AI data quality

We keep bad data out of AI systems.

We review the source material behind RAG, AI search, and internal knowledge systems, point out what is getting in the way, and help teams fix it.

Services

What the service covers

We focus on the content problems that usually show up first when AI systems start giving inconsistent answers.

Audit the corpus

We review the documents, wiki pages, exports, and PDFs feeding your AI system.

Find the weak spots

We flag duplicates, stale material, contradictions, and low-signal content before they hit retrieval.

Turn it into a process

We leave teams with a clear cleanup path and a repeatable way to keep the corpus in shape.

Common problems

What usually goes wrong

These are the issues we see when teams start embedding content without checking the source material first.

Duplicate content

The same fact repeated in too many places pushes weaker answers to the surface.

Stale material

Old policy pages and old docs keep getting embedded long after they should have been retired.

Contradictions

Conflicting source material gives the model too many ways to answer the same question incorrectly.