AI data quality audit
A review of the source material feeding your AI system, with a focus on what will hurt retrieval or answer quality.
Services
AI data quality work for teams shipping real systems.
We work with teams that need a clearer picture of the content feeding their AI systems. The point is to make the corpus easier to trust.
Services
What we offer
We keep the work practical and specific.
RAG readiness review
A practical check that shows whether a document set is ready to be embedded or whether it needs cleanup first.
Cleanup guidance
A clear list of what to retire, rewrite, merge, or keep so the team can improve the corpus without guesswork.
How it works
What the engagement looks like
We keep the process direct so teams can decide quickly what needs cleanup and what can stay in place.
Start
Look at the source set
We start with the actual content the team uses today, not a theoretical future state.
Review
Call out the risks
We identify the material most likely to create bad retrieval, confusion, or inconsistent answers.
Finish
Hand back a plan
You get a straightforward set of next steps and, if needed, a repeatable review process for future updates.