For companies and their sponsors
AI Diligence Readiness
A buyer's AI diligence will ask for evidence you may not currently produce. This engagement runs those questions early, while there is still time to fix the answer rather than negotiate around it.
Why it matters
Unanswerable questions become price adjustments
In our experience of how these processes run, the damage rarely comes from a bad answer. It comes from no answer: a claim that cannot be reproduced, spend that cannot be attributed, a policy that cannot be evidenced.
- We run the same evidence requests a buyer-side AI diligence team would issue.
- Every gap is written as the finding a buyer would write, so you see the language before they use it.
- Remediation is sequenced by what is achievable before the process opens versus what must be disclosed.
Output
A readiness register
Sample readiness register
- HIGH
Evaluation evidence
No frozen held-out set; accuracy claims cannot be reproduced by a third party.
- MODERATE
Inference economics
Provider spend is not mapped to product surface, so gross margin by feature is unknown.
- HIGH
Model dependency
No abstraction layer or tested fallback across providers.
- MODERATE
AI governance
Data handling policy exists but is not evidenced in logging configuration.
- LOW
Product adoption data
Feature-level telemetry is complete and exportable by cohort.
Going into a process with an AI story?
We will tell you which parts of it survive evidence review and which parts need work first.