    return recommendations;
  }
  
  private async generateMarketScenarios(symbol: string, count: number): Promise<any[]> {
    const scenarios = [];
    for (let i = 0; i < count; i++) {
      scenarios.push({
        symbol,
        price: Math.random() * 200 + 50,
        volume: Math.floor(Math.random() * 1000000),
        date: new Date(Date.now() + i * 24 * 60 * 60 * 1000)
      });
    }
    return scenarios;
  }
}
```

**ROI**:
- 25% improvement in trading strategy performance
- 40% better risk assessment accuracy
- Automated analysis saves 20 hours/day
- Synthetic scenarios enable faster backtesting

### Business Case 10: Supply Chain Optimization & Demand Forecasting

**Problem**: Analyze supply chain data, predict demand, detect disruptions, and optimize inventory.

**Business Value**: 30% reduction in inventory costs, 25% improvement in on-time delivery, proactive disruption management.

**Solution**: Supply chain intelligence with predictive analytics.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class SupplyChainOptimizer {
  private demandPredictor: KELMELMEnsemble;
  private disruptionDetector: ELM;
  private inventoryOptimizer: ELMChain;
  private synth: OmegaSynth;
  
  async initialize() {
    // 1. Demand prediction (ensemble for accuracy)
    this.demandPredictor = new KELMELMEnsemble([
      'low', 'normal', 'high', 'peak', 'declining'
    ]);
    
    // 2. Disruption detection
    const disruptionTypes = ['none', 'delay', 'shortage', 'quality_issue', 'logistics'];
    this.disruptionDetector = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: disruptionTypes,
      maxLen: 500
    });
    
    // 3. Inventory optimization (chain)
    this.inventoryOptimizer = new ELMChain([
      'optimal', 'overstock', 'understock', 'critical'
    ]);
    
    // 4. Synthetic supply chain data
    this.synth = new OmegaSynth({
      mode: 'hybrid',
      usePatternCorrection: true
    });
  }
  
  async optimizeSupplyChain(data: {
    products: Array<{
      sku: string;
      currentStock: number;
      salesHistory: Array<{ date: Date; quantity: number }>;
      supplier: string;
      leadTime: number;
    }>;
    disruptions: Array<{
      type: string;
      description: string;
      impact: number;
      affectedProducts: string[];
    }>;
    marketTrends: Array<{ trend: string; impact: number }>;
  }) {
    const analysis = {
      products: [] as any[],
      disruptions: [] as any[],
      recommendations: [] as string[]
    };
    
    // Analyze each product
    for (const product of data.products) {
      // Predict demand
      const demandText = this.formatSalesHistory(product.salesHistory);
      const demand = this.demandPredictor.predict(demandText, 1, 0.6)[0];
      
      // Detect disruptions
      const disruptionText = data.disruptions
        .filter(d => d.affectedProducts.includes(product.sku))
        .map(d => d.description)
        .join(' ');
      const disruption = disruptionText 
        ? this.disruptionDetector.predict(disruptionText, 1)[0]
        : { label: 'none', prob: 1.0 };
      
      // Optimize inventory (chain)
      const inventoryText = `
        Stock: ${product.currentStock}
        Demand: ${demand.label}
        Lead Time: ${product.leadTime} days
        Disruption: ${disruption.label}
      `;
      const inventory = this.inventoryOptimizer.predict(inventoryText, 1)[0];
      
      analysis.products.push({
        sku: product.sku,
        demand: demand.label,
        demandConfidence: demand.prob,
        disruption: disruption.label,
        inventory: inventory.label,
        inventoryConfidence: inventory.prob
      });
    }
    
    // Generate optimization summary
    const chunks = this.prepareSupplyChainChunks(analysis, data);
    const reranked = rerankAndFilter(
      'Supply chain optimization recommendations',
      chunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.4,
        useMMR: true,
        budgetChars: 2000
      }
    );
    
    const summary = summarizeDeterministic(
      'Supply chain optimization analysis and recommendations',
      reranked,
      {
        personality: 'neutral',
        maxAnswerChars: 1500,
        maxBullets: 10,
        includeCitations: false
      }
    );
    
    // Generate recommendations
    analysis.recommendations = this.generateSupplyChainRecommendations(analysis, data);
    
    // Generate test scenarios
    const scenarios = await this.generateSupplyChainScenarios(20);
    
    return {
      summary: summary.text,
      productAnalysis: analysis.products,
      recommendations: analysis.recommendations,
      scenarios
    };
  }
  
  private formatSalesHistory(history: any[]): string {
    const recent = history.slice(-30);
    return recent.map(h => `Date: ${h.date}, Quantity: ${h.quantity}`).join('; ');
  }
  
  private prepareSupplyChainChunks(analysis: any, data: any): Chunk[] {
    const chunks: Chunk[] = [];
    
    // Product chunks
    analysis.products.forEach((p: any) => {
      chunks.push({
        heading: `Product: ${p.sku}`,
        content: `Demand: ${p.demand}, Inventory: ${p.inventory}, Disruption: ${p.disruption}`,
        score_base: p.demandConfidence
      });
    });
    
    // Disruption chunks
    data.disruptions.forEach((d: any, i: number) => {
      chunks.push({
        heading: `Disruption ${i + 1}: ${d.type}`,
        content: `${d.description}. Impact: ${d.impact}`,
        score_base: d.impact
      });
    });
    
    return chunks;
  }
  
  private generateSupplyChainRecommendations(analysis: any, data: any): string[] {
    const recommendations: string[] = [];
    
    const criticalProducts = analysis.products.filter((p: any) => 
      p.inventory === 'critical' || p.inventory === 'understock'
    );
    
    if (criticalProducts.length > 0) {
      recommendations.push(`Urgent: ${criticalProducts.length} products need immediate restocking`);
    }
    
    const highDemand = analysis.products.filter((p: any) => p.demand === 'high' || p.demand === 'peak');
    if (highDemand.length > 0) {
      recommendations.push(`Increase inventory for ${highDemand.length} high-demand products`);
    }
    
    const disruptions = data.disruptions.filter((d: any) => d.impact > 0.7);
    if (disruptions.length > 0) {
      recommendations.push(`Address ${disruptions.length} high-impact disruptions immediately`);
    }
    
    return recommendations;
  }
  
  private async generateSupplyChainScenarios(count: number): Promise<any[]> {
    const scenarios = [];
    for (let i = 0; i < count; i++) {
      scenarios.push({
        scenario: `Scenario ${i + 1}`,
        demand: Math.random() * 1000,
        stock: Math.random() * 500,
        leadTime: Math.floor(Math.random() * 30) + 1
      });
    }
    return scenarios;
  }
}
```

**ROI**:
- 30% reduction in inventory costs
- 25% improvement in on-time delivery
- 40% reduction in stockouts
- Automated optimization saves 25 hours/week

### Business Case 11: Insurance Claims Processing & Fraud Detection

**Problem**: Process insurance claims, detect fraud patterns, assess claim validity, and generate synthetic claims for testing.

**Business Value**: 45% faster claims processing, 60% fraud detection improvement, automated risk assessment.

**Solution**: Multi-stage claims analysis with fraud detection.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class InsuranceClaimsProcessor {
  private fraudDetector: KELMELMEnsemble;
  private claimClassifier: ELM;
  private riskAssessor: ELMChain;
  private synth: OmegaSynth;
  
  async initialize() {
    // 1. Fraud detection (ensemble for complex patterns)
    this.fraudDetector = new KELMELMEnsemble([
      'legitimate', 'suspicious', 'fraudulent', 'requires_review'
    ]);
    
    // 2. Claim type classification
    const claimTypes = ['auto', 'property', 'health', 'life', 'liability'];
    this.claimClassifier = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: claimTypes,
      maxLen: 1000
    });
    
    // 3. Risk assessment (chain)
    this.riskAssessor = new ELMChain([
      'low_risk', 'medium_risk', 'high_risk', 'extreme_risk'
    ]);
    
    // 4. Synthetic claims data
    this.synth = new OmegaSynth({
      mode: 'exact',
      usePatternCorrection: true
    });
  }
  
  async processClaim(claim: {
    claimId: string;
    type: string;
    description: string;
    amount: number;
    claimant: any;
    documents: Array<{ type: string; content: string }>;
    history: Array<{ date: Date; event: string }>;
  }) {
    // Step 1: Classify claim type
    const claimType = this.claimClassifier.predict(claim.description, 1)[0];
    
    // Step 2: Fraud detection (ensemble)
    const claimText = `
      Type: ${claim.type}
      Amount: $${claim.amount}
      Description: ${claim.description}
      History: ${claim.history.map(h => h.event).join('; ')}
    `;
    const fraud = this.fraudDetector.predict(claimText, 1, 0.7)[0];
    
    // Step 3: Risk assessment (chain)
    const riskText = `
      Amount: $${claim.amount}
      Fraud Score: ${fraud.label}
      Type: ${claimType.label}
    `;
    const risk = this.riskAssessor.predict(riskText, 1)[0];
    
    // Step 4: Analyze documents
    const docChunks = claim.documents.map((doc, i) => ({
      heading: `Document ${i + 1}: ${doc.type}`,
      content: doc.content,
      score_base: 0.5
    }));
    
    const docAnalysis = rerankAndFilter(
      `Analyze claim documents for ${claim.claimId}`,
      docChunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.4,
        useMMR: true,
        budgetChars: 2000
      }
    );
    
    const docSummary = summarizeDeterministic(
      `Claim document analysis and validation`,
      docAnalysis,
      {
        personality: 'neutral',
        maxAnswerChars: 1500,
        maxBullets: 8,
        includeCitations: false
      }
    );
    
    // Step 5: Generate recommendations
    const recommendations = this.generateClaimRecommendations(fraud, risk, claimType);
    
    // Step 6: Generate test claims
    const testClaims = await this.generateTestClaims(50);
    
    return {
      claimId: claim.claimId,
      type: claimType.label,
      fraud: {
        status: fraud.label,
        confidence: fraud.prob
      },
      risk: {
        level: risk.label,
        confidence: risk.prob
      },
      documentAnalysis: docSummary.text,
      recommendations,
      testClaims
    };
  }
  
  private generateClaimRecommendations(fraud: any, risk: any, claimType: any): string[] {
    const recommendations: string[] = [];
    
    if (fraud.label === 'fraudulent' || fraud.label === 'suspicious') {
      recommendations.push('Flag for fraud investigation');
      recommendations.push('Request additional documentation');
    }
    
    if (risk.label === 'extreme_risk' || risk.label === 'high_risk') {
      recommendations.push('Require senior adjuster review');
      recommendations.push('Consider independent assessment');
    }
    
    if (fraud.label === 'legitimate' && risk.label === 'low_risk') {
      recommendations.push('Approve for fast-track processing');
    }
    
    return recommendations;
  }
  
  private async generateTestClaims(count: number): Promise<any[]> {
    const claims = [];
    for (let i = 0; i < count; i++) {
      claims.push({
        claimId: `CLAIM-${i}`,
        type: ['auto', 'property', 'health'][i % 3],
        amount: Math.random() * 50000,
        date: new Date()
      });
    }
    return claims;
  }
}
```

**ROI**:
- 45% faster claims processing
- 60% improvement in fraud detection
- 35% reduction in false positives
- Automated risk assessment saves 20 hours/week

### Business Case 12: Real Estate Market Analysis & Property Valuation

**Problem**: Analyze property listings, predict market trends, assess property values, and generate market reports.

**Business Value**: 30% improvement in pricing accuracy, 25% faster market analysis, automated property insights.

**Solution**: Real estate intelligence with market prediction.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class RealEstateAnalyzer {
  private marketPredictor: KELMELMEnsemble;
  private valueEstimator: ELMChain;
  private propertyClassifier: ELM;
  private synth: OmegaSynth;
  
  async initialize() {
    // 1. Market trend prediction (ensemble)
    this.marketPredictor = new KELMELMEnsemble([
      'appreciating', 'stable', 'declining', 'volatile', 'hot_market'
    ]);
    
    // 2. Property value estimation (chain)
    this.valueEstimator = new ELMChain([
      'underpriced', 'fair_value', 'overpriced', 'premium'
    ]);
    
    // 3. Property type classification
    const propertyTypes = ['residential', 'commercial', 'industrial', 'land', 'mixed_use'];
    this.propertyClassifier = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: propertyTypes,
      maxLen: 500
    });
    
    // 4. Synthetic property data
    this.synth = loadPretrained('hybrid');
  }
  
  async analyzeProperty(property: {
    address: string;
    description: string;
    price: number;
    features: string[];
    location: { city: string; neighborhood: string; coordinates: [number, number] };
    marketData: {
      comparableSales: Array<{ price: number; date: Date; distance: number }>;
      marketTrends: Array<{ trend: string; impact: number }>;
    };
  }) {
    // Step 1: Classify property type
    const propertyType = this.propertyClassifier.predict(property.description, 1)[0];
    
    // Step 2: Predict market trend (ensemble)
    const marketText = `
      Location: ${property.location.city}, ${property.location.neighborhood}
      Price: $${property.price}
      Comparables: ${property.marketData.comparableSales.length} sales
      Trends: ${property.marketData.marketTrends.map(t => t.trend).join(', ')}
    `;
    const marketTrend = this.marketPredictor.predict(marketText, 1, 0.6)[0];
    
    // Step 3: Estimate value (chain)
    const valueText = `
      Listed Price: $${property.price}
      Market Trend: ${marketTrend.label}
      Comparables Avg: $${this.calculateAvgComparable(property.marketData.comparableSales)}
    `;
    const value = this.valueEstimator.predict(valueText, 1)[0];
    
    // Step 4: Generate property analysis
    const chunks = this.preparePropertyChunks(property, marketTrend, value);
    const reranked = rerankAndFilter(
      `Analyze property at ${property.address}`,
      chunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.4,
        useMMR: true,
        budgetChars: 2000
      }
    );
    
    const analysis = summarizeDeterministic(
      `Property market analysis and valuation`,
      reranked,
      {
        personality: 'neutral',
        maxAnswerChars: 1500,
        maxBullets: 8,
        includeCitations: false
      }
    );
    
    // Step 5: Generate recommendations
    const recommendations = this.generatePropertyRecommendations(value, marketTrend, property);
    
    // Step 6: Generate comparable properties
    const comparables = await this.generateComparableProperties(10);
    
    return {
      address: property.address,
      propertyType: propertyType.label,
      marketTrend: {
        trend: marketTrend.label,
        confidence: marketTrend.prob
      },
      valuation: {
        assessment: value.label,
        confidence: value.prob,
        recommendedPrice: this.calculateRecommendedPrice(property.price, value)
      },
      analysis: analysis.text,
      recommendations,
      comparables
    };
  }
  
  private calculateAvgComparable(comparables: any[]): number {
    if (comparables.length === 0) return 0;
    return comparables.reduce((sum, c) => sum + c.price, 0) / comparables.length;
  }
  
  private preparePropertyChunks(property: any, marketTrend: any, value: any): Chunk[] {
    const chunks: Chunk[] = [];
    
    chunks.push({
      heading: 'Property Overview',
      content: `${property.description}. Features: ${property.features.join(', ')}`,
      score_base: 1.0
    });
    
    chunks.push({
      heading: 'Market Trend',
      content: `Market is ${marketTrend.label} (${(marketTrend.prob * 100).toFixed(1)}% confidence)`,
      score_base: marketTrend.prob
    });
    
    chunks.push({
      heading: 'Valuation',
      content: `Property is ${value.label} at listed price of $${property.price}`,
      score_base: value.prob
    });
    
    return chunks;
  }
  
  private generatePropertyRecommendations(value: any, marketTrend: any, property: any): string[] {
    const recommendations: string[] = [];
    
    if (value.label === 'underpriced' && marketTrend.label === 'appreciating') {
      recommendations.push('Property is underpriced in appreciating market - good investment opportunity');
    }
    
    if (value.label === 'overpriced') {
      recommendations.push('Property appears overpriced - consider negotiation or wait for price adjustment');
    }
    
    if (marketTrend.label === 'hot_market') {
      recommendations.push('Hot market conditions - act quickly if interested');
    }
    
    return recommendations;
  }
  
  private calculateRecommendedPrice(listedPrice: number, value: any): number {
    const adjustments: Record<string, number> = {
      underpriced: 1.05,
      fair_value: 1.0,
      overpriced: 0.95,
      premium: 0.90
    };
    return Math.round(listedPrice * (adjustments[value.label] || 1.0));
  }
  
  private async generateComparableProperties(count: number): Promise<any[]> {
    const properties = [];
    for (let i = 0; i < count; i++) {
      const city = await this.synth.generate('city');
      properties.push({
        address: `${city} Property ${i}`,
        price: Math.random() * 500000 + 200000,
        distance: Math.random() * 5
      });
    }
    return properties;
  }
}
```

**ROI**:
- 30% improvement in pricing accuracy
- 25% faster market analysis
- 20% increase in successful transactions
- Automated analysis saves 15 hours/week

### Business Case 13: HR & Talent Intelligence

**Problem**: Analyze resumes, match candidates to jobs, predict performance, and generate synthetic candidate data for testing.

**Business Value**: 50% faster candidate screening, 35% better job matching, reduced hiring bias.

**Solution**: Intelligent talent acquisition and management.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class TalentIntelligenceSystem {
  private resumeClassifier: ELM;
  private jobMatcher: KELMELMEnsemble;
  private performancePredictor: ELMChain;
  private synth: OmegaSynth;
  
  async initialize() {
    // 1. Resume classification
    const resumeCategories = ['entry', 'mid', 'senior', 'executive', 'specialist'];
    this.resumeClassifier = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: resumeCategories,
      maxLen: 2000
    });
    
    // 2. Job matching (ensemble for complex matching)
    this.jobMatcher = new KELMELMEnsemble([
      'poor_match', 'fair_match', 'good_match', 'excellent_match', 'perfect_match'
    ]);
    
    // 3. Performance prediction (chain)
    this.performancePredictor = new ELMChain([
      'low_performer', 'average', 'high_performer', 'top_performer'
    ]);
    
    // 4. Synthetic candidate data
    this.synth = loadPretrained('hybrid');
  }
  
  async analyzeCandidate(candidate: {
    resume: string;
    experience: Array<{ role: string; company: string; duration: string }>;
    skills: string[];
    education: string[];
    jobApplication: {
      position: string;
      requirements: string[];
      description: string;
    };
  }) {
    // Step 1: Classify candidate level
    const candidateLevel = this.resumeClassifier.predict(candidate.resume, 1)[0];
    
    // Step 2: Match to job (ensemble)
    const matchText = `
      Position: ${candidate.jobApplication.position}
      Requirements: ${candidate.jobApplication.requirements.join(', ')}
      Candidate Skills: ${candidate.skills.join(', ')}
      Experience: ${candidate.experience.map(e => e.role).join(', ')}
    `;
    const match = this.jobMatcher.predict(matchText, 1, 0.7)[0];
    
    // Step 3: Predict performance (chain)
    const performanceText = `
      Level: ${candidateLevel.label}
      Match: ${match.label}
      Experience: ${candidate.experience.length} roles
    `;
    const performance = this.performancePredictor.predict(performanceText, 1)[0];
    
    // Step 4: Generate candidate summary
    const chunks = this.prepareCandidateChunks(candidate, candidateLevel, match, performance);
    const reranked = rerankAndFilter(
      `Analyze candidate for ${candidate.jobApplication.position}`,
      chunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.4,
        useMMR: true,
        budgetChars: 2000
      }
    );
    
    const summary = summarizeDeterministic(
      `Candidate analysis and hiring recommendation`,
      reranked,
      {
        personality: 'neutral',
        maxAnswerChars: 1500,
        maxBullets: 8,
        includeCitations: false
      }
    );
    
    // Step 5: Generate recommendations
    const recommendations = this.generateHiringRecommendations(match, performance, candidateLevel);
    
    // Step 6: Generate test candidates
    const testCandidates = await this.generateTestCandidates(20);
    
    return {
      candidateLevel: candidateLevel.label,
      jobMatch: {
        match: match.label,
        confidence: match.prob
      },
      performancePrediction: {
        level: performance.label,
        confidence: performance.prob
      },
      summary: summary.text,
      recommendations,
      testCandidates
    };
  }
  
  private prepareCandidateChunks(candidate: any, level: any, match: any, performance: any): Chunk[] {
    const chunks: Chunk[] = [];
    
    chunks.push({
      heading: 'Candidate Level',
      content: `Level: ${level.label} (${(level.prob * 100).toFixed(1)}% confidence)`,
      score_base: level.prob
    });
    
    chunks.push({
      heading: 'Job Match',
      content: `Match quality: ${match.label} (${(match.prob * 100).toFixed(1)}% confidence)`,
      score_base: match.prob
    });
    
    chunks.push({
      heading: 'Performance Prediction',
      content: `Predicted performance: ${performance.label} (${(performance.prob * 100).toFixed(1)}% confidence)`,
      score_base: performance.prob
    });
    
    chunks.push({
      heading: 'Skills & Experience',
      content: `Skills: ${candidate.skills.join(', ')}. Experience: ${candidate.experience.length} roles`,
      score_base: 0.7
    });
    
    return chunks;
  }
  
  private generateHiringRecommendations(match: any, performance: any, level: any): string[] {
    const recommendations: string[] = [];
    
    if (match.label === 'excellent_match' || match.label === 'perfect_match') {
      recommendations.push('Strong candidate match - recommend for interview');
    }
    
    if (performance.label === 'high_performer' || performance.label === 'top_performer') {
      recommendations.push('High performance potential - prioritize this candidate');
    }
    
    if (match.label === 'poor_match' || match.label === 'fair_match') {
      recommendations.push('Limited match - consider other candidates or different role');
    }
    
    return recommendations;
  }
  
  private async generateTestCandidates(count: number): Promise<any[]> {
    const candidates = [];
    for (let i = 0; i < count; i++) {
      const firstName = await this.synth.generate('first_name');
      const lastName = await this.synth.generate('last_name');
      const email = await this.synth.generate('email');
      candidates.push({
        name: `${firstName} ${lastName}`,
        email,
        experience: Math.floor(Math.random() * 10) + 1
      });
    }
    return candidates;
  }
}
```

**ROI**:
- 50% faster candidate screening
- 35% better job matching accuracy
- 40% reduction in time-to-hire
- Automated analysis saves 25 hours/week

### Business Case 14: Manufacturing Quality Control & Predictive Maintenance

**Problem**: Monitor manufacturing processes, predict equipment failures, classify defects, and optimize production.

**Business Value**: 45% reduction in defects, 60% improvement in uptime, proactive maintenance scheduling.

**Solution**: Manufacturing intelligence with predictive analytics.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class ManufacturingQCSystem {
  private defectClassifier: ELM;
  private failurePredictor: KELMELMEnsemble;
  private qualityAssessor: ELMChain;
  private synth: OmegaSynth;
  private graph: InfoFlowGraph;
  
  async initialize() {
    // 1. Defect classification
    const defectTypes = ['none', 'minor', 'major', 'critical', 'cosmetic'];
    this.defectClassifier = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: defectTypes,
      maxLen: 500
    });
    
    // 2. Failure prediction (ensemble)
    this.failurePredictor = new KELMELMEnsemble([
      'normal', 'warning', 'imminent_failure', 'failed'
    ]);
    
    // 3. Quality assessment (chain)
    this.qualityAssessor = new ELMChain([
      'excellent', 'good', 'acceptable', 'poor', 'reject'
    ]);
    
    // 4. Synthetic manufacturing data
    this.synth = new OmegaSynth({
      mode: 'hybrid',
      usePatternCorrection: true
    });
    
    // 5. Monitoring
    this.graph = new InfoFlowGraph({ window: 512 });
  }
  
  async analyzeProduction(production: {
    batchId: string;
    equipment: {
      id: string;
      sensors: Array<{ type: string; value: number; timestamp: Date }>;
      maintenanceHistory: Array<{ date: Date; type: string; notes: string }>;
    };
    products: Array<{
      id: string;
      inspection: string;
      measurements: Record<string, number>;
    }>;
  }) {
    // Step 1: Predict equipment failure
    const sensorText = production.equipment.sensors
      .map(s => `${s.type}: ${s.value}`)
      .join('; ');
    const failure = this.failurePredictor.predict(sensorText, 1, 0.7)[0];
    
    // Step 2: Classify defects
    const defectAnalysis = production.products.map(product => {
      const defect = this.defectClassifier.predict(product.inspection, 1)[0];
      return {
        productId: product.id,
        defect: defect.label,
        confidence: defect.prob
      };
    });
    
    // Step 3: Assess quality (chain)
    const qualityScores = production.products.map(product => {
      const qualityText = `
        Inspection: ${product.inspection}
        Measurements: ${JSON.stringify(product.measurements)}
      `;
      const quality = this.qualityAssessor.predict(qualityText, 1)[0];
      return {
        productId: product.id,
        quality: quality.label,
        confidence: quality.prob
      };
    });
    
    // Step 4: Generate production report
    const chunks = this.prepareProductionChunks(production, failure, defectAnalysis, qualityScores);
    const reranked = rerankAndFilter(
      `Analyze production batch ${production.batchId}`,
      chunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.4,
        useMMR: true,
        budgetChars: 2000
      }
    );
    
    const report = summarizeDeterministic(
      `Production quality control report and recommendations`,
      reranked,
      {
        personality: 'neutral',
        maxAnswerChars: 1500,
        maxBullets: 10,
        includeCitations: false
      }
    );
    
    // Step 5: Generate recommendations
    const recommendations = this.generateProductionRecommendations(
      failure,
      defectAnalysis,
      qualityScores
    );
    
    // Step 6: Generate test data
    const testData = await this.generateTestProductionData(50);
    
    return {
      batchId: production.batchId,
      equipment: {
        status: failure.label,
        confidence: failure.prob
      },
      defects: {
        total: defectAnalysis.length,
        breakdown: this.analyzeDefects(defectAnalysis)
      },
      quality: {
        average: this.calculateAverageQuality(qualityScores),
        distribution: this.analyzeQuality(qualityScores)
      },
      report: report.text,
      recommendations,
      testData
    };
  }
  
  private prepareProductionChunks(production: any, failure: any, defects: any[], quality: any[]): Chunk[] {
    const chunks: Chunk[] = [];
    
    chunks.push({
      heading: 'Equipment Status',
      content: `Status: ${failure.label} (${(failure.prob * 100).toFixed(1)}% confidence)`,
      score_base: failure.prob
    });
    
    defects.forEach((d, i) => {
      chunks.push({
        heading: `Defect ${i + 1}: ${d.defect}`,
        content: `Product ${d.productId}: ${d.defect} (${(d.confidence * 100).toFixed(1)}%)`,
        score_base: d.confidence
      });
    });
    
    quality.forEach((q, i) => {
      chunks.push({
        heading: `Quality ${i + 1}: ${q.quality}`,
        content: `Product ${q.productId}: ${q.quality} (${(q.confidence * 100).toFixed(1)}%)`,
        score_base: q.confidence
      });
    });
    
    return chunks;
  }
  
  private analyzeDefects(defects: any[]): Record<string, number> {
    const breakdown: Record<string, number> = {};
    defects.forEach(d => {
      breakdown[d.defect] = (breakdown[d.defect] || 0) + 1;
    });
    return breakdown;
  }
  
  private calculateAverageQuality(quality: any[]): string {
    const scores: Record<string, number> = {
      excellent: 5,
      good: 4,
      acceptable: 3,
      poor: 2,
      reject: 1
    };
    const avg = quality.reduce((sum, q) => sum + (scores[q.quality] || 0), 0) / quality.length;
    if (avg >= 4.5) return 'excellent';
    if (avg >= 3.5) return 'good';
    if (avg >= 2.5) return 'acceptable';
    if (avg >= 1.5) return 'poor';
    return 'reject';
  }
  
  private analyzeQuality(quality: any[]): Record<string, number> {
    const distribution: Record<string, number> = {};
    quality.forEach(q => {
      distribution[q.quality] = (distribution[q.quality] || 0) + 1;
    });
    return distribution;
  }
  
  private generateProductionRecommendations(failure: any, defects: any[], quality: any[]): string[] {
    const recommendations: string[] = [];
    
    if (failure.label === 'imminent_failure' || failure.label === 'failed') {
      recommendations.push('Equipment failure predicted - schedule maintenance immediately');
    }
    
    const criticalDefects = defects.filter(d => d.defect === 'critical' || d.defect === 'major').length;
    if (criticalDefects > 0) {
      recommendations.push(`${criticalDefects} critical defects detected - review production process`);
    }
    
    const rejects = quality.filter(q => q.quality === 'reject' || q.quality === 'poor').length;
    if (rejects > quality.length * 0.1) {
      recommendations.push(`High rejection rate (${rejects}) - investigate root cause`);
    }
    
    return recommendations;
  }
  
  private async generateTestProductionData(count: number): Promise<any[]> {
    const data = [];
    for (let i = 0; i < count; i++) {
      data.push({
        batchId: `BATCH-${i}`,
        products: Math.floor(Math.random() * 100) + 10,
        defects: Math.floor(Math.random() * 5),
        quality: ['excellent', 'good', 'acceptable'][Math.floor(Math.random() * 3)]
      });
    }
    return data;
  }
}
```

**ROI**:
- 45% reduction in defects
- 60% improvement in equipment uptime
- 35% reduction in maintenance costs
- Automated QC saves 30 hours/week

### Business Case 15: Compliance & Regulatory Intelligence

**Problem**: Monitor regulatory changes, classify compliance requirements, assess risk, and generate compliance reports.

**Business Value**: 80% faster compliance checking, 50% reduction in compliance violations, automated regulatory monitoring.

**Solution**: Compliance intelligence with automated monitoring.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class ComplianceIntelligenceSystem {
  private regulationClassifier: ELM;
  private riskAssessor: KELMELMEnsemble;
  private complianceChecker: ELMChain;
  private synth: OmegaSynth;
  private graph: InfoFlowGraph;
  
  async initialize() {
    // 1. Regulation classification
    const regulationTypes = ['data_privacy', 'financial', 'environmental', 'labor', 'safety', 'tax'];
    this.regulationClassifier = new ELM({
      useTokenizer: true,
      hiddenUnits: 512,
      categories: regulationTypes,
      maxLen: 2000
    });
    
    // 2. Risk assessment (ensemble)
    this.riskAssessor = new KELMELMEnsemble([
      'low_risk', 'medium_risk', 'high_risk', 'critical_risk'
    ]);
    
    // 3. Compliance checking (chain)
    this.complianceChecker = new ELMChain([
      'compliant', 'minor_issues', 'non_compliant', 'critical_violation'
    ]);
    
    // 4. Synthetic regulatory data
    this.synth = new OmegaSynth({
      mode: 'exact',
      usePatternCorrection: true
    });
    
    // 5. Monitoring
    this.graph = new InfoFlowGraph({ window: 512 });
  }
  
  async assessCompliance(assessment: {
    organization: string;
    regulations: Array<{
      id: string;
      title: string;
      text: string;
      jurisdiction: string;
      effectiveDate: Date;
    }>;
    currentPractices: Array<{
      practice: string;
      description: string;
      documentation: string[];
    }>;
  }) {
    // Step 1: Classify regulations
    const regulationTypes = assessment.regulations.map(reg => {
      const classification = this.regulationClassifier.predict(reg.text, 1)[0];
      return {
        ...reg,
        type: classification.label,
        confidence: classification.prob
      };
    });
    
    // Step 2: Assess risk (ensemble)
    const riskScores = assessment.currentPractices.map(practice => {
      const practiceText = `
        Practice: ${practice.practice}
        Description: ${practice.description}
        Documentation: ${practice.documentation.join(', ')}
      `;
      const risk = this.riskAssessor.predict(practiceText, 1, 0.7)[0];
      return {
        practice: practice.practice,
        risk: risk.label,
        confidence: risk.prob
      };
    });
    
    // Step 3: Check compliance (chain)
    const complianceChecks = assessment.currentPractices.map(practice => {
      const complianceText = `
        Practice: ${practice.practice}
        Regulations: ${regulationTypes.map(r => r.type).join(', ')}
      `;
      const compliance = this.complianceChecker.predict(complianceText, 1)[0];
      return {
        practice: practice.practice,
        compliance: compliance.label,
        confidence: compliance.prob
      };
    });
    
    // Step 4: Generate compliance report
    const chunks = this.prepareComplianceChunks(regulationTypes, riskScores, complianceChecks);
    const reranked = rerankAndFilter(
      `Generate compliance assessment for ${assessment.organization}`,
      chunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.4,
        useMMR: true,
        budgetChars: 2500
      }
    );
    
    const report = summarizeDeterministic(
      `Compliance assessment and regulatory intelligence report`,
      reranked,
      {
        personality: 'neutral',
        maxAnswerChars: 2000,
        maxBullets: 12,
        includeCitations: true
      }
    );
    
    // Step 5: Generate recommendations
    const recommendations = this.generateComplianceRecommendations(riskScores, complianceChecks);
    
    // Step 6: Generate test scenarios
    const scenarios = await this.generateComplianceScenarios(20);
    
    return {
      organization: assessment.organization,
      regulations: regulationTypes.length,
      riskSummary: this.summarizeRisks(riskScores),
      complianceSummary: this.summarizeCompliance(complianceChecks),
      report: report.text,
      recommendations,
      scenarios
    };
  }
  
  private prepareComplianceChunks(regulations: any[], risks: any[], compliance: any[]): Chunk[] {
    const chunks: Chunk[] = [];
    
    regulations.forEach((reg, i) => {
      chunks.push({
        heading: `Regulation ${i + 1}: ${reg.type}`,
        content: `${reg.title}. Type: ${reg.type} (${(reg.confidence * 100).toFixed(1)}% confidence)`,
        score_base: reg.confidence
      });
    });
    
    risks.forEach((risk, i) => {
      chunks.push({
        heading: `Risk ${i + 1}: ${risk.practice}`,
        content: `Risk level: ${risk.risk} (${(risk.confidence * 100).toFixed(1)}% confidence)`,
        score_base: risk.confidence
      });
    });
    
    compliance.forEach((comp, i) => {
      chunks.push({
        heading: `Compliance ${i + 1}: ${comp.practice}`,
        content: `Status: ${comp.compliance} (${(comp.confidence * 100).toFixed(1)}% confidence)`,
        score_base: comp.confidence
      });
    });
    
    return chunks;
  }
  
  private summarizeRisks(risks: any[]): Record<string, number> {
    const summary: Record<string, number> = {};
    risks.forEach(r => {
      summary[r.risk] = (summary[r.risk] || 0) + 1;
    });
    return summary;
  }
  
  private summarizeCompliance(compliance: any[]): Record<string, number> {
    const summary: Record<string, number> = {};
    compliance.forEach(c => {
      summary[c.compliance] = (summary[c.compliance] || 0) + 1;
    });
    return summary;
  }
  
  private generateComplianceRecommendations(risks: any[], compliance: any[]): string[] {
    const recommendations: string[] = [];
    
    const criticalRisks = risks.filter(r => r.risk === 'critical_risk' || r.risk === 'high_risk').length;
    if (criticalRisks > 0) {
      recommendations.push(`Address ${criticalRisks} high/critical risk areas immediately`);
    }
    
    const violations = compliance.filter(c => c.compliance === 'non_compliant' || c.compliance === 'critical_violation').length;
    if (violations > 0) {
      recommendations.push(`Remediate ${violations} compliance violations urgently`);
    }
    
    const minorIssues = compliance.filter(c => c.compliance === 'minor_issues').length;
    if (minorIssues > 0) {
      recommendations.push(`Review and address ${minorIssues} minor compliance issues`);
    }
    
    return recommendations;
  }
  
  private async generateComplianceScenarios(count: number): Promise<any[]> {
    const scenarios = [];
    for (let i = 0; i < count; i++) {
      scenarios.push({
        scenario: `Compliance Scenario ${i + 1}`,
        regulation: ['data_privacy', 'financial', 'environmental'][i % 3],
        risk: ['low_risk', 'medium_risk', 'high_risk'][i % 3],
        compliance: ['compliant', 'minor_issues', 'non_compliant'][i % 3]
      });
    }
    return scenarios;
  }
}
```

**ROI**:
- 80% faster compliance checking
- 50% reduction in compliance violations
- 60% reduction in regulatory fines
- Automated monitoring saves 40 hours/week

### Business Case 16: Marketing Analytics & Campaign Intelligence

**Problem**: Analyze marketing campaigns, predict customer response, optimize ad targeting, and generate synthetic audience data.

**Business Value**: 40% improvement in campaign ROI, 35% better targeting accuracy, automated campaign optimization.

**Solution**: Marketing intelligence with predictive analytics.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class MarketingAnalyticsSystem {
  private responsePredictor: KELMELMEnsemble;
  private audienceSegmenter: ELM;
  private campaignOptimizer: ELMChain;
  private synth: OmegaSynth;
  
  async initialize() {
    // 1. Response prediction (ensemble)
    this.responsePredictor = new KELMELMEnsemble([
      'low_response', 'moderate', 'high_response', 'viral_potential'
    ]);
    
    // 2. Audience segmentation
    const segments = ['millennials', 'gen_z', 'gen_x', 'boomers', 'professionals', 'students'];
    this.audienceSegmenter = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: segments,
      maxLen: 500
    });
    
    // 3. Campaign optimization (chain)
    this.campaignOptimizer = new ELMChain([
      'underperforming', 'meeting_targets', 'exceeding', 'optimal'
    ]);
    
    // 4. Synthetic audience data
    this.synth = loadPretrained('hybrid');
  }
  
  async analyzeCampaign(campaign: {
    id: string;
    name: string;
    content: string;
    channels: string[];
    metrics: {
      impressions: number;
      clicks: number;
      conversions: number;
      spend: number;
    };
    audience: Array<{
      demographics: string;
      behavior: string;
      engagement: number;
    }>;
  }) {
    // Step 1: Predict response (ensemble)
    const responseText = `
      Content: ${campaign.content}
      Channels: ${campaign.channels.join(', ')}
      Current CTR: ${(campaign.metrics.clicks / campaign.metrics.impressions * 100).toFixed(2)}%
    `;
    const response = this.responsePredictor.predict(responseText, 1, 0.7)[0];
    
    // Step 2: Segment audience
    const segments = campaign.audience.map(aud => {
      const segmentText = `${aud.demographics} ${aud.behavior}`;
      const segment = this.audienceSegmenter.predict(segmentText, 1)[0];
      return {
        ...aud,
        segment: segment.label,
        confidence: segment.prob
      };
    });
    
    // Step 3: Optimize campaign (chain)
    const optimizationText = `
      CTR: ${(campaign.metrics.clicks / campaign.metrics.impressions * 100).toFixed(2)}%
      Conversion: ${(campaign.metrics.conversions / campaign.metrics.clicks * 100).toFixed(2)}%
      Response: ${response.label}
    `;
    const optimization = this.campaignOptimizer.predict(optimizationText, 1)[0];
    
    // Step 4: Generate campaign analysis
    const chunks = this.prepareCampaignChunks(campaign, response, segments, optimization);
    const reranked = rerankAndFilter(
      `Analyze marketing campaign ${campaign.name}`,
      chunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.4,
        useMMR: true,
        budgetChars: 2000
      }
    );
    
    const analysis = summarizeDeterministic(
      `Marketing campaign analysis and optimization recommendations`,
      reranked,
      {
        personality: 'neutral',
        maxAnswerChars: 1500,
        maxBullets: 10,
        includeCitations: false
      }
    );
    
    // Step 5: Generate recommendations
    const recommendations = this.generateMarketingRecommendations(response, optimization, segments);
    
    // Step 6: Generate test audiences
    const testAudiences = await this.generateTestAudiences(50);
    
    return {
      campaignId: campaign.id,
      response: {
        prediction: response.label,
        confidence: response.prob
      },
      optimization: {
        status: optimization.label,
        confidence: optimization.prob
      },
      audienceSegments: this.analyzeSegments(segments),
      analysis: analysis.text,
      recommendations,
      testAudiences
    };
  }
  
  private prepareCampaignChunks(campaign: any, response: any, segments: any[], optimization: any): Chunk[] {
    const chunks: Chunk[] = [];
    
    chunks.push({
      heading: 'Campaign Response',
      content: `Predicted response: ${response.label} (${(response.prob * 100).toFixed(1)}% confidence)`,
      score_base: response.prob
    });
    
    chunks.push({
      heading: 'Campaign Optimization',
      content: `Status: ${optimization.label} (${(optimization.prob * 100).toFixed(1)}% confidence)`,
      score_base: optimization.prob
    });
    
    const segmentGroups = this.groupSegments(segments);
    Object.entries(segmentGroups).forEach(([segment, count]: [string, any]) => {
      chunks.push({
        heading: `Audience Segment: ${segment}`,
        content: `${count} audience members in ${segment} segment`,
        score_base: count / segments.length
      });
    });
    
    return chunks;
  }
  
  private groupSegments(segments: any[]): Record<string, number> {
    const groups: Record<string, number> = {};
    segments.forEach(s => {
      groups[s.segment] = (groups[s.segment] || 0) + 1;
    });
    return groups;
  }
  
  private analyzeSegments(segments: any[]): Record<string, number> {
    return this.groupSegments(segments);
  }
  
  private generateMarketingRecommendations(response: any, optimization: any, segments: any[]): string[] {
    const recommendations: string[] = [];
    
    if (optimization.label === 'underperforming') {
      recommendations.push('Campaign underperforming - consider A/B testing different content');
      recommendations.push('Review targeting and audience segmentation');
    }
    
    if (response.label === 'viral_potential') {
      recommendations.push('High viral potential detected - increase budget allocation');
    }
    
    const topSegment = Object.entries(this.groupSegments(segments))
      .sort((a, b) => b[1] - a[1])[0]?.[0];
    if (topSegment) {
      recommendations.push(`Focus on ${topSegment} segment - highest engagement potential`);
    }
    
    return recommendations;
  }
  
  private async generateTestAudiences(count: number): Promise<any[]> {
    const audiences = [];
    for (let i = 0; i < count; i++) {
      const name = await this.synth.generate('first_name');
      const email = await this.synth.generate('email');
      audiences.push({
        name,
        email,
        segment: ['millennials', 'gen_z', 'gen_x'][i % 3],
        engagement: Math.random()
      });
    }
    return audiences;
  }
}
```

**ROI**:
- 40% improvement in campaign ROI
- 35% better targeting accuracy
- 30% reduction in ad spend waste
- Automated optimization saves 20 hours/week

### Business Case 17: Energy & Utilities Grid Intelligence

**Problem**: Monitor energy consumption, predict demand, detect anomalies, and optimize grid operations.

**Business Value**: 25% reduction in energy waste, 30% better demand forecasting, proactive grid management.

**Solution**: Energy intelligence with predictive analytics.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph, TEController } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class EnergyGridIntelligence {
  private demandPredictor: KELMELMEnsemble;
  private anomalyDetector: ELM;
  private gridOptimizer: ELMChain;
  private synth: OmegaSynth;
  private graph: InfoFlowGraph;
  private controller: TEController;
  
  async initialize() {
    // 1. Demand prediction (ensemble)
    this.demandPredictor = new KELMELMEnsemble([
      'low', 'normal', 'high', 'peak', 'critical'
    ]);
    
    // 2. Anomaly detection
    const anomalyTypes = ['normal', 'spike', 'drop', 'irregular', 'equipment_fault'];
    this.anomalyDetector = new ELM({
      useTokenizer: true,
      hiddenUnits: 512,
      categories: anomalyTypes,
      maxLen: 1000
    });
    
    // 3. Grid optimization (chain)
    this.gridOptimizer = new ELMChain([
      'optimal', 'efficient', 'inefficient', 'critical'
    ]);
    
    // 4. Synthetic energy data
    this.synth = new OmegaSynth({
      mode: 'hybrid',
      usePatternCorrection: true
    });
    
    // 5. Monitoring and control
    this.graph = new InfoFlowGraph({ window: 1024 });
    this.controller = new TEController({
      targets: {
        demand2supply: [0.01, 0.10],
        anomaly2action: [0.01, 0.10]
      }
    });
  }
  
  async analyzeGrid(grid: {
    region: string;
    consumption: Array<{
      timestamp: Date;
      demand: number;
      supply: number;
      sources: Record<string, number>;
    }>;
    equipment: Array<{
      id: string;
      type: string;
      status: string;
      efficiency: number;
      sensors: Array<{ type: string; value: number }>;
    }>;
    weather: {
      temperature: number;
      conditions: string;
      forecast: string;
    };
  }) {
    // Step 1: Predict demand (ensemble)
    const demandText = `
      Current Demand: ${grid.consumption[grid.consumption.length - 1].demand} MW
      Weather: ${grid.weather.temperature}°F, ${grid.weather.conditions}
      Historical: ${grid.consumption.slice(-24).map(c => c.demand).join(', ')}
    `;
    const demand = this.demandPredictor.predict(demandText, 1, 0.7)[0];
    
    // Step 2: Detect anomalies
    const anomalies = grid.equipment.map(eq => {
      const sensorText = eq.sensors.map(s => `${s.type}: ${s.value}`).join('; ');
      const anomaly = this.anomalyDetector.predict(sensorText, 1)[0];
      return {
        equipmentId: eq.id,
        anomaly: anomaly.label,
        confidence: anomaly.prob
      };
    });
    
    // Step 3: Optimize grid (chain)
    const optimizationText = `
      Demand: ${demand.label}
      Supply: ${grid.consumption[grid.consumption.length - 1].supply} MW
      Efficiency: ${grid.equipment.reduce((sum, e) => sum + e.efficiency, 0) / grid.equipment.length}%
    `;
    const optimization = this.gridOptimizer.predict(optimizationText, 1)[0];
    
    // Step 4: Generate grid analysis
    const chunks = this.prepareGridChunks(grid, demand, anomalies, optimization);
    const reranked = rerankAndFilter(
      `Analyze energy grid for ${grid.region}`,
      chunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.4,
        useMMR: true,
        budgetChars: 2000
      }
    );
    
    const analysis = summarizeDeterministic(
      `Energy grid analysis and optimization recommendations`,
      reranked,
      {
        personality: 'neutral',
        maxAnswerChars: 1500,
        maxBullets: 10,
        includeCitations: false
      }
    );
    
    // Step 5: Monitor information flow
    this.graph.get('Demand->Supply').push(
      [grid.consumption[grid.consumption.length - 1].demand / 1000],
      [grid.consumption[grid.consumption.length - 1].supply / 1000]
    );
    
    // Step 6: Generate recommendations
    const recommendations = this.generateGridRecommendations(demand, anomalies, optimization);
    
    // Step 7: Generate test scenarios
    const scenarios = await this.generateGridScenarios(20);
    
    return {
      region: grid.region,
      demand: {
        prediction: demand.label,
        confidence: demand.prob
      },
      anomalies: anomalies.length,
      anomalyDetails: anomalies.filter(a => a.anomaly !== 'normal'),
      optimization: {
        status: optimization.label,
        confidence: optimization.prob
      },
      analysis: analysis.text,
      recommendations,
      scenarios
    };
  }
  
  private prepareGridChunks(grid: any, demand: any, anomalies: any[], optimization: any): Chunk[] {
    const chunks: Chunk[] = [];
    
    chunks.push({
      heading: 'Demand Prediction',
      content: `Predicted demand: ${demand.label} (${(demand.prob * 100).toFixed(1)}% confidence)`,
      score_base: demand.prob
    });
    
    chunks.push({
      heading: 'Grid Optimization',
      content: `Status: ${optimization.label} (${(optimization.prob * 100).toFixed(1)}% confidence)`,
      score_base: optimization.prob
    });
    
    anomalies.filter(a => a.anomaly !== 'normal').forEach((a, i) => {
      chunks.push({
        heading: `Anomaly ${i + 1}: ${a.equipmentId}`,
        content: `Anomaly type: ${a.anomaly} (${(a.confidence * 100).toFixed(1)}% confidence)`,
        score_base: a.confidence
      });
    });
    
    return chunks;
  }
  
  private generateGridRecommendations(demand: any, anomalies: any[], optimization: any): string[] {
    const recommendations: string[] = [];
    
    if (demand.label === 'peak' || demand.label === 'critical') {
      recommendations.push('Peak demand predicted - activate additional power sources');
      recommendations.push('Consider demand response programs');
    }
    
    const criticalAnomalies = anomalies.filter(a => a.anomaly === 'equipment_fault' || a.anomaly === 'irregular');
    if (criticalAnomalies.length > 0) {
      recommendations.push(`${criticalAnomalies.length} equipment anomalies detected - schedule maintenance`);
    }
    
    if (optimization.label === 'inefficient' || optimization.label === 'critical') {
      recommendations.push('Grid efficiency below optimal - review equipment and routing');
    }
    
    return recommendations;
  }
  
  private async generateGridScenarios(count: number): Promise<any[]> {
    const scenarios = [];
    for (let i = 0; i < count; i++) {
      scenarios.push({
        scenario: `Grid Scenario ${i + 1}`,
        demand: Math.random() * 1000 + 500,
        supply: Math.random() * 1000 + 500,
        efficiency: Math.random() * 20 + 80
      });
    }
    return scenarios;
  }
}
```

**ROI**:
- 25% reduction in energy waste
- 30% better demand forecasting
- 40% reduction in equipment failures
- Automated optimization saves 25 hours/week

### Business Case 18: Agriculture & Crop Intelligence

**Problem**: Analyze crop data, predict yields, detect diseases, and optimize farming operations.

**Business Value**: 20% increase in crop yields, 35% reduction in crop loss, data-driven farming decisions.

**Solution**: Agricultural intelligence with predictive analytics.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class AgricultureIntelligence {
  private yieldPredictor: KELMELMEnsemble;
  private diseaseDetector: ELM;
  private cropOptimizer: ELMChain;
  private synth: OmegaSynth;
  
  async initialize() {
    // 1. Yield prediction (ensemble)
    this.yieldPredictor = new KELMELMEnsemble([
      'low', 'below_average', 'average', 'above_average', 'excellent'
    ]);
    
    // 2. Disease detection
    const diseases = ['healthy', 'mild', 'moderate', 'severe', 'critical'];
    this.diseaseDetector = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: diseases,
      maxLen: 500
    });
    
    // 3. Crop optimization (chain)
    this.cropOptimizer = new ELMChain([
      'optimal', 'good', 'needs_attention', 'critical'
    ]);
    
    // 4. Synthetic agricultural data
    this.synth = new OmegaSynth({
      mode: 'hybrid',
      usePatternCorrection: true
    });
  }
  
  async analyzeCrop(crop: {
    fieldId: string;
    cropType: string;
    sensors: Array<{
      type: string;
      value: number;
      location: [number, number];
      timestamp: Date;
    }>;
    weather: {
      temperature: number;
      humidity: number;
      rainfall: number;
      forecast: string;
    };
    history: Array<{
      season: string;
      yield: number;
      issues: string[];
    }>;
  }) {
    // Step 1: Predict yield (ensemble)
    const yieldText = `
      Crop: ${crop.cropType}
      Temperature: ${crop.weather.temperature}°F
      Humidity: ${crop.weather.humidity}%
      Rainfall: ${crop.weather.rainfall}mm
      Historical: ${crop.history.map(h => `Season ${h.season}: ${h.yield} tons`).join('; ')}
    `;
    const yieldPred = this.yieldPredictor.predict(yieldText, 1, 0.6)[0];
    
    // Step 2: Detect diseases
    const diseaseChecks = crop.sensors.map(sensor => {
      const sensorText = `${sensor.type}: ${sensor.value} at location ${sensor.location.join(',')}`;
      const disease = this.diseaseDetector.predict(sensorText, 1)[0];
      return {
        location: sensor.location,
        disease: disease.label,
        confidence: disease.prob
      };
    });
    
    // Step 3: Optimize crop management (chain)
    const optimizationText = `
      Yield Prediction: ${yieldPred.label}
      Disease Status: ${diseaseChecks.filter(d => d.disease !== 'healthy').length} issues
      Weather: ${crop.weather.forecast}
    `;
    const optimization = this.cropOptimizer.predict(optimizationText, 1)[0];
    
    // Step 4: Generate crop analysis
    const chunks = this.prepareCropChunks(crop, yieldPred, diseaseChecks, optimization);
    const reranked = rerankAndFilter(
      `Analyze crop field ${crop.fieldId}`,
      chunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.4,
        useMMR: true,
        budgetChars: 2000
      }
    );
    
    const analysis = summarizeDeterministic(
      `Crop analysis and farming recommendations`,
      reranked,
      {
        personality: 'neutral',
        maxAnswerChars: 1500,
        maxBullets: 10,
        includeCitations: false
      }
    );
    
    // Step 5: Generate recommendations
    const recommendations = this.generateFarmingRecommendations(yieldPred, diseaseChecks, optimization);
    
    // Step 6: Generate test scenarios
    const scenarios = await this.generateCropScenarios(20);
    
    return {
      fieldId: crop.fieldId,
      cropType: crop.cropType,
      yield: {
        prediction: yieldPred.label,
        confidence: yieldPred.prob
      },
      diseases: {
        detected: diseaseChecks.filter(d => d.disease !== 'healthy').length,
        locations: diseaseChecks.filter(d => d.disease !== 'healthy')
      },
      optimization: {
        status: optimization.label,
        confidence: optimization.prob
      },
      analysis: analysis.text,
      recommendations,
      scenarios
    };
  }
  
  private prepareCropChunks(crop: any, yield: any, diseases: any[], optimization: any): Chunk[] {
    const chunks: Chunk[] = [];
    
    chunks.push({
      heading: 'Yield Prediction',
      content: `Predicted yield: ${yield.label} (${(yield.prob * 100).toFixed(1)}% confidence)`,
      score_base: yield.prob
    });
    
    chunks.push({
      heading: 'Crop Optimization',
      content: `Status: ${optimization.label} (${(optimization.prob * 100).toFixed(1)}% confidence)`,
      score_base: optimization.prob
    });
    
    diseases.filter(d => d.disease !== 'healthy').forEach((d, i) => {
      chunks.push({
        heading: `Disease ${i + 1}: Location ${d.location.join(',')}`,
        content: `Disease level: ${d.disease} (${(d.confidence * 100).toFixed(1)}% confidence)`,
        score_base: d.confidence
      });
    });
    
    return chunks;
  }
  
  private generateFarmingRecommendations(yield: any, diseases: any[], optimization: any): string[] {
    const recommendations: string[] = [];
    
    if (yield.label === 'low' || yield.label === 'below_average') {
      recommendations.push('Yield below expectations - review soil conditions and irrigation');
    }
    
    const severeDiseases = diseases.filter(d => d.disease === 'severe' || d.disease === 'critical');
    if (severeDiseases.length > 0) {
      recommendations.push(`${severeDiseases.length} severe disease outbreaks - apply treatment immediately`);
    }
    
    if (optimization.label === 'needs_attention' || optimization.label === 'critical') {
      recommendations.push('Crop management needs attention - review fertilization and pest control');
    }
    
    return recommendations;
  }
  
  private async generateCropScenarios(count: number): Promise<any[]> {
    const scenarios = [];
    for (let i = 0; i < count; i++) {
      scenarios.push({
        scenario: `Crop Scenario ${i + 1}`,
        yield: Math.random() * 10 + 5,
        disease: ['healthy', 'mild', 'moderate'][Math.floor(Math.random() * 3)],
        weather: ['optimal', 'good', 'challenging'][Math.floor(Math.random() * 3)]
      });
    }
    return scenarios;
  }
}
```

**ROI**:
- 20% increase in crop yields
- 35% reduction in crop loss
- 30% reduction in pesticide use
- Automated analysis saves 20 hours/week

### Business Case 19: Transportation & Logistics Optimization

**Problem**: Optimize routes, predict delays, manage fleet, and generate logistics scenarios.

**Business Value**: 30% reduction in fuel costs, 25% improvement in on-time delivery, optimized fleet utilization.

**Solution**: Logistics intelligence with route optimization.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class LogisticsOptimizer {
  private delayPredictor: KELMELMEnsemble;
  private routeOptimizer: ELM;
  private fleetManager: ELMChain;
  private synth: OmegaSynth;
  
  async initialize() {
    // 1. Delay prediction (ensemble)
    this.delayPredictor = new KELMELMEnsemble([
      'on_time', 'minor_delay', 'moderate_delay', 'major_delay', 'cancelled'
    ]);
    
    // 2. Route optimization
    const routeTypes = ['optimal', 'efficient', 'acceptable', 'inefficient', 'critical'];
    this.routeOptimizer = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: routeTypes,
      maxLen: 1000
    });
    
    // 3. Fleet management (chain)
    this.fleetManager = new ELMChain([
      'optimal', 'efficient', 'needs_optimization', 'critical'
    ]);
    
    // 4. Synthetic logistics data
    this.synth = loadPretrained('hybrid');
  }
  
  async optimizeLogistics(logistics: {
    shipments: Array<{
      id: string;
      origin: string;
      destination: string;
      priority: string;
      deadline: Date;
    }>;
    vehicles: Array<{
      id: string;
      type: string;
      capacity: number;
      location: string;
      status: string;
    }>;
    traffic: Array<{
      route: string;
      delay: number;
      conditions: string;
    }>;
  }) {
    // Step 1: Predict delays (ensemble)
    const delayPredictions = logistics.shipments.map(shipment => {
      const delayText = `
        Route: ${shipment.origin} to ${shipment.destination}
        Priority: ${shipment.priority}
        Traffic: ${logistics.traffic.find(t => t.route.includes(shipment.origin))?.conditions || 'unknown'}
      `;
      const delay = this.delayPredictor.predict(delayText, 1, 0.7)[0];
      return {
        shipmentId: shipment.id,
        delay: delay.label,
        confidence: delay.prob
      };
    });
    
    // Step 2: Optimize routes
    const routeAnalysis = logistics.shipments.map(shipment => {
      const routeText = `
        From: ${shipment.origin}
        To: ${shipment.destination}
        Priority: ${shipment.priority}
        Available Vehicles: ${logistics.vehicles.filter(v => v.status === 'available').length}
      `;
      const route = this.routeOptimizer.predict(routeText, 1)[0];
      return {
        shipmentId: shipment.id,
        route: route.label,
        confidence: route.prob
      };
    });
    
    // Step 3: Manage fleet (chain)
    const fleetText = `
      Vehicles: ${logistics.vehicles.length}
      Available: ${logistics.vehicles.filter(v => v.status === 'available').length}
      Shipments: ${logistics.shipments.length}
    `;
    const fleet = this.fleetManager.predict(fleetText, 1)[0];
    
    // Step 4: Generate logistics analysis
    const chunks = this.prepareLogisticsChunks(logistics, delayPredictions, routeAnalysis, fleet);
    const reranked = rerankAndFilter(
      'Logistics optimization and route planning',
      chunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.4,
        useMMR: true,
        budgetChars: 2000
      }
    );
    
    const analysis = summarizeDeterministic(
      'Logistics optimization analysis and recommendations',
      reranked,
      {
        personality: 'neutral',
        maxAnswerChars: 1500,
        maxBullets: 10,
        includeCitations: false
      }
    );
    
    // Step 5: Generate recommendations
    const recommendations = this.generateLogisticsRecommendations(delayPredictions, routeAnalysis, fleet);
    
    // Step 6: Generate test scenarios
    const scenarios = await this.generateLogisticsScenarios(20);
    
    return {
      delayPredictions,
      routeAnalysis,
      fleet: {
        status: fleet.label,
        confidence: fleet.prob
      },
      analysis: analysis.text,
      recommendations,
      scenarios
    };
  }
  
  private prepareLogisticsChunks(logistics: any, delays: any[], routes: any[], fleet: any): Chunk[] {
    const chunks: Chunk[] = [];
    
    chunks.push({
      heading: 'Fleet Status',
      content: `Status: ${fleet.label} (${(fleet.prob * 100).toFixed(1)}% confidence)`,
      score_base: fleet.prob
    });
    
    delays.forEach((d, i) => {
      chunks.push({
        heading: `Delay ${i + 1}: ${d.shipmentId}`,
        content: `Predicted delay: ${d.delay} (${(d.confidence * 100).toFixed(1)}% confidence)`,
        score_base: d.confidence
      });
    });
    
    routes.forEach((r, i) => {
      chunks.push({
        heading: `Route ${i + 1}: ${r.shipmentId}`,
        content: `Route optimization: ${r.route} (${(r.confidence * 100).toFixed(1)}% confidence)`,
        score_base: r.confidence
      });
    });
    
    return chunks;
  }
  
  private generateLogisticsRecommendations(delays: any[], routes: any[], fleet: any): string[] {
    const recommendations: string[] = [];
    
    const majorDelays = delays.filter(d => d.delay === 'major_delay' || d.delay === 'cancelled').length;
    if (majorDelays > 0) {
      recommendations.push(`${majorDelays} shipments with major delays - implement contingency plans`);
    }
    
    const inefficientRoutes = routes.filter(r => r.route === 'inefficient' || r.route === 'critical').length;
    if (inefficientRoutes > 0) {
      recommendations.push(`${inefficientRoutes} inefficient routes - optimize routing`);
    }
    
    if (fleet.label === 'needs_optimization' || fleet.label === 'critical') {
      recommendations.push('Fleet utilization needs optimization - review vehicle allocation');
    }
    
    return recommendations;
  }
  
  private async generateLogisticsScenarios(count: number): Promise<any[]> {
    const scenarios = [];
    for (let i = 0; i < count; i++) {
      scenarios.push({
        scenario: `Logistics Scenario ${i + 1}`,
        shipments: Math.floor(Math.random() * 50) + 10,
        vehicles: Math.floor(Math.random() * 20) + 5,
        onTimeRate: Math.random() * 20 + 80
      });
    }
    return scenarios;
  }
}
```

**ROI**:
- 30% reduction in fuel costs
- 25% improvement in on-time delivery
- 20% increase in fleet utilization
- Automated optimization saves 30 hours/week

### Business Case 20: Education & Learning Analytics

**Problem**: Analyze student performance, predict learning outcomes, personalize education, and generate synthetic student data.

**Business Value**: 35% improvement in student outcomes, 40% better personalized learning, data-driven education.

**Solution**: Educational intelligence with adaptive learning.

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic, InfoFlowGraph } from '@astermind/astermind-pro';
import { loadPretrained, OmegaSynth } from '@astermind/astermind-synthetic-data';

class EducationAnalyticsSystem {
  private performancePredictor: KELMELMEnsemble;
  private learningStyleClassifier: ELM;
  private interventionRecommender: ELMChain;
  private synth: OmegaSynth;
  
  async initialize() {
    // 1. Performance prediction (ensemble)
    this.performancePredictor = new KELMELMEnsemble([
      'struggling', 'developing', 'proficient', 'advanced', 'exemplary'
    ]);
    
    // 2. Learning style classification
    const learningStyles = ['visual', 'auditory', 'kinesthetic', 'reading', 'mixed'];
    this.learningStyleClassifier = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: learningStyles,
      maxLen: 1000
    });
    
    // 3. Intervention recommendation (chain)
    this.interventionRecommender = new ELMChain([
      'no_intervention', 'light_support', 'moderate_support', 'intensive_support'
    ]);
    
    // 4. Synthetic student data
    this.synth = loadPretrained('hybrid');
  }
  
  async analyzeStudent(student: {
    id: string;
    assignments: Array<{
      subject: string;
      score: number;
      difficulty: string;
      timeSpent: number;
    }>;
    engagement: {
      attendance: number;
      participation: number;
      homeworkCompletion: number;
    };
    assessments: Array<{
      type: string;
      score: number;
      date: Date;
    }>;
  }) {
    // Step 1: Predict performance (ensemble)
    const performanceText = `
      Average Score: ${student.assignments.reduce((sum, a) => sum + a.score, 0) / student.assignments.length}
      Attendance: ${student.engagement.attendance}%
      Participation: ${student.engagement.participation}%
      Recent Assessments: ${student.assessments.slice(-5).map(a => a.score).join(', ')}
    `;
    const performance = this.performancePredictor.predict(performanceText, 1, 0.6)[0];
    
    // Step 2: Classify learning style
    const learningText = `
      Assignments: ${student.assignments.map(a => `${a.subject}: ${a.score}`).join('; ')}
      Engagement: Attendance ${student.engagement.attendance}%, Participation ${student.engagement.participation}%
    `;
    const learningStyle = this.learningStyleClassifier.predict(learningText, 1)[0];
    
    // Step 3: Recommend interventions (chain)
    const interventionText = `
      Performance: ${performance.label}
      Learning Style: ${learningStyle.label}
      Engagement: ${student.engagement.attendance}%
    `;
    const intervention = this.interventionRecommender.predict(interventionText, 1)[0];
    
    // Step 4: Generate student analysis
    const chunks = this.prepareStudentChunks(student, performance, learningStyle, intervention);
    const reranked = rerankAndFilter(
      `Analyze student ${student.id} performance and learning needs`,
      chunks,
      {
        lambdaRidge: 1e-2,
        probThresh: 0.4,
        useMMR: true,
        budgetChars: 2000
      }
    );
    
    const analysis = summarizeDeterministic(
      `Student performance analysis and personalized learning recommendations`,
      reranked,
      {
        personality: 'teacher',
        maxAnswerChars: 1500,
        maxBullets: 10,
        includeCitations: false
      }
    );
    
    // Step 5: Generate recommendations
    const recommendations = this.generateEducationRecommendations(performance, learningStyle, intervention);
    
    // Step 6: Generate test students
    const testStudents = await this.generateTestStudents(30);
    
    return {
      studentId: student.id,
      performance: {
        level: performance.label,
        confidence: performance.prob
      },
      learningStyle: {
        style: learningStyle.label,
        confidence: learningStyle.prob
      },
      intervention: {
        level: intervention.label,
        confidence: intervention.prob
      },
      analysis: analysis.text,
      recommendations,
      testStudents
    };
  }
  
  private prepareStudentChunks(student: any, performance: any, learningStyle: any, intervention: any): Chunk[] {
    const chunks: Chunk[] = [];
    
    chunks.push({
      heading: 'Performance Level',
      content: `Performance: ${performance.label} (${(performance.prob * 100).toFixed(1)}% confidence)`,
      score_base: performance.prob
    });
    
    chunks.push({
      heading: 'Learning Style',
      content: `Learning style: ${learningStyle.label} (${(learningStyle.prob * 100).toFixed(1)}% confidence)`,
      score_base: learningStyle.prob
    });
    
    chunks.push({
      heading: 'Intervention Recommendation',
      content: `Recommended intervention: ${intervention.label} (${(intervention.prob * 100).toFixed(1)}% confidence)`,
      score_base: intervention.prob
    });
    
    chunks.push({
      heading: 'Engagement Metrics',
      content: `Attendance: ${student.engagement.attendance}%, Participation: ${student.engagement.participation}%, Homework: ${student.engagement.homeworkCompletion}%`,
      score_base: (student.engagement.attendance + student.engagement.participation) / 200
    });
    
    return chunks;
  }
  
  private generateEducationRecommendations(performance: any, learningStyle: any, intervention: any): string[] {
    const recommendations: string[] = [];
    
    if (performance.label === 'struggling' || performance.label === 'developing') {
      recommendations.push(`Student needs ${intervention.label} - provide additional support`);
      recommendations.push(`Adapt teaching methods for ${learningStyle.label} learning style`);
    }
    
    if (intervention.label === 'intensive_support') {
      recommendations.push('Student requires intensive support - consider specialized intervention program');
    }
    
    if (performance.label === 'advanced' || performance.label === 'exemplary') {
      recommendations.push('Student performing well - provide enrichment opportunities');
    }
    
    return recommendations;
  }
  
  private async generateTestStudents(count: number): Promise<any[]> {
    const students = [];
    for (let i = 0; i < count; i++) {
      const firstName = await this.synth.generate('first_name');
      const lastName = await this.synth.generate('last_name');
      students.push({
        id: `STU-${i}`,
        name: `${firstName} ${lastName}`,
        performance: ['struggling', 'developing', 'proficient', 'advanced'][Math.floor(Math.random() * 4)],
        attendance: Math.random() * 20 + 80
      });
    }
    return students;
  }
}
```

**ROI**:
- 35% improvement in student outcomes
- 40% better personalized learning
- 30% reduction in dropout rates
- Automated analysis saves 25 hours/week

---

## Advanced Patterns

### Pattern 1: Adaptive Pipeline with TE Control

```typescript
import { InfoFlowGraph, TEController, rerankAndFilter } from '@astermind/astermind-pro';

class AdaptivePipeline {
  private graph: InfoFlowGraph;
  private controller: TEController;
  private currentKnobs: Knobs;
  
  constructor() {
    this.graph = new InfoFlowGraph({ window: 256 });
    this.controller = new TEController({
      targets: { q2score: [0.02, 0.15], feat2score: [0.02, 0.15] }
    });
    this.currentKnobs = {
      alpha: 0.7,
      sigma: 0.35,
      ridge: 0.05,
      probThresh: 0.45,
      mmrLambda: 0.7,
      budgetChars: 1200
    };
  }
  
  async process(query: string, chunks: Chunk[]) {
    // Process with current knobs
    const results = rerankAndFilter(query, chunks, {
      lambdaRidge: this.currentKnobs.ridge,
      probThresh: this.currentKnobs.probThresh,
      mmrLambda: this.currentKnobs.mmrLambda,
      budgetChars: this.currentKnobs.budgetChars
    });
    
    // Monitor
    this.monitorFlow(query, results);
    
    // Get adjustments
    const adjustment = this.controller.maybeAdjust(this.currentKnobs);
    if (adjustment.knobs) {
      this.currentKnobs = adjustment.knobs;
      console.log(`Adjusted: ${adjustment.note}`);
    }
    
    return results;
  }
  
  private monitorFlow(query: string, results: ScoredChunk[]) {
    const querySig = [query.length / 100, query.split(' ').length / 10];
    const resultSig = [
      results.length,
      results.reduce((sum, r) => sum + r.score_rr, 0) / results.length
    ];
    this.graph.get('Query->Results').push(querySig, resultSig);
  }
}
```

### Pattern 2: Multi-Modal Retrieval

```typescript
import { cosine, normalizeL2, rerank } from '@astermind/astermind-pro';

class MultiModalRetriever {
  async retrieve(
    query: { text: string; image?: Float64Array; audio?: Float64Array },
    documents: Array<{
      text: string;
      image?: Float64Array;
      audio?: Float64Array;
    }>
  ) {
    // Text retrieval
    const textScores = this.textRetrieval(query.text, documents);
    
    // Image retrieval (if available)
    const imageScores = query.image 
      ? this.imageRetrieval(query.image, documents)
      : new Map();
    
    // Audio retrieval (if available)
    const audioScores = query.audio
      ? this.audioRetrieval(query.audio, documents)
      : new Map();
    
    // Combine scores
    const combined = documents.map((doc, i) => {
      const text = textScores.get(i) || 0;
      const image = imageScores.get(i) || 0;
      const audio = audioScores.get(i) || 0;
      
      // Weighted combination
      const score = 0.5 * text + 0.3 * image + 0.2 * audio;
      return { doc, score };
    });
    
    return combined.sort((a, b) => b.score - a.score);
  }
  
  private textRetrieval(query: string, docs: any[]): Map<number, number> {
    // Your text retrieval logic
    return new Map();
  }
  
  private imageRetrieval(query: Float64Array, docs: any[]): Map<number, number> {
    const scores = new Map<number, number>();
    const normalizedQuery = normalizeL2(query);
    
    docs.forEach((doc, i) => {
      if (doc.image) {
        const normalized = normalizeL2(doc.image);
        scores.set(i, cosine(normalizedQuery, normalized));
      }
    });
    
    return scores;
  }
  
  private audioRetrieval(query: Float64Array, docs: any[]): Map<number, number> {
    // Similar to image retrieval
    return new Map();
  }
}
```

### Pattern 3: Streaming Pipeline

```typescript
import { OnlineRidge, rerank } from '@astermind/astermind-pro';

class StreamingPipeline {
  private ridge: OnlineRidge;
  private buffer: Chunk[] = [];
  
  constructor() {
    this.ridge = new OnlineRidge(64, 1, 1e-3);
  }
  
  async processStream(
    query: string,
    stream: AsyncIterable<Chunk>
  ): Promise<AsyncIterable<ScoredChunk>> {
    return this.streamingRerank(query, stream);
  }
  
  private async *streamingRerank(
    query: string,
    stream: AsyncIterable<Chunk>
  ): AsyncGenerator<ScoredChunk> {
    for await (const chunk of stream) {
      this.buffer.push(chunk);
      
      // Rerank buffer periodically
      if (this.buffer.length % 10 === 0) {
        const reranked = rerank(query, this.buffer, {
          lambdaRidge: 1e-2
        });
        
        // Yield top results
        for (const result of reranked.slice(0, 5)) {
          yield result;
        }
      }
    }
    
    // Final rerank
    const final = rerank(query, this.buffer, {
      lambdaRidge: 1e-2
    });
    
    for (const result of final) {
      yield result;
    }
  }
}
```

---

## Advanced Architectures: Ensembles & Chaining

Advanced ML architectures using ensemble methods and model chaining for improved performance and complex problem solving.

### Overview

**Ensemble Methods**: Combine multiple models to improve accuracy and robustness
- **ELM/ELM Ensemble**: Multiple ELM models voting together
- **KELM/ELM Ensemble**: KernelELM and ELM combined for non-linear + linear patterns

**Chaining Methods**: Feed one model's output into another for hierarchical processing
- **ELM Chaining**: Sequential ELM models (feature extraction → classification)
- **KELM/ELM Chaining**: KernelELM for feature extraction, ELM for final classification
- **ELM/KELM Chaining**: ELM for initial processing, KernelELM for refinement

---

### Ensemble Method 1: ELM/ELM Ensemble

**Use Case**: When you need robust classification with multiple perspectives

```typescript
import { ELM } from '@astermind/astermind-elm';

class ELMEnsemble {
  private models: ELM[] = [];
  private categories: string[];
  
  constructor(categories: string[], numModels: number = 3) {
    this.categories = categories;
    
    // Create multiple ELM models with different configurations
    for (let i = 0; i < numModels; i++) {
      const elm = new ELM({
        useTokenizer: true,
        hiddenUnits: 256 + i * 64, // Vary hidden units
        categories,
        maxLen: 100,
        activation: i % 2 === 0 ? 'relu' : 'tanh' // Vary activation
      });
      this.models.push(elm);
    }
  }
  
  async train(trainingData: Array<{ text: string; label: string }>) {
    const texts = trainingData.map(d => d.text);
    const labels = trainingData.map(d => d.label);
    const labelIndices = labels.map(l => this.categories.indexOf(l));
    
    // Train each model independently
    for (const elm of this.models) {
      (elm as any).setCategories(this.categories);
      const encodedTexts = texts.map(text => {
        const encoded = (elm as any).encoder.encode(text);
        return (elm as any).encoder.normalize(encoded);
      });
      elm.trainFromData(encodedTexts, labelIndices);
    }
  }
  
  predict(text: string, topK: number = 3): Array<{ label: string; prob: number }> {
    // Get predictions from all models
    const allPredictions = this.models.map(elm => elm.predict(text, topK));
    
    // Combine predictions (weighted voting)
    const combined = new Map<string, number>();
    
    allPredictions.forEach((predictions, modelIdx) => {
      const weight = 1.0 / this.models.length; // Equal weight
      predictions.forEach(p => {
        const current = combined.get(p.label) || 0;
        combined.set(p.label, current + p.prob * weight);
      });
    });
    
    // Convert to array and sort
    const result = Array.from(combined.entries())
      .map(([label, prob]) => ({ label, prob }))
      .sort((a, b) => b.prob - a.prob)
      .slice(0, topK);
    
    return result;
  }
  
  // Confidence-based weighting (higher confidence models get more weight)
  predictWeighted(text: string, topK: number = 3): Array<{ label: string; prob: number }> {
    const allPredictions = this.models.map(elm => {
      const preds = elm.predict(text, 1);
      return {
        predictions: elm.predict(text, topK),
        confidence: preds[0]?.prob || 0
      };
    });
    
    // Weight by confidence
    const totalConfidence = allPredictions.reduce((sum, p) => sum + p.confidence, 0);
    const combined = new Map<string, number>();
    
    allPredictions.forEach(({ predictions, confidence }) => {
      const weight = totalConfidence > 0 ? confidence / totalConfidence : 1 / this.models.length;
      predictions.forEach(p => {
        const current = combined.get(p.label) || 0;
        combined.set(p.label, current + p.prob * weight);
      });
    });
    
    return Array.from(combined.entries())
      .map(([label, prob]) => ({ label, prob }))
      .sort((a, b) => b.prob - a.prob)
      .slice(0, topK);
  }
}
```

**Business Use Case**: Customer support ticket classification
- Multiple ELM models trained on different data splits
- Ensemble voting reduces misclassification
- Confidence weighting prioritizes reliable models

---

### Ensemble Method 2: KELM/ELM Ensemble

**Use Case**: Combining linear (ELM) and non-linear (KernelELM) pattern recognition

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';

class KELMELMEnsemble {
  private elm: ELM;
  private kelm: KernelELM;
  private categories: string[];
  private encoder: any;
  
  constructor(categories: string[]) {
    this.categories = categories;
    
    // ELM for linear patterns
    this.elm = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories,
      maxLen: 100
    });
    
    // KernelELM for non-linear patterns
    this.kelm = new KernelELM({
      outputDim: categories.length,
      kernel: {
        type: 'rbf',
        gamma: 0.01
      },
      ridgeLambda: 0.001,
      task: 'classification',
      mode: 'nystrom',
      nystrom: {
        m: 100,
        strategy: 'uniform'
      }
    });
  }
  
  async train(trainingData: Array<{ text: string; label: string }>) {
    const texts = trainingData.map(d => d.text);
    const labels = trainingData.map(d => d.label);
    
    // Train ELM
    (this.elm as any).setCategories(this.categories);
    const labelIndices = labels.map(l => this.categories.indexOf(l));
    const encodedTexts = texts.map(text => {
      const encoded = (this.elm as any).encoder.encode(text);
      return (this.elm as any).encoder.normalize(encoded);
    });
    this.elm.trainFromData(encodedTexts, labelIndices);
    this.encoder = (this.elm as any).encoder;
    
    // Train KernelELM on same encoded features
    const oneHotLabels = labels.map(label => {
      const idx = this.categories.indexOf(label);
      const oneHot = new Array(this.categories.length).fill(0);
      oneHot[idx] = 1;
      return oneHot;
    });
    
    this.kelm.fit(encodedTexts, oneHotLabels);
  }
  
  predict(text: string, topK: number = 3, kelmWeight: number = 0.6): Array<{ label: string; prob: number }> {
    // ELM prediction
    const elmPreds = this.elm.predict(text, this.categories.length);
    const elmProbs = new Array(this.categories.length).fill(0);
    elmPreds.forEach(p => {
      const idx = this.categories.indexOf(p.label);
      if (idx >= 0) elmProbs[idx] = p.prob;
    });
    
    // KernelELM prediction
    const encoded = this.encoder.encode(text);
    const normalized = this.encoder.normalize(encoded);
    const kelmProbs = this.kelm.predictProbaFromVectors([normalized])[0];
    
    // Combine (weighted average)
    const elmWeight = 1 - kelmWeight;
    const combined = this.categories.map((label, idx) => ({
      label,
      prob: elmWeight * elmProbs[idx] + kelmWeight * kelmProbs[idx]
    }));
    
    return combined
      .sort((a, b) => b.prob - a.prob)
      .slice(0, topK);
  }
}
```

**Business Use Case**: Fraud detection
- ELM captures linear patterns (amount thresholds, time patterns)
- KernelELM captures complex non-linear interactions
- Ensemble improves detection accuracy

---

### Chaining Method 1: ELM Chaining (Feature Extraction → Classification)

**Use Case**: Hierarchical feature learning and classification

```typescript
import { ELM } from '@astermind/astermind-elm';

class ELMChain {
  private featureExtractor: ELM;
  private classifier: ELM;
  private categories: string[];
  
  constructor(categories: string[]) {
    this.categories = categories;
    
    // First ELM: Feature extraction (autoencoder-like)
    this.featureExtractor = new ELM({
      useTokenizer: true,
      hiddenUnits: 512, // Large hidden layer for rich features
      categories: [], // No classification, just feature extraction
      maxLen: 200,
      activation: 'relu'
    });
    
    // Second ELM: Classification on extracted features
    this.classifier = new ELM({
      useTokenizer: false, // Input is already feature vectors
      inputSize: 512, // Matches feature extractor output
      categories,
      hiddenUnits: 256,
      activation: 'tanh'
    });
  }
  
  async train(trainingData: Array<{ text: string; label: string }>) {
    const texts = trainingData.map(d => d.text);
    const labels = trainingData.map(d => d.label);
    
    // Step 1: Train feature extractor (reconstruct input)
    (this.featureExtractor as any).setCategories([]);
    const encodedTexts = texts.map(text => {
      const encoded = (this.featureExtractor as any).encoder.encode(text);
      return (this.featureExtractor as any).encoder.normalize(encoded);
    });
    
    // Train as autoencoder (input -> input)
    this.featureExtractor.trainFromData(encodedTexts, encodedTexts);
    
    // Step 2: Extract features using trained extractor
    const extractedFeatures = encodedTexts.map(encoded => {
      // Get hidden layer representation
      const hidden = (this.featureExtractor as any).buildHidden(
        [encoded],
        (this.featureExtractor as any).model.W,
        (this.featureExtractor as any).model.b
      );
      return hidden[0];
    });
    
    // Step 3: Train classifier on extracted features
    const labelIndices = labels.map(l => this.categories.indexOf(l));
    this.classifier.trainFromData(extractedFeatures, labelIndices);
  }
  
  predict(text: string, topK: number = 3): Array<{ label: string; prob: number }> {
    // Step 1: Extract features
    const encoded = (this.featureExtractor as any).encoder.encode(text);
    const normalized = (this.featureExtractor as any).encoder.normalize(encoded);
    const hidden = (this.featureExtractor as any).buildHidden(
      [normalized],
      (this.featureExtractor as any).model.W,
      (this.featureExtractor as any).model.b
    );
    const features = hidden[0];
    
    // Step 2: Classify
    return this.classifier.predictFromVector([features], topK);
  }
}
```

**Business Use Case**: Document categorization
- First ELM learns document-level features
- Second ELM classifies based on learned features
- Better generalization than single-stage classification

---

### Chaining Method 2: KELM/ELM Chaining (Non-linear Features → Linear Classification)

**Use Case**: Complex feature extraction with efficient classification

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';

class KELMELMChain {
  private featureExtractor: KernelELM;
  private classifier: ELM;
  private categories: string[];
  private encoder: any;
  
  constructor(categories: string[], featureDim: number = 128) {
    this.categories = categories;
    
    // Temporary ELM for encoding
    const tempELM = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: [],
      maxLen: 200
    });
    this.encoder = (tempELM as any).encoder;
    
    // KernelELM for non-linear feature extraction
    this.featureExtractor = new KernelELM({
      outputDim: featureDim,
      kernel: {
        type: 'rbf',
        gamma: 0.01
      },
      ridgeLambda: 0.001,
      task: 'regression', // Feature extraction, not classification
      mode: 'nystrom',
      nystrom: {
        m: 100,
        strategy: 'uniform'
      }
    });
    
    // ELM for final classification
    this.classifier = new ELM({
      useTokenizer: false,
      inputSize: featureDim,
      categories,
      hiddenUnits: 128,
      activation: 'relu'
    });
  }
  
  async train(trainingData: Array<{ text: string; label: string }>) {
    const texts = trainingData.map(d => d.text);
    const labels = trainingData.map(d => d.label);
    
    // Encode texts
    const encodedTexts = texts.map(text => {
      const encoded = this.encoder.encode(text);
      return this.encoder.normalize(encoded);
    });
    
    // Step 1: Train KernelELM feature extractor
    // Use encoded texts as both input and target (autoencoder-like)
    this.featureExtractor.fit(encodedTexts, encodedTexts);
    
    // Step 2: Extract features using KernelELM
    const extractedFeatures = this.featureExtractor.transform(encodedTexts);
    
    // Step 3: Train ELM classifier on extracted features
    const labelIndices = labels.map(l => this.categories.indexOf(l));
    this.classifier.trainFromData(extractedFeatures, labelIndices);
  }
  
  predict(text: string, topK: number = 3): Array<{ label: string; prob: number }> {
    // Step 1: Encode
    const encoded = this.encoder.encode(text);
    const normalized = this.encoder.normalize(encoded);
    
    // Step 2: Extract features with KernelELM
    const features = this.featureExtractor.transform([normalized])[0];
    
    // Step 3: Classify with ELM
    return this.classifier.predictFromVector([features], topK);
  }
}
```

**Business Use Case**: Sentiment analysis
- KernelELM captures complex sentiment patterns
- ELM efficiently classifies on extracted features
- Better accuracy than single-stage models

---

### Chaining Method 3: ELM/KELM Chaining (Initial Processing → Refinement)

**Use Case**: Initial classification with non-linear refinement

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';

class ELMKELMChain {
  private initialClassifier: ELM;
  private refiner: KernelELM;
  private categories: string[];
  
  constructor(categories: string[]) {
    this.categories = categories;
    
    // ELM for initial classification
    this.initialClassifier = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories,
      maxLen: 200
    });
    
    // KernelELM for refinement
    this.refiner = new KernelELM({
      outputDim: categories.length,
      kernel: {
        type: 'rbf',
        gamma: 0.01
      },
      ridgeLambda: 0.001,
      task: 'classification',
      mode: 'nystrom',
      nystrom: {
        m: 50,
        strategy: 'uniform'
      }
    });
  }
  
  async train(trainingData: Array<{ text: string; label: string }>) {
    const texts = trainingData.map(d => d.text);
    const labels = trainingData.map(d => d.label);
    
    // Step 1: Train initial ELM classifier
    (this.initialClassifier as any).setCategories(this.categories);
    const labelIndices = labels.map(l => this.categories.indexOf(l));
    const encodedTexts = texts.map(text => {
      const encoded = (this.initialClassifier as any).encoder.encode(text);
      return (this.initialClassifier as any).encoder.normalize(encoded);
    });
    this.initialClassifier.trainFromData(encodedTexts, labelIndices);
    
    // Step 2: Get initial predictions as features
    const initialFeatures = encodedTexts.map(encoded => {
      const preds = this.initialClassifier.predictFromVector([encoded], this.categories.length)[0];
      return preds.map(p => p.prob);
    });
    
    // Step 3: Train KernelELM refiner on initial predictions + original features
    const combinedFeatures = encodedTexts.map((encoded, i) => {
      return [...encoded, ...initialFeatures[i]];
    });
    
    const oneHotLabels = labels.map(label => {
      const idx = this.categories.indexOf(label);
      const oneHot = new Array(this.categories.length).fill(0);
      oneHot[idx] = 1;
      return oneHot;
    });
    
    this.refiner.fit(combinedFeatures, oneHotLabels);
  }
  
  predict(text: string, topK: number = 3): Array<{ label: string; prob: number }> {
    // Step 1: Initial classification
    const encoded = (this.initialClassifier as any).encoder.encode(text);
    const normalized = (this.initialClassifier as any).encoder.normalize(encoded);
    const initialPreds = this.initialClassifier.predictFromVector([normalized], this.categories.length)[0];
    const initialProbs = initialPreds.map(p => p.prob);
    
    // Step 2: Refine with KernelELM
    const combined = [...normalized, ...initialProbs];
    const refinedProbs = this.refiner.predictProbaFromVectors([combined])[0];
    
    // Combine initial and refined (weighted)
    const final = this.categories.map((label, idx) => ({
      label,
      prob: 0.3 * initialProbs[idx] + 0.7 * refinedProbs[idx]
    }));
    
    return final
      .sort((a, b) => b.prob - a.prob)
      .slice(0, topK);
  }
}
```

**Business Use Case**: Medical diagnosis
- ELM provides initial diagnosis
- KernelELM refines based on initial prediction + symptoms
- Improved accuracy for complex cases

---

### Real-World Business Use Cases

#### Use Case 1: Multi-Stage Content Moderation

**Problem**: Classify content safety, then determine severity level

**Solution**: ELM Chain

```typescript
class ContentModerationSystem {
  private safetyClassifier: ELM;
  private severityClassifier: ELM;
  
  async initialize() {
    // Stage 1: Safety classification
    this.safetyClassifier = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: ['safe', 'unsafe'],
      maxLen: 500
    });
    
    // Stage 2: Severity classification (only for unsafe content)
    this.severityClassifier = new ELM({
      useTokenizer: false,
      inputSize: 256, // Features from safety classifier
      categories: ['low', 'medium', 'high', 'critical'],
      hiddenUnits: 128
    });
  }
  
  async moderate(content: string) {
    // Stage 1: Safety check
    const safety = this.safetyClassifier.predict(content, 1)[0];
    
    if (safety.label === 'safe') {
      return { action: 'approve', safety: 'safe' };
    }
    
    // Stage 2: Extract features and classify severity
    const encoded = (this.safetyClassifier as any).encoder.encode(content);
    const normalized = (this.safetyClassifier as any).encoder.normalize(encoded);
    const features = this.extractFeatures(normalized);
    
    const severity = this.severityClassifier.predictFromVector([features], 1)[0];
    
    return {
      action: 'flag',
      safety: 'unsafe',
      severity: severity.label,
      confidence: severity.prob
    };
  }
  
  private extractFeatures(encoded: number[]): number[] {
    // Extract hidden layer features from safety classifier
    const hidden = (this.safetyClassifier as any).buildHidden(
      [encoded],
      (this.safetyClassifier as any).model.W,
      (this.safetyClassifier as any).model.b
    );
    return hidden[0];
  }
}
```

#### Use Case 2: Financial Risk Assessment

**Problem**: Assess credit risk with multiple factors

**Solution**: KELM/ELM Ensemble

```typescript
class CreditRiskAssessor {
  private ensemble: KELMELMEnsemble;
  
  async initialize() {
    this.ensemble = new KELMELMEnsemble([
      'low_risk',
      'medium_risk',
      'high_risk',
      'reject'
    ]);
  }
  
  async assess(application: {
    income: number;
    creditScore: number;
    debtRatio: number;
    employmentHistory: string;
    loanAmount: number;
  }) {
    // Format as text for processing
    const text = `
      Income: ${application.income}
      Credit Score: ${application.creditScore}
      Debt Ratio: ${application.debtRatio}
      Employment: ${application.employmentHistory}
      Loan Amount: ${application.loanAmount}
    `;
    
    // Ensemble prediction (KELM captures non-linear interactions)
    const prediction = this.ensemble.predict(text, 1, 0.7); // 70% weight to KELM
    
    return {
      risk: prediction[0].label,
      confidence: prediction[0].prob,
      recommendation: this.getRecommendation(prediction[0].label)
    };
  }
  
  private getRecommendation(risk: string): string {
    const recommendations = {
      low_risk: 'Approve with standard terms',
      medium_risk: 'Approve with higher interest rate',
      high_risk: 'Approve with strict terms',
      reject: 'Reject application'
    };
    return recommendations[risk] || 'Review required';
  }
}
```

#### Use Case 3: Hierarchical Document Classification

**Problem**: Classify documents by category, then by subcategory

**Solution**: ELM Chain

```typescript
class HierarchicalDocumentClassifier {
  private categoryClassifier: ELM;
  private subcategoryClassifiers: Map<string, ELM>;
  
  async initialize() {
    // Top-level categories
    const categories = ['legal', 'financial', 'technical', 'medical'];
    this.categoryClassifier = new ELM({
      useTokenizer: true,
      hiddenUnits: 512,
      categories,
      maxLen: 2000
    });
    
    // Subcategory classifiers (one per category)
    this.subcategoryClassifiers = new Map();
    const subcategories = {
      legal: ['contract', 'brief', 'motion', 'opinion'],
      financial: ['invoice', 'statement', 'report', 'analysis'],
      technical: ['spec', 'manual', 'ticket', 'bug_report'],
      medical: ['record', 'diagnosis', 'prescription', 'report']
    };
    
    for (const [category, subs] of Object.entries(subcategories)) {
      const subClassifier = new ELM({
        useTokenizer: false,
        inputSize: 512, // Features from category classifier
        categories: subs,
        hiddenUnits: 256
      });
      this.subcategoryClassifiers.set(category, subClassifier);
    }
  }
  
  async classify(document: string) {
    // Stage 1: Category classification
    const categoryPred = this.categoryClassifier.predict(document, 1)[0];
    const category = categoryPred.label;
    
    // Stage 2: Extract features
    const encoded = (this.categoryClassifier as any).encoder.encode(document);
    const normalized = (this.categoryClassifier as any).encoder.normalize(encoded);
    const features = this.extractFeatures(normalized);
    
    // Stage 3: Subcategory classification
    const subClassifier = this.subcategoryClassifiers.get(category);
    if (!subClassifier) {
      return { category, subcategory: 'unknown' };
    }
    
    const subcategoryPred = subClassifier.predictFromVector([features], 1)[0];
    
    return {
      category,
      categoryConfidence: categoryPred.prob,
      subcategory: subcategoryPred.label,
      subcategoryConfidence: subcategoryPred.prob
    };
  }
  
  private extractFeatures(encoded: number[]): number[] {
    const hidden = (this.categoryClassifier as any).buildHidden(
      [encoded],
      (this.categoryClassifier as any).model.W,
      (this.categoryClassifier as any).model.b
    );
    return hidden[0];
  }
}
```

---

### Advanced Business Use Case: Intelligent Document Processing Pipeline

**Problem**: Process legal documents with multi-stage analysis: extract entities, classify document type, assess relevance, and generate summaries.

**Solution**: Combined chaining and ensemble methods

```typescript
import { ELM, KernelELM } from '@astermind/astermind-elm';
import { rerankAndFilter, summarizeDeterministic } from '@astermind/astermind-pro';
import { loadPretrained } from '@astermind/astermind-synthetic-data';

class IntelligentDocumentProcessor {
  // Stage 1: Entity extraction (ELM)
  private entityExtractor: ELM;
  
  // Stage 2: Document type classification (KELM/ELM Ensemble)
  private typeEnsemble: KELMELMEnsemble;
  
  // Stage 3: Relevance assessment (ELM Chain)
  private relevanceChain: ELMChain;
  
  // Stage 4: Summary generation (Pro)
  private synth: any;
  
  constructor() {
    // Entity extraction
    const entityTypes = ['person', 'organization', 'date', 'amount', 'location'];
    this.entityExtractor = new ELM({
      useTokenizer: true,
      hiddenUnits: 256,
      categories: entityTypes,
      maxLen: 500
    });
    
    // Document type classification (ensemble)
    const docTypes = ['contract', 'brief', 'motion', 'opinion', 'correspondence'];
    this.typeEnsemble = new KELMELMEnsemble(docTypes);
    
    // Relevance assessment (chain)
    const relevanceLevels = ['critical', 'important', 'relevant', 'low_priority'];
    this.relevanceChain = new ELMChain(relevanceLevels);
    
    // Synthetic data for testing
    this.synth = loadPretrained('retrieval');
  }
  
  async processDocument(document: {
    content: string;
    metadata: any;
  }) {
    // Stage 1: Extract entities
    const entities = await this.extractEntities(document.content);
    
    // Stage 2: Classify document type (ensemble)
    const docType = this.typeEnsemble.predict(document.content, 1, 0.6)[0];
    
    // Stage 3: Assess relevance (chain)
    const relevance = this.relevanceChain.predict(document.content, 1)[0];
    
    // Stage 4: Generate summary if relevant
    let summary = null;
    if (relevance.label !== 'low_priority') {
      const chunks = this.chunkDocument(document.content);
      const reranked = rerankAndFilter(
        `Summarize ${docType.label} document`,
        chunks,
        {
          lambdaRidge: 1e-2,
          probThresh: 0.5,
          useMMR: true,
          budgetChars: 2000
        }
      );
      
      summary = summarizeDeterministic(
        `Summarize ${docType.label} document`,
        reranked,
        {
          personality: 'neutral',
          maxAnswerChars: 1000,
          includeCitations: true
        }
      );
    }
    
    // Stage 5: Generate synthetic test cases
    const testCases = await this.generateTestCases(docType.label, 10);
    
    return {
      entities,
      documentType: {
        type: docType.label,
        confidence: docType.prob
      },
      relevance: {
        level: relevance.label,
        confidence: relevance.prob
      },
      summary: summary?.text || null,
      testCases
    };
  }
  
  private async extractEntities(text: string): Promise<Array<{ type: string; value: string }>> {
    // Use entity extractor to find entities
    const sentences = text.split(/[.!?]+/);
    const entities: Array<{ type: string; value: string }> = [];
    
    for (const sentence of sentences.slice(0, 20)) { // Limit for performance
      const preds = this.entityExtractor.predict(sentence, 3);
      if (preds[0].prob > 0.5) {
        entities.push({
          type: preds[0].label,
          value: sentence.substring(0, 50) // Simplified
        });
      }
    }
    
    return entities;
  }
  
  private chunkDocument(content: string): Chunk[] {
    // Split document into chunks
    const paragraphs = content.split(/\n\n+/);
    return paragraphs.map((para, i) => ({
      heading: `Paragraph ${i + 1}`,
      content: para,
      score_base: 0.5
    }));
  }
  
  private async generateTestCases(docType: string, count: number): Promise<string[]> {
    const cases: string[] = [];
    for (let i = 0; i < count; i++) {
      const company = await this.synth.generate('company_name');
      const date = await this.synth.generate('date');
      cases.push(`${docType} document: ${company} - ${date}`);
    }
    return cases;
  }
}
```

**Why This Architecture Works**:

1. **Entity Extraction (ELM)**: Fast, efficient for named entity recognition
2. **Type Classification (KELM/ELM Ensemble)**: Combines linear and non-linear patterns for accurate classification
3. **Relevance Assessment (ELM Chain)**: Hierarchical feature learning for nuanced relevance scoring
4. **Summary Generation (Pro)**: Production-grade summarization with reranking
5. **Test Case Generation (Synth)**: Privacy-safe synthetic data for testing

**Business Value**:
- **Accuracy**: Ensemble methods reduce classification errors
- **Efficiency**: Chaining allows specialized models at each stage
- **Scalability**: Each stage can be optimized independently
- **Maintainability**: Clear separation of concerns

---

## Integration Examples

### Integration with React

```typescript
import { useState, useEffect } from 'react';
import { rerankAndFilter, summarizeDeterministic } from '@astermind/astermind-pro';

function SearchComponent() {
  const [query, setQuery] = useState('');
  const [results, setResults] = useState<ScoredChunk[]>([]);
  const [summary, setSummary] = useState('');
  const [loading, setLoading] = useState(false);
  
  const handleSearch = async () => {
    setLoading(true);
    
    try {
      // Rerank
      const reranked = rerankAndFilter(query, documents, {
        lambdaRidge: 1e-2,
        probThresh: 0.45,
        useMMR: true,
        budgetChars: 1200
      });
      
      setResults(reranked);
      
      // Summarize
      const summaryResult = summarizeDeterministic(query, reranked, {
        maxAnswerChars: 1000,
        includeCitations: true
      });
      
      setSummary(summaryResult.text);
    } finally {
      setLoading(false);
    }
  };
  
  return (
    <div>
      <input value={query} onChange={e => setQuery(e.target.value)} />
      <button onClick={handleSearch}>Search</button>
      
      {loading && <div>Loading...</div>}
      
      {summary && (
        <div>
          <h3>Summary</h3>
          <p>{summary}</p>
        </div>
      )}
      
      {results.map((result, i) => (
        <div key={i}>
          <h4>{result.heading}</h4>
          <p>Relevance: {(result.p_relevant * 100).toFixed(1)}%</p>
          <p>{result.content.slice(0, 200)}...</p>
        </div>
      ))}
    </div>
  );
}
```

### Integration with Node.js API

```typescript
import express from 'express';
import { rerankAndFilter, summarizeDeterministic } from '@astermind/astermind-pro';

const app = express();
app.use(express.json());

app.post('/api/search', async (req, res) => {
  const { query, documents } = req.body;
  
  try {
    // Rerank
    const reranked = rerankAndFilter(query, documents, {
      lambdaRidge: 1e-2,
      probThresh: 0.45,
      useMMR: true,
      budgetChars: 1200
    });
    
    // Summarize
    const summary = summarizeDeterministic(query, reranked, {
      maxAnswerChars: 1000,
      includeCitations: true
    });
    
    res.json({
      summary: summary.text,
      results: reranked.map(r => ({
        heading: r.heading,
        relevance: r.p_relevant,
        score: r.score_rr
      })),
      citations: summary.cites
    });
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

app.listen(3000);
```

---

## Performance Optimization

### 1. Batch Processing

```typescript
// Process multiple queries in batch
async function batchProcess(
  queries: string[],
  documents: Chunk[]
): Promise<Array<{ query: string; results: ScoredChunk[] }>> {
  // Pre-compute document features once
  const docFeatures = documents.map(doc => 
    computeFeatures(doc)
  );
  
  // Process queries in parallel
  const results = await Promise.all(
    queries.map(async query => {
      const queryFeatures = computeFeatures({ content: query });
      const reranked = rerank(query, documents, {
        lambdaRidge: 1e-2
      });
      return { query, results: reranked };
    })
  );
  
  return results;
}
```

### 2. Caching

```typescript
class CachedPipeline {
  private cache = new Map<string, ScoredChunk[]>();
  
  async process(query: string, documents: Chunk[]) {
    const cacheKey = this.getCacheKey(query, documents);
    
    if (this.cache.has(cacheKey)) {
      return this.cache.get(cacheKey)!;
    }
    
    const results = rerankAndFilter(query, documents, {
      lambdaRidge: 1e-2
    });
    
    this.cache.set(cacheKey, results);
    return results;
  }
  
  private getCacheKey(query: string, docs: Chunk[]): string {
    return `${query}:${docs.length}:${docs.map(d => d.heading).join(',')}`;
  }
}
```

### 3. Incremental Updates

```typescript
// Use OnlineRidge for incremental learning
class IncrementalReranker {
  private ridge: OnlineRidge;
  
  constructor() {
    this.ridge = new OnlineRidge(64, 1, 1e-3);
  }
  
  update(features: Float64Array, relevance: number) {
    this.ridge.update(features, new Float64Array([relevance]));
  }
  
  score(features: Float64Array): number {
    return this.ridge.predict(features)[0];
  }
}
```

---

## Advanced ELM Variants

### DeepELMPro - Improved Deep ELM

**Key Improvements over Base DeepELM:**

1. **Autoencoder Pretraining** - Each layer can be pretrained as an autoencoder for better feature learning
2. **Layer-wise Training** - Sequential layer training for more stable learning (base DeepELM only supports joint training)
3. **Regularization** - L1/L2/Elastic Net regularization to prevent overfitting (not in base DeepELM)
4. **Batch Normalization** - Optional normalization between layers for faster convergence (not in base DeepELM)
5. **Dropout** - Optional dropout with configurable rate to reduce overfitting (not in base DeepELM)
6. **Flexible Training** - Choose between layer-wise or joint training modes (base DeepELM is joint-only)

**Example Usage:**

```typescript
import { DeepELMPro } from '@astermind/astermind-pro';

// Create DeepELMPro with advanced features
const deepElm = new DeepELMPro({
  layers: [256, 128, 64], // Three hidden layers
  categories: ['positive', 'negative', 'neutral'],
  activation: 'relu',
  useDropout: true,
  dropoutRate: 0.2,
  useBatchNorm: true,
  regularization: {
    type: 'l2',
    lambda: 0.0001,
  },
  layerWiseTraining: true,
  pretraining: true, // Enable autoencoder pretraining
  maxLen: 100,
});

// Train with improved strategies
await deepElm.train(X, y);

// Predict
const predictions = deepElm.predict(query, 3);
```

**When to Use DeepELMPro vs Base DeepELM:**
- Use **DeepELMPro** when you need better generalization, have overfitting issues, or want more training control
- Use **Base DeepELM** for simpler use cases where basic multi-layer learning is sufficient

---

## Best Practices

1. **Start Simple**: Begin with basic reranking, then add complexity
2. **Monitor Quality**: Use Transfer Entropy to monitor pipeline health
3. **Tune Gradually**: Adjust parameters incrementally
4. **Cache Aggressively**: Cache expensive computations
5. **Batch When Possible**: Process multiple items together
6. **Use Production Worker**: For inference-only deployments
7. **Validate Inputs**: Check data quality before processing
8. **Handle Errors**: Gracefully handle edge cases

---

## Troubleshooting

### Low Relevance Scores

- Increase `probThresh` in reranking
- Adjust `queryWeight` in summarization
- Check input data quality

### Poor Diversity

- Increase `mmrLambda` in MMR filtering
- Adjust `budgetChars` to allow more content

### Slow Performance

- Use production worker for inference
- Enable caching
- Reduce `randomProjDim`
- Batch process queries

---

## Next Steps

1. Explore the [API Reference](#api-reference)
2. Try the [Use Cases](#real-world-use-cases)
3. Build your own [Custom Pipeline](#building-custom-pipelines)
4. Optimize for your [Performance Requirements](#performance-optimization)

For more examples and support, see the main [README.md](../../README.md) and [PREMIUM_FEATURES.md](../features/PREMIUM_FEATURES.md).