Optimize roadmap under resource substitution

What it's for

Shows how a roadmap changes when validated cross-capability staffing options exist, without assuming every engineer or vendor is freely interchangeable.

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.

{
  "initiatives": [
    {
      "id": "platform",
      "implementation_cost": 50,
      "incremental_value_scenarios": [
        300,
        220,
        150
      ],
      "resource_plans": [
        {
          "id": "native",
          "resource_demands": {
            "backend": 10
          }
        },
        {
          "id": "validated-substitute",
          "resource_demands": {
            "backend": 2,
            "frontend": 8
          }
        }
      ]
    },
    {
      "id": "product",
      "implementation_cost": 40,
      "incremental_value_scenarios": [
        250,
        180,
        100
      ],
      "resource_plans": [
        {
          "id": "native",
          "resource_demands": {
            "backend": 8,
            "frontend": 4
          }
        }
      ]
    }

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": [
    "Each resource plan is a separately validated executable native/substitute staffing design with quality, coordination, ramp, and productivity losses already reflected in demands and value.",
    "Initiative values are incremental, comparable, and independent of the chosen resource plan unless plan-specific value effects are already encoded in the approved case.",
    "The fallback is a deterministic heuristic without an optimality certificate; unlisted substitution is infeasible rather than assumed from job-title similarity."
  ],
  "decision": "resource_substitution_roadmap_supported",
  "initiative_diagnostics": [
    {
      "available_resource_plan_ids": [
        "native",
        "validated-substitute"
      ],
      "expected_net_value": 165,
      "initiative_id": "platform",
      "selected": true,
      "selected_resource_plan_id": "validated-substitute"
    },
    {
      "available_resource_plan_ids": [
        "native"
      ],
      "expected_net_value": 130,
      "initiative_id": "product",
      "selected": true,
      "selected_resource_plan_id": "native"
    }
  ],
  "method": "validated_resource_plan_substitution_roadmap_optimization_v1",
  "resource_allocation": [
    {
      "allocated": 10,
      "capacity": 12,
      "remaining": 2,
      "resource": "backend"
    },
    {
      "allocated": 12,
      "capacity": 12,
      "remaining": 0,
      "resource": "frontend"
    }
  ],
  "solver_diagnostics": {

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 capability-specific capacity and separately validated execution plans whose demands already reflect ramp, coordination, quality, and substitution productivity effects.
2. 2 Calculate expected net initiative value, enumerate selection/plan states through the governed state boundary, and enforce every resource capacity while choosing at most one plan per initiative.
3. 3 Disclose the value-density fallback above the exact boundary, reject unlisted substitution as infeasible, and require an approved integer solver or shortlisted exact rerun for material roadmaps.

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": "choose a valuemaximizing roadmap and one" }
  → finds "optimize_roadmap_under_resource_substitution"

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

gitrevio_capability_run
  { "capability_id": "optimize_roadmap_under_resource_substitution", "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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