Data readiness
Most AI projects stall on the data underneath them. We audit where it lives, how it moves and what state it is in, then clean it up before anything is built.
What the work covers
Pipeline audit
Where the data comes from, which systems copy it, and where it breaks or goes stale.
Clean-up
Duplicates, conflicting records and missing fields fixed at the source, not patched downstream.
Access map
Who can read what today, so the AI built later sees only what it should.
What you get
- A written audit of the pipelines and their problems.
- A cleaned, documented data set ready to build on.
- A plan for what to build first, and what has to wait.
Common questions
What is a data readiness assessment?
An audit of the pipelines and a clean-up of the data, before anything is built on it.
Do you fix bad data or just report it?
Duplicates, conflicting records and missing fields fixed at the source, not patched downstream.
How does this control what AI can see later?
Who can read what today, so the AI built later sees only what it should.
What do we get at the end?
A written audit of the pipelines and their problems, a cleaned and documented data set ready to build on, and a plan for what to build first.
Start with an audit
A first conversation about the data you have, the work you want AI to do, and what has to be true before it can.