ExitValue.ai

METHODOLOGY COMPARISON

Why ChatGPT can't value your business

We tested ChatGPT, Claude, and Perplexity on five real-world business valuation questions. They got the methodology wrong on three, the buyer-pool segmentation wrong on five, and the post-2022 multiple compression wrong on two. Here's what each got wrong, and what 25,592 real M&A transactions say instead.

Quick answers

Can ChatGPT value my business?
Directionally, yes. Accurately for your specific buyer pool, no. ChatGPT averages historical training data without distinguishing PE platform vs strategic vs individual buyers (1.5x-2x spread) and uses wrong methodology for industries like dental (collections-based, not EBITDA).
Where does ChatGPT's valuation data come from?
Training cutoff varies by version (typically 12-24 months stale). No live access to current M&A transactions unless using search-augmented mode. Averages across all buyer types, all sizes, all geographies in its training set.
How is ExitValue.ai different?
25,592 real M&A transactions from SEC filings + Capital IQ + press releases, refreshed daily. Methodology is industry-specific (collections for dental, EV/Revenue for SaaS, EV/EBITDA for most services). Output is stratified by your actual size bracket and buyer pool.

Five test cases

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

ChatGPT typical answer

$2-4M (3-5x EBITDA, no buyer-pool nuance, no recurring revenue weighting)

ExitValue.ai engine

$3.1M-$5.2M with explicit buyer-pool segmentation: PE-backed roll-up territory if maintenance-contract mix is 30%+; individual-buyer if project-based.

Why ChatGPT misses it: ChatGPT averages across all training-data ranges (3-5x EBITDA) without distinguishing PE-platform vs individual buyers — actual market spread is 1.5x to 2x for the same business.

Run this with the engine →

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

ChatGPT typical answer

$1.5-2.5M (vague SDE multiple, no DSO context, no collection-multiple methodology)

ExitValue.ai engine

$1.3M-$1.8M (60-80% of TTM collections — the actual methodology dental DSOs use, not generic SDE).

Why ChatGPT misses it: Dentistry uses percent-of-collections, not SDE multiples. ChatGPT's training data conflates the two; gives wrong methodology + wrong number.

Run this with the engine →

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

ChatGPT typical answer

$15-30M (5-10x ARR, generic, no NRR-vs-growth tradeoff)

ExitValue.ai engine

$18M-$28M (6-9x ARR; NRR matters more than growth rate at this size; vertical-SaaS premium applies).

Why ChatGPT misses it: ChatGPT can't access actual recent SaaS multiples (data goes stale fast — 2021 ZIRP-era multiples ≠ 2026); it averages historical ranges that haven't been valid for 2-3 years.

Run this with the engine →

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

ChatGPT typical answer

$15-25M (mid-range EBITDA multiple, no per-OR adjustment, no platform vs single-center distinction)

ExitValue.ai engine

$22M-$32M (9-13x EBITDA; multi-center platforms trade at premium to single-center; payer mix and case-types drive 30% spread within the band).

Why ChatGPT misses it: ASC pricing has bifurcated: PE platform consolidators (Surgery Partners, USPI, SCA, Tenet) pay 11-15x for fee-viable scale. Single-OR or low-payer-mix sites stay at 6-8x. ChatGPT averages, misses the platform premium entirely.

Run this with the engine →

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

ChatGPT typical answer

$4-6M (generic 4-5x EBITDA)

ExitValue.ai engine

$5.4M-$7.2M (6-8x EBITDA; PDGM-era post-2020 multiples; payer mix matters — Medicare-heavy now discounted vs Medicare-Advantage-heavy).

Why ChatGPT misses it: Home health multiples shifted post-PDGM (2020) and again post-COVID. ChatGPT's training data lags 12-24 months; current PDGM-discount baseline differs from historical ranges by 1-2 turns.

Run this with the engine →

The three structural problems with LLM valuations

  1. 1. Methodology blindness. Dental practices trade on percent of collections (60-85%). Vet practices trade on percent of collections too. SaaS trades on EV/Revenue and increasingly NRR-weighted. Insurance agencies trade on multiple of annual commissions. Restaurants on SDE. ChatGPT applies generic EBITDA multiples by default and misses these methodology splits, producing wrong-by-30-50% answers for any industry that doesn't use standard EBITDA pricing.
  2. 2. Training-data lag of 12-24 months. SaaS multiples compressed roughly 50% from 2021 ZIRP peaks to 2024 floor. Home health multiples reset post-PDGM (2020). Healthcare IT re-rated after the 2022 capital-markets shift. ChatGPT's answer for any of these is averaging an obsolete range with a current one and getting both wrong.
  3. 3. No buyer-pool segmentation. A $4M HVAC company sells for 3.2x EBITDA to an individual buyer, 4.1x to a strategic regional add-on, and 5.8x to a PE platform building density. Same company, same financials, $1.5M spread depending on who buys. ChatGPT averages across buyer types instead of pricing the actual pool you'll be sold to.

Get a real valuation in 3 fields

Methodology-aware. Buyer-pool segmented. Refreshed nightly with new SEC EDGAR filings. No training-data lag.

Run a Valuation
Test cases last refreshed: 2026-05-05. Full methodology