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How do you crash-test a board pack with several AI models before the meeting?
An AI crash-test runs your board pack through several cross-checked models before the meeting, to surface weak signals, internal contradictions and points that won't hold in the room. AI sharpens the reading; the decision stays yours.
The crash-test is an outside reading of a decision document — board pack, quarterly memo, executive presentation, investment file, sale teaser — submitted to several cross-checked AI models before it goes to committee. It surfaces what an internal re-read no longer sees, because it already knows the answer.
What a crash-test surfaces
A team that produced a document no longer reads it: it recognises it. The crash-test restores a fresh eye, without fatigue or internal politics.
- Internal contradictions — an assumption made on page 6 that no longer holds on page 30.
- Weak signals — what is half-said, or not said at all.
- The points that won't hold — the figures, shortcuts and blind spots a demanding board member will catch in the room.
The method, in three steps
The document is submitted to several AI models — Claude, ChatGPT, Gemini and others — with adversarial reading instructions. The feedback is cross-checked: what recurs across models deserves attention; what appears only once is set aside.
The output is not an AI report. It is a short note, read and signed by a human, pointing to the three to five items to address before the meeting. Returned within 72 hours.
What AI does not replace
AI does not decide. It knows neither your sector better than you, nor the people around the table. It speeds up detection; the trade-offs, the ranking of priorities and the accountability stay yours. That is the WYP rule: AI to sharpen the decision, never to make it.
FAQ
Can a sale memo or information memorandum be reviewed this way?
Yes. The crash-test applies to any decision document: board pack, quarterly memo, investment file, teaser or sale memorandum. The point is the same: have the file read adversarially before buyers or board members do.
Why several AI models and not just one?
A single model has blind spots and can hallucinate. Cross-checking several models does not remove error, but it surfaces what is robust: a point raised by several independent models is worth pausing on.
How fast is the feedback?
A useful return is possible within 72 hours for a committee file, as a short, actionable note rather than a technical report.
Does the AI make the decision for us?
No. The crash-test surfaces signals; it does not rule. The decision, the ranking of priorities and the signature remain the leader's and the committee's.
Is the confidentiality of our documents preserved?
It is a precondition. Sensitive documents are handled in a controlled setting, without exposing your data or feeding it into the training of a public model.