Estimate marginal engineering ROI

What it's for

Shows leaders exactly where another engineering dollar stops earning its hurdle rate, while respecting technical prerequisites instead of ranking isolated feature ideas unrealistically.

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.

{
  "increments": [
    {
      "id": "core-automation",
      "incremental_benefit_scenarios": [
        190,
        160,
        130
      ],
      "incremental_cost": 120
    },
    {
      "id": "advanced-workflows",
      "incremental_benefit_scenarios": [
        180,
        120,
        70
      ],
      "incremental_cost": 100
    },
    {
      "id": "long-tail-customization",
      "incremental_benefit_scenarios": [
        130,
        80,
        40
      ],
      "incremental_cost": 90
    }
  ],
  "maximum_negative_return_probability": 0.2,
  "minimum_marginal_roi": 0.15,
  "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": [
    "Increments are ordered, individually feasible only after predecessors, and their benefits/costs are genuinely incremental without overlap.",
    "Benefit scenarios are finance-approved and aligned joint futures; engineering activity is not monetary benefit.",
    "Stopping at the first failed increment is a governed prefix policy and does not optimize arbitrary non-prefix combinations."
  ],
  "configuration": {
    "maximum_negative_return_probability": 0.2,
    "minimum_marginal_roi": 0.15,
    "prefix_policy": true,
    "scenario_count": 3,
    "tail_probability": 0.1
  },
  "decision": "engineering_investment_prefix_supported",
  "increment_diagnostics": [
    {
      "accepted_in_prefix": true,
      "clears_hurdles": true,
      "cvar_incremental_loss": -10,
      "expected_incremental_benefit": 157,
      "expected_incremental_net_value": 37,
      "increment_id": "core-automation",
      "incremental_cost": 120,
      "marginal_roi": 0.3083,
      "order": 1,
      "probability_negative_return": 0
    },
    {
      "accepted_in_prefix": false,
      "clears_hurdles": false,
      "cvar_incremental_loss": 30,
      "expected_incremental_benefit": 117,
      "expected_incremental_net_value": 17,
      "increment_id": "advanced-workflows",
      "incremental_cost": 100,
      "marginal_roi": 0.17,
      "order": 2,
      "probability_negative_return": 0.3
    },
    {
      "accepted_in_prefix": false,
      "clears_hurdles": false,
      "cvar_incremental_loss": 50,
      "expected_incremental_benefit": 78,

How it works

Statistical audit & measurement — Check whether a number is fit to decide on: coverage, timing, reconciliation, and the gaps a dashboard hides.

1. 1 Freeze the dependency-ordered investment increments, fully incremental costs and finance-approved benefit scenarios, joint scenario probabilities, and marginal hurdle rates before evaluation.
2. 2 For each increment calculate expected net value, marginal ROI, probability of negative return, and tail loss without averaging it into the preceding investment case.
3. 3 Accept only the consecutive prefix that clears every hurdle, expose later economically attractive but dependency-stranded increments, and sensitivity-test the ordering and hurdles before action.

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": "evaluate an ordered engineering investment curve" }
  → finds "estimate_marginal_engineering_roi"

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

gitrevio_capability_run
  { "capability_id": "estimate_marginal_engineering_roi", "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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