ExitValue.ai

METHODOLOGY COMPARISON

Why ChatGPT can't value your business

ChatGPT averages a fixed snapshot of training data. ExitValue.ai prices your business off 25,592 real M&A transactions, routes to the correct methodology across 107 sub-verticals, and pulls named recent comparable dealsfrom SEC EDGAR filings refreshed daily. Below: five test cases where ChatGPT structurally can't match the rigor our engine applies.

Quick answers

Can ChatGPT value my business?
Directionally, yes. For your specific buyer pool, no. ChatGPT averages historical training data without distinguishing PE platform vs strategic vs individual buyers, and defaults to a generic earnings basis for industries that actually trade on other bases (dental on collections, insurance on commissions, SaaS on a revenue basis).
Where does ChatGPT's valuation data come from?
A fixed training-data snapshot with a hard cutoff. No live access to current M&A transactions. Cannot pull named recent comps from disclosed deals. Averages across all buyer types, all sizes, all geographies in the snapshot.
How is ExitValue.ai different?
25,592 real M&A transactions from SEC filings + licensed historical M&A data + press releases, refreshed daily. Methodology routes by industry across 107 sub-verticals. Output includes named comparable transactions, a Sellability Score with dollar-lift per driver, and a written analysis in an actual M&A advisor voice.

Five test cases

We're comparing how the answer is generated, not the dollar output. Run any of these in our engine to see the data-backed range for your specific business.

HVAC business with $4M revenue and $580K EBITDA in Phoenix, AZ

How ChatGPT answers

Generic single-number guess. No buyer-pool segmentation, no recurring-revenue weighting, no distinction between PE-platform consolidator vs individual-buyer pricing.

How ExitValue.ai answers

Data-backed range from disclosed home-services M&A in the last 18 months. Explicit buyer-pool segmentation: PE-backed roll-up tier when maintenance-contract mix is dense; individual-buyer tier when project-based. Named recent comps from Wrench Group, Apex Service Partners, and Home Service Holdings.

Why ChatGPT misses it: ChatGPT can't access recently disclosed home-services M&A. It averages across all buyer types without distinguishing PE-platform vs individual buyers - and the actual market spread between those two buyer pools is substantial for the same business.

Run this with the engine →

Dental practice with $2.2M annual collections, 1 dentist + 2 hygienists

How ChatGPT answers

Applies a generic SDE multiple. Doesn't distinguish DSO consolidator pricing from private-buyer pricing, doesn't route to the percent-of-collections methodology brokers and DSOs actually use.

How ExitValue.ai answers

Routes dental practices to the percent-of-collections methodology that dental DSOs and brokers actually use. Comp set includes MB2, Smile Brands, Aspen, Heartland, and PDS pricing for tuck-in vs platform deals.

Why ChatGPT misses it: Dentistry trades on percent-of-collections, not generic SDE or EBITDA. ChatGPT's training data conflates the methodologies; our engine routes dental to the correct method automatically.

Run this with the engine →

B2B SaaS with $3M ARR, 110% NRR, 30% YoY growth

How ChatGPT answers

Generic ARR multiple. No NRR-vs-growth tradeoff, no vertical-SaaS premium, no distinction between bootstrapped vs VC-backed pricing, averages obsolete ZIRP-era cohorts with current ones.

How ExitValue.ai answers

Sub-vertical SaaS methodology weighted by NRR over raw growth at this revenue scale; vertical-SaaS premium applied when applicable. Comp set pulls from Vista, Thoma Bravo, Insight, and K1 platform tuck-ins - post-2022 cohort only.

Why ChatGPT misses it: SaaS pricing reset materially after 2022. ChatGPT averages obsolete 2020-2021 ZIRP-era ranges with current ones. We pull comps from the post-2022 cohort only.

Run this with the engine →

ASC (ambulatory surgery center) with $8M revenue, $2.4M EBITDA, 4 ORs

How ChatGPT answers

Mid-range EBITDA average. No per-OR adjustment, no platform-vs-single-center distinction, no payer-mix or case-type variance.

How ExitValue.ai answers

Routes to ASC-specific methodology with platform-vs-single-center bifurcation. Payer mix and case types drive variance within the range. Comp set: Surgery Partners, USPI, SCA, and Tenet platform-tier prints.

Why ChatGPT misses it: ASC pricing has bifurcated: PE platform consolidators pay materially more than the long-tail of single-OR or low-payer-mix sites. ChatGPT averages, misses the platform premium entirely.

Run this with the engine →

Home health agency with $5M revenue, 18% EBITDA margin, Medicare-heavy

How ChatGPT answers

Generic EBITDA multiple across all home-health cohorts. Averages pre- and post-PDGM (2020) deals together, doesn't account for payer-mix repricing.

How ExitValue.ai answers

Post-PDGM regime cohort only. Payer-mix-aware (Medicare-heavy vs Medicare-Advantage-heavy now priced differently). Comp set pulled from post-2020 disclosed home-health deals only.

Why ChatGPT misses it: Home health pricing shifted post-PDGM (2020) and again post-COVID. ChatGPT averages across pre- and post-regime cohorts. Our engine filters to the current-regime comp set automatically.

Run this with the engine →

What you get on /results that ChatGPT structurally cannot produce

Sellability Score

0-100 score with a 5-driver breakdown: recurring revenue, owner independence, customer diversification, growth profile, operating margin. Each driver shows the dollar liftavailable if you move it to best-in-class. ChatGPT doesn't compute this.

Named recent comparables

Actual recent disclosed deals from your sub-vertical: target, acquirer, year, deal value. Pulled from SEC EDGAR filings refreshed daily. ChatGPT cannot pull named recent comps - its data has a fixed cutoff and no live filing access.

Sub-vertical methodology routing

107 sub-verticals, each routed to the methodology actual buyers and brokers use: collections for dental + vet, a revenue basis for SaaS, an earnings basis for most services, blended for hybrid, asset-based for distressed. ChatGPT defaults to a generic earnings basis across the board.

Operator-voice narrative

Written analysis in an actual M&A advisor voice - names your weakest Sellability lever, ties it to the dollar-lift available, references your specific comp set. Not generic LLM filler.

Sample: the analysis you get on every /results page

Example narrative for: HVAC business · $4.2M revenue · $580K EBITDA · Phoenix, AZ

Your $4.2M revenue, $580K EBITDA HVAC business in Phoenixsits squarely in the "PE-platform-adjacent" band for the home-services consolidation wave. Your placement reflects premium pricing relative to the individual-buyer baseline but a step below where active consolidators like Wrench Group, Apex Service Partners, and Home Service Holdings transact platform tuck-ins. The spread is driven by your maintenance-contract density.

What pushes you toward the platform end: Phoenix is a top-5 target metro for HVAC roll-up activity (sustained growth + AC-replacement cycle), and your revenue scale crosses the threshold where strategic buyers - not just individuals on SBA financing - actually participate. If you have recurring maintenance contracts representing a material share of revenue, you're likely already on at least one platform consolidator's target list. That tier prices materially above the individual-buyer comp set.

What keeps you off the top of the range:at $580K EBITDA you are below the typical $1M+ EBITDA platform-tier threshold most consolidators apply, so you're either an add-on for an existing platform (lower pricing than the platform itself sold at) or a buy-out candidate for a strategic regional. Either way, demonstrating a path to $1M+ EBITDA in 12-18 months - through technician hiring, additional truck capacity, or a small add-on acquisition - would shift you from add-on pricing to platform-adjacent pricing.

Generated per-business from your specific Sellability drivers, adjustment factors, and comp set. Not template text. Different per-business.

Want one for your business? Run a valuation →

The three structural advantages our engine has

  1. 1. A real M&A transaction database, not training data. 25,592 disclosed deals from a licensed historical M&A database, SEC EDGAR 8-K + S-4 filings, and verified press releases. Refreshed daily as new filings hit EDGAR. ChatGPT works off a fixed training-data snapshot with a hard cutoff - it cannot see what closed last week.
  2. 2. Methodology routing across 107 sub-verticals. Dental + vet trade on percent of collections. SaaS on a revenue basis with NRR weighting. Insurance agencies on a multiple of annual commissions. Restaurants on SDE. Healthcare services on an earnings basis with payer-mix adjustments. Our engine routes each business to the methodology its actual buyers use. ChatGPT defaults to a generic earnings basis and gets it wrong on anything that doesn't use that framework.
  3. 3. Named comparables + Sellability driver analysis. Every /results page shows actual recent disclosed deals from your sub-vertical (target, acquirer, year, value) plus a Sellability Score with dollar-lift per driver. ChatGPT cannot pull named recent comps from a private database and cannot run driver-level lift math against your inputs.

Get a real valuation in about 2 minutes

Methodology-aware. Named comps from your sub-vertical. Sellability Score with driver lift. Refreshed nightly with new SEC EDGAR filings.

See your business's range
Full methodologyIndustry benchmarksRecent dealsIndustry hubs