Audit financing term sheet integrity

Audit startup financing terms as exact share, price, proceeds and ownership identities: include pre-money option-pool increases and converting instruments in the pricing denominator, keep secondary purchases out of company cash and post-money share creation, reconcile primary issuance, post-money equity value and reported investor ownership, and retain evidence failures and impossible fees or secondary sales.

What it's for

Shows founders and investors the real price, cash and dilution behind headline pre-money/post-money terms—including the option-pool shuffle and secondary that ordinary dashboards omit.

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.

Field Type Role Required
max_detail_rows integer ≥ 1, ≤ 500 Numerical control Optional
maximum_absolute_reconciliation_error number ≥ 0 Your calibration Optional
maximum_relative_reconciliation_error number ≥ 0, ≤ 1 Your calibration Optional
term_sheets array of objects (17 fields) ≥ 1 item Evidence Yes

Each term_sheets record

Field Type Required
converting_instrument_shares number (≥ 0) Yes
evidence_verified boolean Yes
gross_primary_proceeds number (≥ 0) Yes
gross_secondary_proceeds number (≥ 0) Yes
id string (non-empty) Yes
option_pool_increase_shares number (≥ 0) Yes
pre_money_equity_value number (> 0) Yes
pre_money_fully_diluted_shares number (> 0) Yes
reported_company_net_cash_proceeds number Yes
reported_new_investor_ownership_fraction number (≥ 0, ≤ 1) Yes
reported_new_primary_shares number (≥ 0) Yes
reported_post_money_equity_value number (≥ 0) Yes
reported_post_money_fully_diluted_shares number (> 0) Yes
reported_price_per_share number (> 0) Yes
reported_secondary_shares_purchased number (≥ 0) Yes
term_sheet_id string (non-empty) Yes
transaction_fees number (≥ 0) Yes
Example input
{
  "term_sheets": [
    {
      "converting_instrument_shares": 10,
      "evidence_verified": true,
      "gross_primary_proceeds": 300,
      "gross_secondary_proceeds": 100,
      "id": "series-a-row",
      "option_pool_increase_shares": 10,
      "pre_money_equity_value": 1200,
      "pre_money_fully_diluted_shares": 100,
      "reported_company_net_cash_proceeds": 280,
      "reported_new_investor_ownership_fraction": 0.2666666667,
      "reported_new_primary_shares": 30,
      "reported_post_money_equity_value": 1500,
      "reported_post_money_fully_diluted_shares": 150,
      "reported_price_per_share": 10,
      "reported_secondary_shares_purchased": 10,
      "term_sheet_id": "series-a",
      "transaction_fees": 20
    }
  ]
}

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.

Example output
{
  "configuration": {
    "maximum_absolute_reconciliation_error": 0.01,
    "maximum_relative_reconciliation_error": 0.001
  },
  "decision": "financing_term_sheet_integrity_supported",
  "guardrails": [
    "The pricing denominator explicitly includes the pre-money option-pool increase and converting instruments. Secondary proceeds transfer existing shares but do not fund the company or increase post-money shares.",
    "This audit reconciles declared arithmetic and evidence; it is not legal, tax, securities, valuation or fairness advice. SAFEs, notes, warrants and anti-dilution provisions must first be converted into finance-and-counsel-approved as-converted shares.",
    "A discrepancy diagnoses a term-sheet model, never an individual's intent or conduct. Do not infer deception, misconduct or fiduciary breach from arithmetic alone."
  ],
  "method": "financing_sources_shares_price_ownership_identity_audit",
  "summary": {
    "action_required_term_sheet_count": 0,
    "evidence_failure_count": 0,
    "supported_term_sheet_count": 1,
    "term_sheet_count": 1
  },
  "term_sheet_diagnostics": [
    {
      "computed_company_net_cash_proceeds": 280,
      "computed_new_investor_ownership_fraction": 0.2667,
      "computed_new_primary_shares": 30,
      "computed_post_money_equity_value": 1500,
      "computed_post_money_fully_diluted_shares": 150,
      "computed_price_per_share": 10,
      "computed_pricing_fully_diluted_shares": 120,
      "computed_secondary_shares_purchased": 10,
      "decision": "financing_term_sheet_integrity_supported",
      "failed_checks": [],
      "option_pool_and_conversion_pre_money_dilution_fraction": 0.1333,
      "secondary_purchase_fraction_of_post_money_shares": 0.0667,
      "term_sheet_id": "series-a"
    }
  ],
  "truncation": {
    "term_sheets_omitted": 0
  }
}

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 one finance-and-counsel-approved as-converted cap-table version, then separate pre-money fully diluted shares, pool expansion, converting instruments, primary proceeds, secondary proceeds and transaction fees.
  2. 2 Recompute the pricing denominator and price per share, derive primary and secondary shares, post-money fully diluted shares, net company cash, post-money value and total new-investor ownership under explicit primary-versus-secondary treatment.
  3. 3 Compare every reported term within absolute and relative tolerance; gate evidence, fees above primary proceeds, secondary shares above the pre-money base and ownership above one without attributing intent.

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.

Assumptions & guardrails

  • Metric definitions, weights, aggregate grain, sampling, missingness, dependence, and comparison windows correspond to the management claim being audited.
  • All SAFEs, notes, warrants, anti-dilution adjustments and pool changes have been converted to counsel-approved as-converted shares on the same cap-table version; primary and secondary consideration use the declared price basis; taxes and fees follow the governed perimeter.
  • Association, instability, or measurement quality is not a causal effect and must not be converted directly into an individual employment decision.
  • Arithmetic integrity is not legal, tax, securities, valuation, fiduciary or fairness advice. A discrepancy diagnoses an aggregate financing model and is never evidence of deception, misconduct or intent.

Minimum evidence

  • term_sheets: at least 1 rows/items

How to validate it

Validate on future periods or held-out aggregate units, compare with a simple baseline, and require stability across plausible metric definitions and decision thresholds.

Calibrating it to your org

Same for everyone

The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.

Specific to you

  • immutable counsel-approved as-converted cap-table snapshot joining security conversions, pool changes, primary issuance, secondary transfer and transaction-cost ledger to one signed term-sheet version
  • fully diluted and as-converted definitions, SAFE/note/warrant and anti-dilution treatment, primary/secondary classification, price/currency/tax/fee perimeter, legal version, evidence verification, tolerances and accountable approval

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": "audit startup financing terms as exact" }
  → finds "audit_financing_term_sheet_integrity"

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

gitrevio_capability_run
  { "capability_id": "audit_financing_term_sheet_integrity", "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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