Rank portfolio companies by risk adjusted progress

What it's for

Gives investors a transparent capital-efficiency ranking in which evidence quality and downside change priority instead of rewarding confident milestone narratives.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "companies": [
    {
      "achieved_milestone_weight": 8,
      "additional_capital_required": 100000,
      "cash_consumed": 1000000,
      "evidence_reliability": 0.9,
      "id": "company-a",
      "milestone_value_if_fully_achieved_scenarios": [
        1000000,
        3000000,
        5000000
      ],
      "total_milestone_weight": 10
    },
    {
      "achieved_milestone_weight": 9,
      "additional_capital_required": 300000,
      "cash_consumed": 1500000,
      "evidence_reliability": 0.8,
      "id": "company-b",
      "milestone_value_if_fully_achieved_scenarios": [
        -2000000,
        4000000,
        7000000
      ],
      "total_milestone_weight": 10
    }
  ],
  "minimum_evidence_reliability": 0.5,
  "risk_aversion": 1,
  "scenario_probabilities": [
    0.2,
    0.5,
    0.3
  ],
  "skeptical_prior_progress": 0
}

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "assumptions": [
    "Companies are stage-, window-, currency-, accounting-, milestone-, and scenario-comparable; milestone weights and fully-achieved value are governed before outcomes are inspected.",
    "Evidence reliability is calibrated on resolved comparable claims, weak progress is shrunk to the skeptical prior, and scenario columns describe the same joint future across companies.",
    "The declared ranking formula is evidence-adjusted milestone value minus a CVaR loss penalty per cash consumed; it is not causal attribution, valuation, company quality, an investment recommendation, or a person-level decision."
  ],
  "company_diagnostics": [
    {
      "clears_evidence_gate": true,
      "company_id": "company-a",
      "cvar_net_milestone_value_loss": -620000,
      "decision": "rank_eligible",
      "evidence_adjusted_milestone_progress": 0.72,
      "evidence_reliability": 0.9,
      "expected_net_milestone_value": 2204000,
      "observed_milestone_progress": 0.8,
      "probability_negative_net_milestone_value": 0,
      "rank": 1,
      "risk_adjusted_net_milestone_value": 2824000,
      "risk_adjusted_value_per_cash_consumed": 2.824
    },
    {
      "clears_evidence_gate": true,
      "company_id": "company-b",
      "cvar_net_milestone_value_loss": 1740000,
      "decision": "rank_eligible",
      "evidence_adjusted_milestone_progress": 0.72,
      "evidence_reliability": 0.8,
      "expected_net_milestone_value": 2364000,
      "observed_milestone_progress": 0.9,
      "probability_negative_net_milestone_value": 0.2,
      "rank": 2,
      "risk_adjusted_net_milestone_value": 624000,
      "risk_adjusted_value_per_cash_consumed": 0.416
    }
  ],
  "configuration": {
    "minimum_evidence_reliability": 0.5,
    "risk_aversion": 1,
    "scenario_count": 3,
    "skeptical_prior_progress": 0,
    "tail_probability": 0.1
  },
  "decision": "risk_adjusted_progress_ranking_available",

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 Freeze a stage-comparable cohort, milestone weights, cash and additional-capital perimeter, aligned fully-achieved milestone value scenarios, and reliability estimates calibrated on resolved comparable claims.
2. 2 Shrink observed weighted progress toward the skeptical prior, translate it into net scenario value, subtract additional capital, penalize CVaR loss by the governed risk aversion, and divide by cash consumed.
3. 3 Rank only companies clearing the evidence gate using the declared risk-adjusted value-per-cash formula, showing every component and leaving insufficient-evidence companies unranked.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "rank stagecomparable portfolio companies by evidenceshrunk" }
  → finds "rank_portfolio_companies_by_risk_adjusted_progress"

gitrevio_capability_describe
  { "capability_id": "rank_portfolio_companies_by_risk_adjusted_progress" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "rank_portfolio_companies_by_risk_adjusted_progress", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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