Calculate financing exit waterfall

What it's for

Lets a CEO, founder or investor see who receives what at low, medium and high exits after seniority, participation caps and rational conversion—not just nominal ownership percentages.

What you give it

Every tool is a pure function: it computes from what you pass it and reads nothing on its own. Inputs split into evidence you supply from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "holding_period_years": 5,
  "scenarios": [
    {
      "debt_and_senior_claims": 0,
      "evidence_verified": true,
      "gross_exit_value": 80,
      "id": "downside",
      "probability": 0.4,
      "transaction_costs": 0
    },
    {
      "debt_and_senior_claims": 0,
      "evidence_verified": true,
      "gross_exit_value": 1000,
      "id": "upside",
      "probability": 0.6,
      "transaction_costs": 0
    }
  ],
  "security_classes": [
    {
      "as_converted_shares": 100,
      "evidence_verified": true,
      "id": "founder-common",
      "invested_capital": 0,
      "liquidation_preference_multiple": 0,
      "participating": false,
      "participation_cap_multiple": 0,
      "security_type": "common",
      "seniority_rank": 100,
      "stakeholder_group": "founders"
    },
    {
      "as_converted_shares": 25,
      "evidence_verified": true,
      "id": "series-a-preferred",
      "invested_capital": 100,
      "liquidation_preference_multiple": 1,
      "participating": false,
      "participation_cap_multiple": 0,
      "security_type": "preferred",
      "seniority_rank": 1,
      "stakeholder_group": "series-a-investors"

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": {
    "conversion_tolerance": 0,
    "holding_period_years": 5,
    "maximum_reconciliation_error": 0.01,
    "maximum_unverified_probability_mass": 0,
    "tail_probability": 0.4
  },
  "decision": "financing_exit_waterfall_supported",
  "expected_payout_by_stakeholder_group": {
    "founders": 480,
    "series-a-investors": 152
  },
  "failed_gates": [],
  "guardrails": [
    "The solver pays non-converted preferences in seniority order, shares equal-rank shortfalls pro rata, distributes residual by as-converted shares, iteratively enforces participation caps, and accepts only conversion choices with no profitable unilateral class deviation.",
    "All scenarios must share one cap table, security-rights version, exit-value perimeter and debt/cost basis. The output is scenario economics, not legal, tax, securities, valuation, fiduciary or investment advice.",
    "Stakeholder-group payouts are aggregate contractual outcomes. They must not be used to evaluate employee performance, infer intent, or assign personal credit for company value."
  ],
  "method": "seniority_participation_cap_and_endogenous_conversion_waterfall",
  "scenario_diagnostics": [
    {
      "converted_security_class_ids": [
        "series-a-preferred"
      ],
      "distributable_equity_value": 1000,
      "maximum_unilateral_conversion_regret": 0,
      "payout_reconciliation_error": 0,
      "probability": 0.6,
      "pure_conversion_equilibrium": true,
      "scenario_id": "upside"
    },
    {
      "converted_security_class_ids": [],
      "distributable_equity_value": 80,
      "maximum_unilateral_conversion_regret": 0,
      "payout_reconciliation_error": 0,
      "probability": 0.4,
      "pure_conversion_equilibrium": true,
      "scenario_id": "downside"
    }
  ],
  "security_class_diagnostics": [
    {

How it works

Statistical audit & measurement — Check whether a number is fit to decide on: coverage, timing, reconciliation, and the gaps a dashboard hides.

1. 1 Freeze a counsel-approved security-rights version and coherent exit scenarios, subtract debt/senior claims and transaction costs once, and enumerate every preferred-class conversion combination within the bounded class count.
2. 2 For each combination, pay non-converted preferences by seniority, allocate equal-rank shortfalls pro rata, distribute residual by as-converted shares and iteratively redistribute amounts above participating caps.
3. 3 Accept a conversion profile only when no preferred class improves by unilaterally toggling conversion; then reconcile total payouts and aggregate expected/lower-tail payouts, conversion, MOIC, annualized return and capital-loss probability by class and stakeholder group.

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 20 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": "calculate a financing exit waterfall across" }
  → finds "calculate_financing_exit_waterfall"

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

gitrevio_capability_run
  { "capability_id": "calculate_financing_exit_waterfall", "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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