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AI Readiness and Data Audit
A fixed-price assessment of whether the AI work you are considering will actually function on the data and systems you have, before you spend a quarter finding out.
- Fixed price
- 2 weeks
- A straight answer
- Either way
Built with
- Claude
- Python
- PostgreSQL
- Evaluation harnesses
The problem
The expensive failure in AI is rarely technical. It is spending two quarters building the wrong candidate, on data that was never going to support it, discovered at the point where the budget is gone and someone has to explain it. The questions that decide this are answerable in weeks rather than quarters, but only if somebody asks them first.
How we would sequence it
- 01Inventory the data these features would depend on, and assess it honestly for coverage, freshness and permissions
- 02Cost each candidate at your real volume, with latency and accuracy estimated up front
- 03Prototype only the single riskiest assumption, far enough to answer it
- 04Rank the candidates by risk rather than by ease, and name the ones not worth building
- 05Write the first fortnight of whichever wins in enough detail that any team could start
This solution describes our method for a class of problem. It is not a record of a completed client engagement, and the figures above are scope estimates rather than measured results.
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