Forecast cash burn uncertainty

What it's for

Shows CEOs and investors when cash risk appears across plausible operating plans and how much rescue capital restores the required reserve—not just a single average runway date.

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.

{
  "maximum_reserve_breach_probability": 0.15,
  "minimum_cash_reserve": 200,
  "scenarios": [
    {
      "id": "downside",
      "net_cash_flows": [
        -180,
        -180,
        -160,
        -150,
        -140,
        -130
      ],
      "probability": 0.2
    },
    {
      "id": "base",
      "net_cash_flows": [
        -120,
        -110,
        -100,
        -80,
        -60,
        -40
      ],
      "probability": 0.5
    },
    {
      "id": "upside",
      "net_cash_flows": [
        -100,
        -70,
        -30,
        10,
        40,
        70
      ],
      "probability": 0.3
    }
  ],
  "starting_cash": 1000
}

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": [
    "Net cash-flow paths are aligned joint scenarios including payroll, vendors, taxes, financing, working capital, and committed investment consistently.",
    "Probabilities and cash reserve were governed before inspecting the result; scenario frequency is not automatically probability.",
    "The forecast is not a solvency opinion and excludes cash sources or obligations absent from the submitted paths."
  ],
  "configuration": {
    "confidence_level": 0.9,
    "maximum_reserve_breach_probability": 0.15,
    "period_count": 6,
    "scenario_count": 3
  },
  "decision": "cash_reserve_breach_risk_material",
  "method": "aligned_scenario_cash_burn_uncertainty_v1",
  "period_breach_probability": [
    {
      "period": 1,
      "probability": 0
    },
    {
      "period": 2,
      "probability": 0
    },
    {
      "period": 3,
      "probability": 0
    },
    {
      "period": 4,
      "probability": 0
    },
    {
      "period": 5,
      "probability": 0.2
    },
    {
      "period": 6,
      "probability": 0.2
    }
  ],
  "scenario_diagnostics": [
    {
      "ending_cash": 60,
      "first_reserve_breach_period": 5,

How it works

Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.

1. 1 Freeze starting available cash, reserve definition, cadence/horizon, complete net cash-flow paths, aligned scenario probabilities, and accepted breach probability.
2. 2 Cumulate every joint path, locate its first reserve breach and minimum cash, calculate rescue capital, then aggregate cumulative breach probability at every period.
3. 3 Report ending-cash intervals, expected and maximum rescue capital, and abstain from solvency language when obligations or financing sources are outside the submitted paths.

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": "forecast aligned cash paths into reservebreach" }
  → finds "forecast_cash_burn_uncertainty"

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

gitrevio_capability_run
  { "capability_id": "forecast_cash_burn_uncertainty", "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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