Calculate capital efficiency frontier

What it's for

Shows investors and tech CEOs which companies or initiatives convert comparable deployed capital into realized value near the observed frontier and where gaps warrant investigation.

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.

{
  "minimum_efficiency": 0.8,
  "units": [
    {
      "capital_deployed": 2,
      "id": "company-a",
      "realized_value": 5
    },
    {
      "capital_deployed": 4,
      "id": "company-b",
      "realized_value": 8
    },
    {
      "capital_deployed": 4,
      "id": "company-c",
      "realized_value": 5
    },
    {
      "capital_deployed": 8,
      "id": "company-d",
      "realized_value": 11
    }
  ]
}

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": [
    "Capital and realized value share a comparable horizon, currency, attribution, maturity, and accounting basis.",
    "The concave envelope is a relative benchmark among submitted units, not a causal production function or valuation model.",
    "Value is finance-approved realized value or a separately governed equivalent, never engineering activity volume."
  ],
  "configuration": {
    "frontier_tolerance": 0.001,
    "minimum_efficiency": 0.8,
    "returns_to_scale": "variable_concave_upper_envelope"
  },
  "decision": "capital_efficiency_gaps_material",
  "frontier_segments": [
    {
      "capital_from": 0,
      "capital_to": 2,
      "marginal_value_per_capital": 2.5,
      "value_from": 0,
      "value_to": 5
    },
    {
      "capital_from": 2,
      "capital_to": 4,
      "marginal_value_per_capital": 1.5,
      "value_from": 5,
      "value_to": 8
    },
    {
      "capital_from": 4,
      "capital_to": 8,
      "marginal_value_per_capital": 0.75,
      "value_from": 8,
      "value_to": 11
    }
  ],
  "method": "concave_capital_efficiency_frontier_v1",
  "summary": {
    "frontier_unit_count": 3,
    "median_capital_efficiency": 1,
    "total_value_gap_to_frontier": 3,
    "unit_count": 4,
    "units_clearing_minimum_efficiency": 3
  },
  "truncated_detail_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 comparable units, deployed capital, finance-approved realized value, attribution, maturity, horizon, currency, and accounting basis.
2. 2 Remove capital-dominated points, construct the upper monotone concave envelope from the origin, and interpolate frontier value at every observed capital level.
3. 3 Report relative value-to-frontier efficiency, value gaps, and declining marginal frontier value per capital without translating the benchmark into causal performance.

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": "construct a monotone concave capitaltorealizedvalue envelope" }
  → finds "calculate_capital_efficiency_frontier"

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

gitrevio_capability_run
  { "capability_id": "calculate_capital_efficiency_frontier", "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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