Cross industry

Almost every AI verification company sells to the builder

Hundreds of millions went into AI trustworthiness in a year. Almost all of it is sold to the engineer shipping the feature, not the person trusting it.

Almost every funded company in AI verification sells to the person shipping the AI, not the person receiving the answer. Artificial Reality went looking for something Christine McDannell's clients or Jennifer Kerum's could buy after a confidently wrong answer — a $5 million valuation on $125,000 of net profit, a yes to anchoring in 25 knots — and found one exception in thirteen companies.

Why can't an operator buy this?

Price and packaging. The cheapest published enterprise tier found is Vectara's $100,000 a year. The consumer-adjacent tier of the best-funded evaluation platform, Braintrust, is billed in processed-data gigabytes and scores — units meaningless to a hotel owner. Every product surveyed prices as though you have an engineer and an application, not a question.

Why doesn't hallucination detection help with a general question?

Because nearly all of it verifies an output against source material you supplied. The yacht client supplied nothing. The couple with the $5 million valuation supplied nothing. Groundedness scoring structurally cannot help with a general-purpose assistant answering a general question, which is precisely how all three failures happened.

Which verification products do reach end users?

The vertical ones, at enterprise scale. Anterior sells to health plans, Daloopa to institutional finance, Norm Ai to enterprise compliance. The single genuine small-scale end-user product found — Clearbrief, a Word add-in for litigators — is also, jointly, the least funded company in the survey at roughly $7.5M, with nothing raised in about two years. Clearbrief is why the finding reads "almost every" rather than "every". The market is not rewarding the end-user version.

Where did the money go instead?

Into making the model better rather than warning the user. The largest raises Artificial Reality found adjacent to this category in 2026 were for interpretability research and for pretraining models to be more reliable. Two of the best-known output-checking companies moved toward simulation and synthetic data: Patronus AI now leads with simulated environments, Guardrails AI with a synthetic-data product. Cleanlab was acquired by Handshake AI. And several guardrails companies have been acquired by security vendors, who sell to chief information security officers, not to hotel owners.

What is left for an operator is a tier of unfunded browser extensions and freemium checkers, typically capped at a few dozen verifications a month, with no disclosed team behind them. That is not infrastructure a business can build a workflow on.

Artificial Reality's caveat on its own finding: absence of evidence in a research sweep is weaker than presence of it. A small end-user product could exist below the coverage threshold. What can be said firmly is that no well-funded company is building this at scale, and the reasons are structural rather than accidental.

What can an operator do this week?

Since the product does not exist, these are the practices the operators themselves used.

Put a competent human between the AI and the decision. A restaurant founder's lease review worked because an attorney read the findings; the valuation failed because nobody qualified read the report before the owners believed it.
Ask the AI to show its work, then read the working. Christine McDannell caught the error by looking at how the number was assembled, not by checking the number. A total is unfalsifiable; a method is not.
Never accept a number without a methodology. "What multiple did you apply, and to what base?" would have exposed the $5 million valuation in one question.
Ask the same question again in a fresh session, phrased differently. Cheap, imperfect, and it catches the answers shaped by your framing rather than by the facts.
Treat any answer that needs information the AI cannot have as unreliable by default. The anchoring question needed the seabed, the vessel and the forecast.
For anything with real consequences, get the expert. AI does the volume, the licensed professional does the last mile: three minutes of work that saved one operator $1,000.

AI tools named in this report

Tool	Named by	Verdict	Used for
No product named	Christine McDannell, M&A broker, The Magnolia Firm	Didn't work — summed operating departments as assets	Valuing a business for sale
No product named	Jennifer Kerum, yacht charter	Didn't work — confident answer with no grounding	Anchoring safety in 25 knots of wind

Neither operator named the AI product that produced the wrong answer, and no operator named a verification product. Every company discussed here was identified by Artificial Reality. Inclusion is reporting, not endorsement.

Is the end-user version a bad business or an unbuilt one?

The evidence points at bad business. The one company doing it well is the smallest funded in the survey and has not raised in two years. But the demand is documented, it is cross-industry, and it is the failure operators talk about first. The company best placed to sit between a general-purpose assistant and its user and say "this is probably wrong" is the assistant vendor, which has the least commercial reason to build it.

For builders, the decision is narrower than the market question: pick one of the three failure types and price for the person holding the printout, not the team shipping the feature. For operators, the reviewer inside your own business is the verification layer, and refusing to let an AI answer reach a consequential decision without one is the only structural protection on offer.