Optimize multi period capital allocation

What it's for

Turns a one-year project list into a period-by-period capital plan that cannot spend next quarter's money today or force cash into negative-value work.

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.

{
  "discount_rate_per_period": 0.02,
  "period_budgets": [
    180,
    110,
    50
  ],
  "projects": [
    {
      "id": "platform",
      "period_funding_requirements": [
        80,
        60,
        20
      ],
      "terminal_value_scenarios": [
        350,
        260,
        180
      ],
      "value_realization_period": 3
    },
    {
      "dependency_ids": [
        "platform"
      ],
      "id": "product",
      "period_funding_requirements": [
        100,
        50,
        30
      ],
      "terminal_value_scenarios": [
        500,
        320,
        160
      ],
      "value_realization_period": 3
    },
    {
      "id": "campaign",
      "period_funding_requirements": [
        100,
        100,

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 project is an indivisible approved funding schedule with incremental terminal value, aligned scenarios, dependency/exclusion feasibility, and no double-counted portfolio benefit.",
    "Budgets represent genuinely available period capital after reserves and commitments; unused capital remains available rather than being forced into a negative-value project.",
    "The greedy fallback is a scalable heuristic without an optimality certificate; large material plans require an approved integer solver or shortlisted exact rerun."
  ],
  "configuration": {
    "discount_rate_per_period": 0.02,
    "period_count": 3,
    "scenario_count": 3
  },
  "decision": "multi_period_capital_allocation_supported",
  "method": "dependency_aware_multi_period_capital_allocation_v1",
  "period_allocation": [
    {
      "allocated": 180,
      "budget": 180,
      "period": 1,
      "remaining": 0,
      "utilization": 1
    },
    {
      "allocated": 110,
      "budget": 110,
      "period": 2,
      "remaining": 0,
      "utilization": 1
    },
    {
      "allocated": 50,
      "budget": 50,
      "period": 3,
      "remaining": 0,
      "utilization": 1
    }
  ],
  "project_diagnostics": [
    {
      "dependency_ids": [
        "platform"
      ],
      "discounted_expected_net_value": 115.868,
      "discounted_expected_value": 290.2353,
      "discounted_funding_cost": 174.3673,

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 approved indivisible funding schedules, aligned incremental terminal-value scenarios, realization timing, period capital available after reserves, dependencies, exclusions, and discount convention.
2. 2 Discount funding and expected value consistently, test every feasible project subset through the exact boundary, and retain capital whenever no positive expected-net-value set clears every period constraint.
3. 3 Above the exact boundary disclose the dependency-closure value-density heuristic and require an approved integer optimizer or shortlisted exact rerun before material allocation.

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": "allocate indivisible project funding schedules across" }
  → finds "optimize_multi_period_capital_allocation"

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

gitrevio_capability_run
  { "capability_id": "optimize_multi_period_capital_allocation", "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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