Market size (TAM/SAM/SOM)
Does the TAM have a named, dated source? Do top-down and bottom-up reconcile? A TAM defined as "everyone who might conceivably pay" is the single most-mocked slide in venture.
Investors desk-research your deck before they ever meet you — and increasingly run AI diligence tools over it. Every unverified number is a potential veto. This page explains exactly how to fact-check a fundraising deck against primary sources, and where an accountable outside check fits in.
In one line
To verify pitch deck claims, cross-reference every quantified statement — market size, growth rate, churn, savings — against primary sources, grade each one by evidence quality, and rewrite the weak ones with citations before investors do. ThriveFinity does this for you and a named human signs the result.
What gets checked
Diligence is predictable. The same claim families get challenged in the same order — because they are where decks most often fail.
Does the TAM have a named, dated source? Do top-down and bottom-up reconcile? A TAM defined as "everyone who might conceivably pay" is the single most-mocked slide in venture.
ARR vs annualised-last-month vs booked. "Active users" defined daily, weekly, or monthly. Headline metrics get re-derived from cohorts — mismatches end processes.
Is CAC fully loaded (people, agency, tooling)? Which churn figure is inside the LTV? Does the payback claim match the CAC and margin claims on the same slide?
Every "faster / cheaper / only" claim is checked against what the investor already knows about the named competitor. "Category of one" claims attract the hardest questions.
"Studies show", "industry-leading", "up to 3×", "conservative estimate" — unsourced authority phrases are red flags that trigger a deeper dig, not shortcuts past one.
Pick the one an investor would challenge and submit it free. Our AI returns a cited verdict in 1 hour.
Start a Free Pulse →The method
This is the same sequence ThriveFinity's QUAD protocol runs on every paid engagement. You can run steps 1–5 yourself; step 6 is where motivated reasoning makes an outside check worth paying for.
List every assertion an investor could challenge — anything with a number, percentage, or comparator. Most decks contain 15–30. If a slide makes no checkable claim, ask why it is in the deck.
Name the original source, not the blog that quoted it. Record the publication date and sample size. "Gartner 2024" is a citation; "research shows" is a confession.
Rebuild your analyst-report TAM from the bottom up: addressable customers × realistic price. If the two disagree by more than ~2×, an associate with a spreadsheet will find it.
Define metrics the way a sceptic would. ARR or annualised last month? Fully-loaded CAC or media spend only? Which churn input is inside the LTV? Definitions kill more deals than numbers.
For each comparison, draft the named competitor's rebuttal and the partner's follow-up question. If you cannot answer either in two sentences, the claim needs work or removal.
You cannot mark your own homework — founders systematically over-grade their own evidence. An independent verifier with access to the primary record signs a verdict and gives you the citation trail. The difference between steps 1–5 and step 6 is the difference between believing your deck is sound and knowing it is.
The honest comparison
AI research tools are genuinely useful — we use them ourselves, disclosed openly. But there are three things an AI claim checker structurally cannot do, and they are exactly the three things diligence demands.
| Capability | AI Claim Checker | Signed Human Verification |
|---|---|---|
| Breadth of initial research | Useful Fast and wide coverage — genuinely useful for breadth | Verified Uses the same tooling, disclosed openly, then checks against the primary record |
| Holds verdict under pushback | Fails Flips judgment under rebuttal — documented in peer-reviewed research on LLM sycophancy | Holds The verdict does not change because you argued with it |
| Citation reliability | Weak Citation accuracy is LLMs' weakest task family; hallucinated sources occur | Checked Every citation verified against named, datestamped primary sources before signing |
| Accountability for being wrong | None Output disclaimed — nobody signs, nobody is professionally liable | Signed Named verifier signs the verdict and stands behind it professionally |
| Usable in front of investors | Risky An investor can re-run the same tool and receive a different answer | Yes Signed, dated verification document with a full citation audit trail |
| Incentive structure | Biased Optimised for user satisfaction — telling you what you want to hear feels good | Aligned Paid to find what is wrong before the partner meeting does |
Based on peer-reviewed research on LLM sycophancy and judgment-flipping under rebuttal · published findings on AI-assisted decision quality degradation · OpenAI’s own disclosures on Deep Research factual hallucinations. Full references available on request.
No credit card to start. No commitment. Send us one claim and see what we find — most founders are surprised by what comes back.
The other side of the ledger: one wrong claim challenged in the room can cost £40k–£500k+ in a stalled or lost raise. Every tier below is a rounding error against that.
One claim, one rebuttal, one edit. Delivered in 1 hour. No payment required.
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One full asset — deck, LP, or one-pager — verified with sources. AI-generated, unsigned. 2 hours.
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Your full asset, three rebuttals, one rewrite. Named human verifier. 48 hours.
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