Forecast fundraising close and runway

What it's for

Connects the fundraising CRM to runway in one answer: how likely target cash is to arrive, whether the company stays above its buffer while waiting, and what tail rescue capital closes the gap.

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.

{
  "current_opportunities": [
    {
      "age_periods": 1,
      "current_stage": "diligence",
      "evidence_verified": true,
      "id": "fund-alpha",
      "potential_primary_proceeds": 600
    },
    {
      "age_periods": 0,
      "current_stage": "term_sheet",
      "evidence_verified": true,
      "id": "fund-beta",
      "potential_primary_proceeds": 400
    }
  ],
  "historical_stage_episodes": [
    {
      "duration_periods": 1,
      "evidence_verified": true,
      "id": "contacted-0",
      "outcome": "lost",
      "stage": "contacted"
    },
    {
      "duration_periods": 2,
      "evidence_verified": true,
      "id": "contacted-1",
      "outcome": "censored",
      "stage": "contacted"
    },
    {
      "duration_periods": 3,
      "evidence_verified": true,
      "id": "contacted-2",
      "outcome": "advanced",
      "stage": "contacted"
    },
    {
      "duration_periods": 4,
      "evidence_verified": true,
      "id": "contacted-3",
      "outcome": "advanced",

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.

{
  "configuration": {
    "age_bucket_boundaries": [
      1,
      3,
      6
    ],
    "horizon_periods": 6,
    "period_cash_timing": "burn_then_financing_close",
    "simulations": 500,
    "tail_probability": 0.1
  },
  "decision": "fundraising_or_runway_action_required",
  "failed_gates": [
    "minimum_liquidity_probability"
  ],
  "guardrails": [
    "Stage transitions and losses are competing risks with age-bucket hazards shrunk toward each stage's own base rate; censored episodes contribute exposure without fabricated terminal outcomes.",
    "Each simulation draws one common market scenario for every live opportunity, preserving shared fundraising conditions. The stated cash convention subtracts period burn before adding closes.",
    "Memory within an age bucket, investor dependence beyond the common scenario, negotiation, legal terms, dilution, bridge availability, and omitted cash obligations remain outside this model. This is a conditional planning forecast, not a financing guarantee or solvency opinion."
  ],
  "method": "empirical_bayes_age_state_competing_risks_fundraising_simulation",
  "opportunity_diagnostics": [
    {
      "age_periods": 0,
      "current_stage": "term_sheet",
      "opportunity_id": "fund-beta",
      "potential_primary_proceeds": 400,
      "simulated_close_probability": 0.684
    },
    {
      "age_periods": 1,
      "current_stage": "diligence",
      "opportunity_id": "fund-alpha",
      "potential_primary_proceeds": 600,
      "simulated_close_probability": 0.336
    }
  ],
  "stage_hazard_diagnostics": [
    {
      "age_bucket": 0,
      "loss_events": 1,
      "period_exposure": 20,
      "posterior_loss_hazard": 0.06,

How it works

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

1. 1 Convert historical fundraising histories into stage episodes and person-period exposure, preserving right censoring; estimate age-bucket advance/close and loss hazards with stage-local empirical-Bayes shrinkage.
2. 2 For each simulation draw one coherent market regime shared by every opportunity, adjust the competing hazards, advance or absorb each live opportunity, subtract burn before adding realized primary proceeds, and retain the full cash shortfall path.
3. 3 Report opportunity close probabilities, target-capital probability, liquidity survival, their joint probability, expected and tail rescue capital, and abstain when evidence or stage exposure is inadequate.

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 whether enough primary capital closes" }
  → finds "forecast_fundraising_close_and_runway"

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

gitrevio_capability_run
  { "capability_id": "forecast_fundraising_close_and_runway", "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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