Optimize regime contingent growth capital policy

What it's for

Moves a board from 'grow or cut burn' to an executable playbook: precommit the right product/growth actions, trigger them only in observable revenue regimes, and show ARR upside, liquidity breach and tail-value risk on the same scenarios.

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.

{
  "actions": [
    {
      "budget_cost": 1,
      "capacity_units": 1,
      "dependency_ids": [],
      "eligible_regime_ids": [
        "down",
        "base"
      ],
      "evidence_verified": true,
      "exclusion_ids": [],
      "growth_rate_delta": 0.04,
      "id": "retention",
      "net_cash_burn_rate_delta": 0.01,
      "one_time_commitment_cost": 2,
      "recurring_cost_per_active_period": 1
    },
    {
      "budget_cost": 1,
      "capacity_units": 1,
      "dependency_ids": [],
      "eligible_regime_ids": [
        "base",
        "growth"
      ],
      "evidence_verified": true,
      "exclusion_ids": [],
      "growth_rate_delta": 0.08,
      "id": "growth-investment",
      "net_cash_burn_rate_delta": 0.05,
      "one_time_commitment_cost": 3,
      "recurring_cost_per_active_period": 1
    }
  ],
  "commitment_budget": 2,
  "commitment_capacity_units": 2,
  "horizon_periods": 3,
  "maximum_cvar_shortfall": 1000,
  "minimum_target_probability": 0.5,
  "regimes": [
    {
      "id": "down"
    },

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": "growth_capital_policy_supported",
  "failed_gates": [],
  "guardrails": [
    "One action is chosen per observable revenue regime and reused whenever that regime occurs, preventing scenario-specific hindsight. Commitment resources are charged once for every action enabled anywhere in the policy.",
    "Exact mode certifies the declared finite policy set. Beam mode evaluates coherent scenarios exactly only after additive screening and therefore withholds global optimality; both modes remain conditional on action effects and scenario paths.",
    "Finance owns recurring revenue, cash, terminal-value and risk definitions; product/growth owners validate executable actions and causal effects. The result is not accounting, valuation, financing, investment, solvency, marketing-targeting or personnel advice."
  ],
  "method": "regime_contingent_recurring_revenue_policy_with_cvar_and_liquidity",
  "scenario_diagnostics": [
    {
      "minimum_unrestricted_cash": 65.556,
      "probability": 0.5,
      "scenario_id": "downside",
      "terminal_recurring_revenue": 130.8384,
      "terminal_unrestricted_cash": 65.556,
      "terminal_value": 719.748
    },
    {
      "minimum_unrestricted_cash": 68.2471,
      "probability": 0.5,
      "scenario_id": "upside",
      "terminal_recurring_revenue": 150.516,
      "terminal_unrestricted_cash": 68.2471,
      "terminal_value": 820.8271
    }
  ],
  "selected_policy_by_regime": [
    {
      "action_id": "growth-investment",
      "regime_id": "base"
    },
    {
      "action_id": "retention",
      "regime_id": "down"
    },
    {
      "action_id": "growth-investment",
      "regime_id": "growth"
    }
  ],
  "solver": {
    "globally_optimal": true,
    "mode": "exact_enumeration",

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 observable regime IDs, coherent probability-weighted regime paths with base growth/burn, and evidence-backed actions whose effects, one-time/recurring costs, resources and relations are executable before outcomes are known.
2. 2 Enumerate one passive-or-action choice per regime inside the exact boundary; otherwise use deterministic additive screening and a disclosed beam. Charge each enabled action's commitment once even when reused across regimes.
3. 3 Propagate ARR and unrestricted cash on every common scenario, calculate target and liquidity probability, terminal value, exact weighted CVaR shortfall and risk-adjusted objective, then select only a feasible policy and report value versus the all-passive baseline.

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 one action for each observable" }
  → finds "optimize_regime_contingent_growth_capital_policy"

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

gitrevio_capability_run
  { "capability_id": "optimize_regime_contingent_growth_capital_policy", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

Related tools

See every tool in Customer, revenue & pricing →