Optimize budgeted initiative portfolio

What it's for

Turns a roadmap backlog into an auditable capital-allocation answer for product owners, tech CEOs, and investors, including what cash and scarce skills remain constrained.

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.

{
  "budget": 80,
  "initiatives": [
    {
      "cost": 40,
      "dependencies": [],
      "exclusions": [],
      "id": "platform",
      "resource_requirements": {
        "backend": 3,
        "product": 1
      },
      "scenario_values": [
        40,
        70,
        100
      ]
    },
    {
      "cost": 35,
      "dependencies": [
        "platform"
      ],
      "exclusions": [],
      "id": "product-line",
      "resource_requirements": {
        "backend": 2,
        "product": 3
      },
      "scenario_values": [
        10,
        90,
        150
      ]
    },
    {
      "cost": 55,
      "dependencies": [],
      "exclusions": [
        "platform"
      ],
      "id": "vendor-suite",
      "resource_requirements": {
        "backend": 1,

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": [
    "Scenario values are incremental, mutually comparable, and exclude double-counted dependency value.",
    "Scenario probabilities, costs, budgets, dependencies, and exclusions are organization-owned and point-in-time valid.",
    "Optimality is claimed only when exact enumeration is reported."
  ],
  "decision": "budgeted_initiative_portfolio_supported",
  "method": "budgeted_initiative_portfolio_v1",
  "selected_initiatives": [
    {
      "cost": 40,
      "expected_value": 73,
      "expected_value_per_cost": 1.825,
      "initiative_id": "platform"
    },
    {
      "cost": 35,
      "expected_value": 92,
      "expected_value_per_cost": 2.6286,
      "initiative_id": "product-line"
    }
  ],
  "solver_diagnostics": {
    "candidate_portfolios_evaluated": 4,
    "feasible_candidate_count": 4,
    "optimality_proven": true,
    "scenario_count": 3,
    "solver": "exact_enumeration"
  },
  "summary": {
    "budget_slack": 5,
    "expected_portfolio_value": 165,
    "maximum_scenario_value": 250,
    "minimum_scenario_value": 50,
    "resource_use": {
      "backend": 5,
      "product": 4
    },
    "selected_count": 2,
    "selected_initiative_ids": [
      "platform",
      "product-line"
    ],
    "total_cost": 75

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 point-in-time initiative costs, resource requirements, dependency and exclusion graph, aligned joint scenarios, probabilities, and incremental value definitions.
2. 2 Enumerate every dependency-closed feasible portfolio within the exact boundary, or disclose the bounded dependency-closed greedy candidate search beyond it.
3. 3 Compare expected scenario value with retaining budget, publish resource and cash slack, and withhold an optimality claim unless exact enumeration was used.

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": "select the highest expectedvalue initiative portfolio" }
  → finds "optimize_budgeted_initiative_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_budgeted_initiative_portfolio", "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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