Data Pipeline Development for AI Systems

Most "AI data pipeline" work is a script that ran fine in a demo and breaks the first time an upstream source changes shape — and when a downstream number looks wrong, there's no way to trace it back to the row, the feed, or the run that produced it.

We build ingestion with full lineage instead: every canonical row carries its source feed, source document id, run id, and the timestamp it was ingested, so any output can be traced back through every transform to the row that produced it. Deterministic rules run first — most of the pipeline is plain logic, not a model call — and an LLM is invoked only where deterministic rules genuinely can't reach. That model call never sits in the live request path; it runs during scheduled ingestion, where a slow or wrong call can't take down a user-facing request.

Every model call sits behind read-through caching keyed to the prompt version, so changing a prompt busts the cache instead of silently serving stale output, and every call is cost-logged rather than estimated after the invoice arrives. Records are soft-deleted, never hard-deleted, and a new pipeline version ships through a blue/green swap that only cuts over once its output validates against the version it's replacing.