When this is the right call
These are the sentences that usually precede the enquiry:
- There is pressure to "use AI" with no agreed definition of what it would improve.
- Documentation is permanently out of date because writing it competes with delivery.
- Data quality assessment lives in one person’s head and stops when they are on leave.
- Someone has started pasting production data into a chat window and nobody has said anything.
What is different afterwards
- A small number of workflows where AI measurably saves time, and a stated list of where it must not be used.
- Quality assessment that any team member can run and get comparable results from.
- Documentation that stays current because generating it is part of the pipeline, not a separate task.
- A clear boundary on what data may go where, agreed before anyone improvises one.
What you receive
- A repeatable, documented AI-assisted data-quality assessment workflow your team can run unaided.
- Model and column documentation generation wired into the existing dbt project.
- Triage assistance for incidents: candidate causes ranked from lineage and recent changes, for a human to confirm.
- A written boundary policy: which data may be sent to which service, and which decisions stay human.
- An honest list of the use cases that were considered and rejected, with the reason for each.
How it runs
- Find the repetitive workWhere the team actually spends time on structured, reviewable tasks. That is where this pays, and it is rarely where the initial enthusiasm points.
- Draw the boundary firstWhat data may leave your environment, and which outputs a human must confirm. Agreed before anything is built, not after an incident.
- Build one workflow properlyOne task, documented well enough that a teammate can run it and get the same result. A workflow only one person can operate has not been productized.
- Verify against known answersRun it against cases where the correct outcome is already known, so its reliability is measured rather than assumed.
- Hand it overDocumentation and a working session, so the workflow belongs to the team rather than to whoever introduced it.
Who this suits
- Data teams whose quality assessment or documentation depends on one person.
- Teams under pressure to adopt AI who want to do it somewhere it will actually survive contact with production.
- Organisations that need the boundary on data handling written down before someone sets one by accident.
Engagement: Short scoping conversation, then a fixed-scope build of one or two workflows.