AI verification
CrossCheck AI
A second opinion for your AI. CrossCheck takes what one model said and runs a different model over it — Claude Opus, GPT-5, whichever you choose — to confirm the answer is correct and verified.
- Price
- $999
- The principle
- No model marks its own homework
- You choose
- The verifying model, per workflow
- Output
- The claims that do not hold up, surfaced rather than passed through
- Where it sits
- In front of work that carries consequences
What it actually is
Ask a model to check its own answer and it will usually agree with itself. That is not a bug you can prompt your way out of — it is a property of asking the same system the same question twice.
CrossCheck AI takes an output from one model and puts a different model, from a different family, over the top of it. Disagreements surface. Claims that cannot be supported get flagged. What you get back is not a confidence score but a list of the specific things that did not hold up.
Who this is for
Anyone who has moved AI from drafting into a position where being wrong has a consequence:
- Teams extracting numbers that drive payments — a wrong total on an invoice is a real loss, not a typo.
- Professional services where an AI-assisted document goes out under somebody's name and licence.
- Anyone summarising for a decision, where a dropped caveat changes the call.
- Businesses with an AI governance obligation who need a documented check rather than a stated intention.
How it works
- A primary model does the work — extraction, summary, analysis, draft.
- You choose the verifier. A different family, deliberately. Claude Opus checking GPT, or the reverse. Same-family verification is much weaker and the tool does not pretend otherwise.
- The verifier is given the source and the claim, not the reasoning, and asked whether the claim is supported.
- Disagreements surface. You see the specific claim, what the source actually said, and why the verifier objected.
- Your rule decides what happens. Block, flag for review, or log and continue — per workflow, not globally.
Why this is an emerging category worth being early in
Almost every AI governance framework now asks the same question: how do you know the output was right? Most organisations answer with a process diagram and a human in the loop who reviews a sample.
Verification by an independent model is a stronger answer, and it is one of the few parts of AI assurance that can actually be automated. It is also the instrument behind the disagreement-rate research we publish in the AI governance guide — running two models over the same real business documents produces data almost nobody else has.
What determines the cost
Verification means running a second model, so the marginal cost is roughly a second inference per checked item. That is why the rule is per workflow rather than global — you verify the invoice totals, not the internal meeting summary. Per-engine token prices are published at AI engine pricing.
Questions people actually ask
Before you call
Why not just ask the same model to double-check itself?
Because it will mostly agree with itself. The same model, with the same training and the same blind spots, asked the same question, tends to produce the same answer and then endorse it. Independence is the entire mechanism; without a different model there is no check.
Does this make AI output correct?
No, and we would not claim it. It catches disagreement between two independent systems, which catches a meaningful class of error and misses others — notably anything both models get wrong in the same way. It is a control that reduces risk, not a guarantee.
Which models can verify?
You choose, and the point is to pick a different family from the one that produced the output. What matters is independence, not which particular model you trust most.
Does this double our AI cost?
On verified items, roughly. That is why verification is applied per workflow rather than everywhere — you put it in front of the work where being wrong is expensive, and leave it off the rest.
Talk to us about CrossCheck AI
A short call, and a straight answer about whether this fits your business.
