Calculate venture milestone efficiency

What it's for

Lets investors and tech CEOs see which ventures convert cash into credible milestone de-risking and value without collapsing unlike companies into a vanity score.

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": [
    {
      "cash_consumed": 1000000,
      "evidence_reliability": 0.9,
      "id": "company-a",
      "observed_milestone_progress": 0.9,
      "planned_cash_consumption": 1200000,
      "planned_milestone_progress": 0.8,
      "value_uplift_scenarios": [
        2000000,
        4000000,
        6000000
      ]
    },
    {
      "cash_consumed": 4000000,
      "evidence_reliability": 0.8,
      "id": "company-b",
      "observed_milestone_progress": 0.8,
      "planned_cash_consumption": 3000000,
      "planned_milestone_progress": 0.8,
      "value_uplift_scenarios": [
        -1000000,
        1000000,
        2000000
      ]
    }
  ],
  "minimum_evidence_reliability": 0.5,
  "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 use the same currency, accounting perimeter, elapsed decision window, stage-appropriate milestone taxonomy, outcome maturity, and scenario identity; otherwise cross-company comparison is invalid.",
    "Evidence reliability is calibrated on resolved comparable milestone claims, and the skeptical prior—not activity volume or stakeholder confidence—governs shrinkage of weak progress evidence.",
    "The Pareto frontier preserves milestone efficiency, expected value per cash, and downside probability as separate axes; it is not a universal company ranking, investment recommendation, causal attribution, or valuation opinion."
  ],
  "company_diagnostics": [
    {
      "cash_consumption_ratio": 0.8333,
      "clears_evidence_gate": true,
      "company_id": "company-a",
      "cvar_value_uplift_loss": -2000000,
      "decision": "efficiency_frontier",
      "evidence_adjusted_milestone_progress": 0.81,
      "evidence_reliability": 0.9,
      "expected_value_uplift": 4200000,
      "expected_value_uplift_per_cash": 4.2,
      "milestone_efficiency_index": 1.215,
      "on_efficiency_frontier": true,
      "probability_negative_value_uplift": 0,
      "progress_attainment_ratio": 1.0125
    },
    {
      "cash_consumption_ratio": 1.3333,
      "clears_evidence_gate": true,
      "company_id": "company-b",
      "cvar_value_uplift_loss": 1000000,
      "decision": "dominated_on_declared_axes",
      "evidence_adjusted_milestone_progress": 0.64,
      "evidence_reliability": 0.8,
      "expected_value_uplift": 900000,
      "expected_value_uplift_per_cash": 0.225,
      "milestone_efficiency_index": 0.6,
      "on_efficiency_frontier": false,
      "probability_negative_value_uplift": 0.2,
      "progress_attainment_ratio": 0.8
    }
  ],
  "configuration": {
    "downside_probability_epsilon": 0,
    "efficiency_epsilon": 0,
    "minimum_evidence_reliability": 0.5,
    "scenario_count": 3,
    "skeptical_prior_progress": 0,

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 company cohort, milestone taxonomy, elapsed window, cash perimeter, plans, scenario identity, and reliability estimates calibrated on resolved milestone claims.
2. 2 Shrink observed progress toward a skeptical prior, calculate progress-versus-plan per cash-versus-plan, propagate value-uplift scenarios into value per cash and tail loss, and exclude evidence below the governed gate.
3. 3 Construct the practical Pareto frontier over milestone efficiency, expected value per cash, and downside probability; present frontier membership without converting it into an investment or company-quality ranking.

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": "compare evidenceadjusted milestone progress and scenario" }
  → finds "calculate_venture_milestone_efficiency"

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

gitrevio_capability_run
  { "capability_id": "calculate_venture_milestone_efficiency", "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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