Optimize enterprise technology capital plan

What it's for

Gives CEOs, CTOs, boards, and investors an adaptive technology capital plan that preserves money until real evidence arrives and quantifies the value over committing the whole portfolio today.

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.

{
  "follow_on_capital_budget_by_signal": {
    "adverse": 6,
    "favorable": 6
  },
  "follow_on_implementation_capacity_by_signal": {
    "adverse": 1,
    "favorable": 1
  },
  "initial_capital_budget": 6,
  "initial_implementation_capacity": 1,
  "initiatives": [
    {
      "dependency_ids": [],
      "eligible_follow_on_scenarios": [
        true,
        true
      ],
      "eligible_initial": true,
      "exclusion_ids": [],
      "follow_on_investment_cost": 7,
      "gross_value_scenarios": [
        10,
        10
      ],
      "id": "foundation",
      "implementation_capacity_units": 1,
      "initial_investment_cost": 5
    },
    {
      "dependency_ids": [],
      "eligible_follow_on_scenarios": [
        false,
        true
      ],
      "eligible_initial": true,
      "exclusion_ids": [],
      "follow_on_investment_cost": 6,
      "gross_value_scenarios": [
        0,
        20
      ],
      "id": "growth-option",
      "implementation_capacity_units": 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.

{
  "decision": "approve_staged_signal_contingent_capital_plan",
  "failed_gates": [],
  "guardrails": [
    "Follow-on actions are constant for all futures sharing the same submitted signal and eligibility; hidden future outcomes cannot choose capital inside a signal cell.",
    "Gross values must be incremental, mutually reconciled, finance-approved, and comparable across initial and follow-on timing; activity, code volume, executive enthusiasm, or prior spend is not economic value.",
    "The exact certificate covers the submitted two-stage initiatives, dependencies, exclusions, budgets, capacity, signals, values, and scenarios only; omitted liquidity, tax, security, procurement, workforce, delivery, interaction, or financing constraints invalidate promotion.",
    "The output is decision support for accountable executives and investment committees—not autonomous spending, procurement, hiring, firing, vendor action, company ranking, or employee evaluation."
  ],
  "method": "two_stage_signal_contingent_enterprise_capital_plan_v1",
  "policy_by_signal": [
    {
      "follow_on_capacity_used": 0,
      "follow_on_capital_used": 0,
      "follow_on_initiative_ids": [],
      "signal_id": "adverse",
      "signal_probability": 0.5
    },
    {
      "follow_on_capacity_used": 1,
      "follow_on_capital_used": 6,
      "follow_on_initiative_ids": [
        "growth-option"
      ],
      "signal_id": "favorable",
      "signal_probability": 0.5
    }
  ],
  "scenario_diagnostics": [
    {
      "policy_npv": 5,
      "probability": 0.5,
      "scenario_id": "adverse",
      "selected_initiative_ids": [
        "foundation"
      ],
      "signal_id": "adverse"
    },
    {
      "policy_npv": 19,
      "probability": 0.5,
      "scenario_id": "favorable",
      "selected_initiative_ids": [
        "foundation",

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 incremental initiative values across coherent scenarios, initial/follow-on investment cost, implementation capacity, dependencies, exclusions, timing eligibility, scenario signals, and stage-specific capital/capacity limits.
2. 2 Enumerate three-way initiative states inside the exact boundary or build deterministic value-density supported policies; choose initial actions and then a single identical follow-on action for every future inside each observable signal partition.
3. 3 Replay scenario NPVs, compare the adaptive policy with the best initial one-shot portfolio, calculate signal-contingent option value and CVaR loss, and apply governed value/tail gates before approval.

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": "optimize a twostage enterprise technology portfolio" }
  → finds "optimize_enterprise_technology_capital_plan"

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

gitrevio_capability_run
  { "capability_id": "optimize_enterprise_technology_capital_plan", "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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