# VC Portfolio Monitoring Report
# Use case: Monthly portfolio review across multiple portfolio companies.
# Each company has its own docset (separate GraphRAG index).
# Extracts ARR, burn, runway, headcount, and key risks per company,
# then synthesises a cross-portfolio dashboard.
# Schema v2.2

schema_version: "2.2"

report:
  id: vc_portfolio_monitoring_q1_2026
  name: "VC Portfolio Monitoring — Q1 2026"
  description: |
    Cross-portfolio KPI dashboard extracting key metrics from each portfolio
    company's board update or monthly investor report.
    Covers ARR/revenue, burn/runway, headcount, recent milestones, and risks.
  version: "2026.Q1"

context:
  fund_name: "Acme Ventures Fund III"
  reporting_period: "Q1 2026"
  as_of_date: "2026-03-31"
  currency: "USD"
  units_policy: |
    Return raw full-dollar numbers. Convert abbreviations:
    "$2.5M" → 2500000, "$350K" → 350000, "$1.2B" → 1200000000.
    Percentages as whole numbers: 92.3 not 0.923.
    Return null for any value not explicitly found in the document.
  answer_quality_policy:
    numeric_requirements:
      - "Every numeric value must have a citation (c_xxxxxxxx format)"
      - "Return null for values not explicitly stated — never estimate"
    labeling_requirements:
      - "Always include the reporting period with each metric"

doc_repository:
  api_base_url: ${DOC_REPO_URL:-http://localhost:8080}
  api_key: ${DOC_REPO_API_KEY}
  rotation: 200

mcp: []

# Each portfolio company is its own docset with its own GraphRAG index
docsets:
  - id: company_alpha
    description: "Alpha Inc — Q1 2026 board update"
    api_key: ${OPENAI_API_KEY}
    docs:
      - id: alpha_board_update
        locator: "repo:doc-alpha0001"
        description: "Alpha Inc Q1 2026 Board Update"

  - id: company_beta
    description: "Beta Systems — Q1 2026 monthly investor report"
    api_key: ${OPENAI_API_KEY}
    docs:
      - id: beta_investor_report
        locator: "repo:doc-beta0002"
        description: "Beta Systems Q1 2026 Investor Report"

  - id: company_gamma
    description: "Gamma Health — Q1 2026 board deck"
    api_key: ${OPENAI_API_KEY}
    docs:
      - id: gamma_board_deck
        locator: "repo:doc-gamma003"
        description: "Gamma Health Q1 2026 Board Deck"

llamaindex:
  model: gpt-4o
  base_url: ${OPENAI_BASE_URL:-https://api.openai.com/v1}
  api_key: ${OPENAI_API_KEY}
  max_tool_calls: 40
  tool_timeout_seconds: 150
  max_agent_iterations: 35
  system_prompt: |
    You are a venture capital analyst extracting KPIs from portfolio company reports.

    ## ANTI-FABRICATION RULE
    ALL values MUST come from your search tool results. Never guess. Return null if not found.

    ## Rules
    1. Return raw full-dollar numbers (9700000 not "$9.7M")
    2. Percentages as whole numbers (92.3 not 0.923)
    3. Every numeric value needs a c_xxxxxxxx citation
    4. Return null for any value not explicitly stated in the document
    5. Negative values are valid (e.g., churn ARR, net cash burn)

computations:
  fields:

    # ── Company Alpha KPIs ─────────────────────────────────────────────────────
    - id: alpha_kpis
      label: "Alpha Inc — Q1 2026 KPIs"
      type: object
      priority: 1
      docset_id: company_alpha
      agents:
        - id: alpha_extract
          prompt: |
            Extract the following KPIs for Alpha Inc from their Q1 2026 board update.

            ## Financial Metrics
            - arr_end: Ending ARR for the most recent period (full dollars)
            - arr_mom_growth_pct: MoM or QoQ ARR growth % (whole number)
            - monthly_burn: Net monthly cash burn (full dollars)
            - cash_on_hand: Current cash balance (full dollars)
            - runway_months: Months of runway at current burn

            ## Operating Metrics
            - headcount: Total employee count
            - customer_count: Total paying customer or logo count
            - nrr: Net Revenue Retention % (if stated)

            ## Qualitative
            - top_milestone: Single most important milestone this quarter (string)
            - top_risk: Single most important risk mentioned (string)

            ## Search Strategy
            1. basic_search: "ARR revenue growth Q1 2026 monthly"
            2. basic_search: "cash burn runway headcount customers"
            3. basic_search: "highlights milestones risks challenges"

            Set to null anything not explicitly stated.
          output_schema:
            type: object
            properties:
              company: { type: string }
              period: { type: string }
              arr_end: { type: number }
              arr_mom_growth_pct: { type: number }
              monthly_burn: { type: number }
              cash_on_hand: { type: number }
              runway_months: { type: number }
              headcount: { type: number }
              customer_count: { type: number }
              nrr: { type: number }
              top_milestone: { type: string }
              top_risk: { type: string }
              citations: { type: array, items: { type: string } }
              notes: { type: string }
      mcp_scope: []
      prompt: "Return Alpha Inc Q1 2026 KPIs with citations."

    # ── Company Beta KPIs ──────────────────────────────────────────────────────
    - id: beta_kpis
      label: "Beta Systems — Q1 2026 KPIs"
      type: object
      priority: 1
      docset_id: company_beta
      agents:
        - id: beta_extract
          prompt: |
            Extract the following KPIs for Beta Systems from their Q1 2026 investor report.

            ## Financial Metrics
            - arr_end: Ending ARR for the most recent period
            - arr_mom_growth_pct: MoM or QoQ ARR growth %
            - monthly_burn: Net monthly cash burn
            - cash_on_hand: Current cash balance
            - runway_months: Months of runway at current burn

            ## Operating Metrics
            - headcount: Total employee count
            - customer_count: Total paying customer count
            - nrr: Net Revenue Retention % (if stated)

            ## Qualitative
            - top_milestone: Most important milestone this quarter
            - top_risk: Most important risk mentioned

            ## Search Strategy
            1. basic_search: "ARR revenue MRR monthly quarterly growth"
            2. basic_search: "cash burn runway balance headcount team"
            3. basic_search: "highlights wins milestones risks concerns"

            Set to null anything not explicitly stated.
          output_schema:
            type: object
            properties:
              company: { type: string }
              period: { type: string }
              arr_end: { type: number }
              arr_mom_growth_pct: { type: number }
              monthly_burn: { type: number }
              cash_on_hand: { type: number }
              runway_months: { type: number }
              headcount: { type: number }
              customer_count: { type: number }
              nrr: { type: number }
              top_milestone: { type: string }
              top_risk: { type: string }
              citations: { type: array, items: { type: string } }
              notes: { type: string }
      mcp_scope: []
      prompt: "Return Beta Systems Q1 2026 KPIs with citations."

    # ── Company Gamma KPIs ─────────────────────────────────────────────────────
    - id: gamma_kpis
      label: "Gamma Health — Q1 2026 KPIs"
      type: object
      priority: 1
      docset_id: company_gamma
      agents:
        - id: gamma_extract
          prompt: |
            Extract KPIs for Gamma Health from their Q1 2026 board deck.

            ## Financial Metrics
            - arr_end or revenue_end: ARR or revenue for the quarter
            - arr_mom_growth_pct: MoM or QoQ growth %
            - monthly_burn: Net monthly cash burn
            - cash_on_hand: Current cash balance
            - runway_months: Months of runway

            ## Operating Metrics
            - headcount: Total employee count
            - customer_count: Patients, clients, or paying customer count
            - nrr: Net Revenue Retention % (if applicable)

            ## Qualitative
            - top_milestone: Most important milestone
            - top_risk: Most important risk

            ## Search Strategy
            1. basic_search: "revenue ARR growth quarterly monthly"
            2. basic_search: "cash burn runway balance employees headcount"
            3. basic_search: "milestones highlights risks"

            Set to null anything not explicitly stated.
          output_schema:
            type: object
            properties:
              company: { type: string }
              period: { type: string }
              arr_end: { type: number }
              arr_mom_growth_pct: { type: number }
              monthly_burn: { type: number }
              cash_on_hand: { type: number }
              runway_months: { type: number }
              headcount: { type: number }
              customer_count: { type: number }
              nrr: { type: number }
              top_milestone: { type: string }
              top_risk: { type: string }
              citations: { type: array, items: { type: string } }
              notes: { type: string }
      mcp_scope: []
      prompt: "Return Gamma Health Q1 2026 KPIs with citations."

    # ── Cross-Portfolio Summary (depends on all 3 companies) ──────────────────
    - id: portfolio_summary
      label: "Cross-Portfolio Summary"
      type: object
      priority: 2
      depends_on: [alpha_kpis, beta_kpis, gamma_kpis]
      agents:
        - id: portfolio_summary_extract
          docset_id: company_alpha   # use any docset — this agent synthesises from context
          prompt: |
            Summarise the overall health of the portfolio based on the individual
            company KPIs available in your context (from depends_on).

            ## What to Produce
            - total_portfolio_arr: Sum of all companies' ARR (null if any are missing)
            - portfolio_avg_runway_months: Average runway across companies
            - healthiest_company: Company name with strongest metrics
            - most_at_risk_company: Company name with lowest runway or highest burn
            - common_themes: 2-3 themes appearing across multiple companies
            - portfolio_outlook: "positive" | "cautious" | "mixed" | "concerning"

            Base your answer entirely on the company KPIs from context.
            If a company KPI is null, exclude it from calculations and note it.
          output_schema:
            type: object
            properties:
              total_portfolio_arr: { type: number }
              portfolio_avg_runway_months: { type: number }
              healthiest_company: { type: string }
              most_at_risk_company: { type: string }
              common_themes: { type: array, items: { type: string } }
              portfolio_outlook: { type: string }
              notes: { type: string }
      mcp_scope: []
      prompt: "Return cross-portfolio summary derived from company KPIs."

  tables:
    # ── Portfolio Dashboard Table ──────────────────────────────────────────────
    - id: portfolio_dashboard
      title: "Portfolio Company Dashboard — Q1 2026"
      priority: 2
      depends_on: [alpha_kpis, beta_kpis, gamma_kpis]
      agents:
        - id: dashboard_build
          docset_id: company_alpha   # placeholder — values come from context
          prompt: |
            Build the portfolio dashboard table from the company KPI results
            available in your context (from depends_on).

            For each of the 3 companies (Alpha Inc, Beta Systems, Gamma Health),
            create one row with:
            - company: Company name
            - arr_end: Ending ARR (full dollars)
            - arr_growth_pct: QoQ ARR growth %
            - monthly_burn: Monthly burn (full dollars)
            - runway_months: Runway in months
            - headcount: Employee count
            - nrr: NRR % (if available)
            - outlook: "green" | "yellow" | "red" based on runway and growth

            Use only values from the company KPIs in context. Set null where missing.
            Return {"rows": [...]} with one object per company.
          output_schema:
            type: object
            properties:
              rows:
                type: array
                items:
                  type: object
                  properties:
                    company: { type: string }
                    arr_end: { type: number }
                    arr_growth_pct: { type: number }
                    monthly_burn: { type: number }
                    runway_months: { type: number }
                    headcount: { type: number }
                    nrr: { type: number }
                    outlook: { type: string }
      mcp_scope: []
      prompt: "Build portfolio dashboard table from company KPIs."

template:
  format: markdown
  sections:
    header: |
      # {{report.name}}
      > **Fund:** {{context.fund_name}} | **As of:** {{context.as_of_date}} | **Period:** {{context.reporting_period}}

  content: |
    {{sections.header}}

    ---

    ## Portfolio Dashboard

    {{table.portfolio_dashboard}}

    **Overall outlook:** {{ field.portfolio_summary.portfolio_outlook | default("—") }}
    **Combined portfolio ARR:** {{ field.portfolio_summary.total_portfolio_arr | currency("$", 0) | default("—") }}
    **Average runway:** {{ field.portfolio_summary.portfolio_avg_runway_months | number(1) | default("—") }} months

    **Common themes:** {{ field.portfolio_summary.common_themes | default("—") }}
    **Healthiest:** {{ field.portfolio_summary.healthiest_company | default("—") }}
    **Most at risk:** {{ field.portfolio_summary.most_at_risk_company | default("—") }}

    ---

    ## Alpha Inc

    | Metric | Value |
    |--------|-------|
    | ARR | {{ field.alpha_kpis.arr_end | currency("$", 0) | default("—") }} |
    | QoQ Growth | {{ field.alpha_kpis.arr_mom_growth_pct | number(1) | default("—") }}% |
    | Monthly Burn | {{ field.alpha_kpis.monthly_burn | currency("$", 0) | default("—") }}/mo |
    | Cash | {{ field.alpha_kpis.cash_on_hand | currency("$", 0) | default("—") }} |
    | Runway | {{ field.alpha_kpis.runway_months | number(1) | default("—") }} months |
    | Headcount | {{ field.alpha_kpis.headcount | number(0) | default("—") }} |
    | NRR | {{ field.alpha_kpis.nrr | number(1) | default("—") }}% |

    **Milestone:** {{ field.alpha_kpis.top_milestone | default("—") }}
    **Risk:** {{ field.alpha_kpis.top_risk | default("—") }}

    ---

    ## Beta Systems

    | Metric | Value |
    |--------|-------|
    | ARR | {{ field.beta_kpis.arr_end | currency("$", 0) | default("—") }} |
    | QoQ Growth | {{ field.beta_kpis.arr_mom_growth_pct | number(1) | default("—") }}% |
    | Monthly Burn | {{ field.beta_kpis.monthly_burn | currency("$", 0) | default("—") }}/mo |
    | Cash | {{ field.beta_kpis.cash_on_hand | currency("$", 0) | default("—") }} |
    | Runway | {{ field.beta_kpis.runway_months | number(1) | default("—") }} months |
    | Headcount | {{ field.beta_kpis.headcount | number(0) | default("—") }} |
    | NRR | {{ field.beta_kpis.nrr | number(1) | default("—") }}% |

    **Milestone:** {{ field.beta_kpis.top_milestone | default("—") }}
    **Risk:** {{ field.beta_kpis.top_risk | default("—") }}

    ---

    ## Gamma Health

    | Metric | Value |
    |--------|-------|
    | ARR | {{ field.gamma_kpis.arr_end | currency("$", 0) | default("—") }} |
    | QoQ Growth | {{ field.gamma_kpis.arr_mom_growth_pct | number(1) | default("—") }}% |
    | Monthly Burn | {{ field.gamma_kpis.monthly_burn | currency("$", 0) | default("—") }}/mo |
    | Cash | {{ field.gamma_kpis.cash_on_hand | currency("$", 0) | default("—") }} |
    | Runway | {{ field.gamma_kpis.runway_months | number(1) | default("—") }} months |
    | Headcount | {{ field.gamma_kpis.headcount | number(0) | default("—") }} |
    | NRR | {{ field.gamma_kpis.nrr | number(1) | default("—") }}% |

    **Milestone:** {{ field.gamma_kpis.top_milestone | default("—") }}
    **Risk:** {{ field.gamma_kpis.top_risk | default("—") }}

guardrails:
  - id: gr_no_fabrication
    target: computations
    on_fail: warn
    model: gpt-4.1-mini
    api_key: ${OPENAI_API_KEY}
    description: "Detect fabricated or placeholder values"
    prompt: |
      FAIL if you find: citations like "Source A"/"Source B" (real ones use c_xxxxxxxx),
      ARR values that are ALL round multiples of 100000, or retention values below 1.0.
      Return JSON: {"pass": boolean, "issues": []}
      Content: {{content}}

  - id: gr_runway_sanity
    target: computations
    on_fail: warn
    model: gpt-4.1-mini
    api_key: ${OPENAI_API_KEY}
    description: "Runway values must be positive and plausible"
    prompt: |
      Check all runway_months values. FAIL if any are negative or greater than 120 months.
      Check all monthly_burn values. FAIL if any are positive (burn should be stored as positive number,
      but flag if burn is implausibly large compared to cash, e.g., burn > cash).
      Return JSON: {"pass": boolean, "issues": []}
      Content: {{content}}

execution:
  retries:
    max_attempts: 3
    backoff_seconds: 2.0
  output:
    directory: ./output
    timestamp_suffix: true
    include_final_report: true
    include_computed_json: true
    include_evidence: true
    include_guardrails: true