Operations · ANALYTICS
An AI-readiness audit of your data in five days
5d
Most AI projects fail on the data underneath them, not the model on top. This audit samples each source for gaps, drift and identifier mess, then says plainly what is usable now and what has to be fixed first.
The process
Map the sources
Every system holding customer, transaction or content data listed with its owner, its API, its update cadence and its retention policy.
Sample and profile
A statistical sample per table profiled for null rates, type inconsistency, duplicate keys and unexpected value distributions.
Test identity and drift
Whether a customer can be joined across systems at all, and whether the same field has changed meaning over the last two years.
Rate each use case
Candidate AI use cases scored against the data they actually require — ready, ready after a named fix, or not supported by this data.
Sequence the remediation
Fixes ordered by what unblocks the most valuable use case first, each with an owner and a re-test that proves it landed.
What we need from you
- Read access to each candidate data source
- A shortlist of AI use cases you are considering
- Two hours with whoever owns each system
What you get
- Source inventory with data-quality profile
- Identity and drift findings
- Use-case readiness rating with a sequenced remediation plan
Timeline
Five working days for the audit itself.