How ExitValue.ai estimates business value

The data layer

The transaction database holds 25,592 unique completed M&A deals from 1977 to 2026, deduplicated across multiple source feeds. Every deal is sourced from a licensed historical M&A database (historical backbone), SEC EDGAR (8-K and S-4 filings auto-ingested through a daily pipeline), or verified press releases. The data layer is strictly factual - no estimated, modeled, or imputed values. Estimation only happens in the calculation engine, never in the raw record.

Deals are classified into one of 107 sub-verticals across 17 industries. For each (sub-vertical × size bracket) cell, the engine computes stratified medians, p25, and p75 from the most recent (post-2018) cohort with at least 7 disclosed deals; it falls back to all-time data when the recent cohort is too thin.

Methodology selection

Different industries trade on different valuation conventions. The engine selects the right one per sub-vertical:

• Dental practices, optometry, primary care, veterinary: percent-of-collections methodology, with a practice-based clamp on adjustments
• SaaS, software, fintech, healthtech, cybersecurity: revenue-based methodology, weighted by growth and recurring-revenue mix
• HVAC, plumbing, electrical, MSP, pest control: earnings-multiple methodology with recurring-revenue adjustment
• Restaurants, salons, Main Street services: owner-earnings (SDE) methodology for sub-management-team operators
• Insurance agencies: commission-multiple methodology
• Asset-heavy industrials (manufacturing, distribution): blended asset and earnings methodology

When an earnings-method business has unusually thin margin, the engine auto-switches to revenue methodology with a thin-margin band - this reflects how buyers actually price distressed-margin operators. The switch is suppressed for high-multiple verticals (SaaS, software, cybersecurity) where low margin is normal investment-stage behavior. To see the specific multiple range applied to your business and sub-vertical, run a valuation.

Source blending

The base multiple is a weighted blend of: direct-bracket medians from the transaction database (highest weight when the cell has 7+ deals); published sub-vertical benchmarks; adjacent-bracket medians (one step down for SMB conservatism); fallback medians from the parent industry; and Damodaran-style public-company comps adjusted for size and liquidity. When a registered business-type segmentation applies (DSO vs solo dental, corporate vs independent ASC, growth-stage vs bootstrapped SaaS), the type-specific multiples dominate the blend.

Adjustments (multiplicative)

On top of the base multiple, the engine applies adjustment factors that compound multiplicatively, clamped overall to a defensible range:

• Owner dependency - minimal: +15%, important: -5%, critical: -20% to -30%
• Customer concentration - graduated penalty topping out at -35% for B2B at 100% single-customer
• Recurring revenue mix - super-linear premium curve: 30% recurring → +13%, 70%+ → +25%
• Growth trend - declining: -15%, flat: 0, growing: +5-10%, rapid (30%+ YoY): +20-30%
• Margin quality - premium for sustained 40%+ EBITDA margin
• Years in business - small maturity premium for 10+ year operators
• Deal structure - earnout exposure compresses upfront; clean stock sale neutral

Sanity checks

The output passes through a sanity layer that caps multiples per methodology and per sub-vertical (preventing implausible outputs at margin extremes or in thin-data cells), enforces revenue floors and ceilings, and runs an earnings-vs-revenue cross-check that blends the two methodologies when they diverge beyond a calibrated threshold. Method-specific caps and thresholds are part of the engine's internal calibration and are surfaced on a per-business basis when you run a valuation.

Validation framework

The engine is governed by a layered validation framework that runs on every change and every night:

• Mathematical invariantson engine output: ordering (low <= mid <= high), multiple bounds, methodology-bound enforcement, NaN rejection
• Probe regression suite of operator-curated business profiles with expected output bands; any engine change that pushes a probe out of band is reverted
• Public-company control sampleacross three size bands ($100M-500M, $500M-2B, $2B+) confirming the engine's private-acquisition output sits appropriately above public trading multiples with the expected control premium
• Output-sanity scan walks the engine across margin and revenue axes to detect non-monotonicities and discontinuities before they reach production

Continuous calibration

Four feedback loops keep the engine current with how buyers actually transact, without per-session rebuilds:

• Probe regression suite - operator-curated business profiles with expected output bands, replayed nightly
• Closed-deal feedback - when a deal sourced through the funnel actually closes, the realized sale price feeds back into per-cell calibration
• Quarterly drift detection - every 90 days the engine configuration is checked against the latest population medians per (sub-vertical x bracket); drift exceeding threshold triggers retuning
• Daily EDGAR ingestion- new SEC 8-K and S-4 M&A filings are auto-ingested into the database, refreshing stratified medians nightly