The operating problem
A specialist product catalog can contain many incomplete records. Processing the entire file blindly makes it difficult to distinguish useful enrichment from malformed output, missing information, or previously completed work.
What we built
We built Python-based enrichment workflows, including a notebook-based CSV pipeline and application-side enrichment tooling. The process asks for structured output and validates the returned record before treating it as usable.
Completed identifiers can be skipped on subsequent runs. Successful outputs, pending items, and errors remain separable, making partial runs and human follow-up practical instead of requiring a full restart.
What the work enables
The tools turn enrichment into a resumable process with reviewable outputs. AI-generated product information is not treated as independently verified fact and still requires appropriate editorial checks.
The valuable automation is the whole reviewable pipeline, not just the model call.