Optimize stage gate funding

What it's for

Lets investors and CEOs fund learning in stages, preserving the option to stop after weak evidence instead of committing the entire project budget upfront.

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.

{
  "initial_gate_budget": 100,
  "maximum_expected_follow_on_capital": 300,
  "projects": [
    {
      "id": "new-product",
      "stages": [
        {
          "conditional_success_probability": 0.7,
          "funding_required": 50,
          "id": "discovery"
        },
        {
          "conditional_success_probability": 0.6,
          "funding_required": 150,
          "id": "pilot"
        },
        {
          "conditional_success_probability": 0.7,
          "funding_required": 300,
          "id": "scale"
        }
      ],
      "terminal_value_scenarios": [
        1200,
        800,
        300
      ]
    }
  ],
  "scenario_probabilities": [
    0.2,
    0.5,
    0.3
  ]
}

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": [
    "Stage success is conditionally calibrated, gates reveal decision-relevant evidence before later funding, failure stops subsequent spend, and terminal values share one incremental counterfactual.",
    "Expected follow-on capital is a planning constraint rather than a guarantee of peak liquidity sufficiency; correlated project outcomes and funding calls require separate joint-path stress testing.",
    "Backward induction values the represented abandonment option only; a greedy large-portfolio result has no global optimality certificate."
  ],
  "configuration": {
    "initial_gate_budget": 100,
    "maximum_expected_follow_on_capital": 300,
    "scenario_count": 3
  },
  "decision": "retain_capital_no_positive_stage_gate_portfolio",
  "method": "backward_induction_stage_gate_portfolio_funding_v1",
  "project_diagnostics": [
    {
      "expected_follow_on_capital": 0,
      "expected_npv": 0,
      "expected_terminal_value": 0,
      "funded_stage_ids": [],
      "gate_values": [
        -50,
        -23.4,
        211
      ],
      "initial_gate_cost": 0,
      "project_id": "new-product",
      "selected": false
    }
  ],
  "solver_diagnostics": {
    "candidate_count": 1,
    "exact_project_limit": 20,
    "optimality_proven": true,
    "solver": "exact_enumeration"
  },
  "summary": {
    "expected_follow_on_capital": 0,
    "initial_gate_capital": 0,
    "portfolio_expected_npv": 0,
    "selected_project_count": 0,
    "selected_project_ids": []
  },
  "truncated_project_count": 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 sequential evidence gates, conditional success probabilities, funding calls, terminal incremental value paths, initial budget, and expected follow-on capital appetite.
2. 2 Work backward from terminal value, fund a reached stage only when its cost is below probability-weighted continuation value, and stop all later spend after an uneconomic gate.
3. 3 Optimize the resulting project options exactly through the governed boundary, disclose the capital-density fallback beyond it, and stress joint liquidity/correlation separately.

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": "value project continuation and abandonment by" }
  → finds "optimize_stage_gate_funding"

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

gitrevio_capability_run
  { "capability_id": "optimize_stage_gate_funding", "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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