Data Infrastructure
Pipelines, vector stores, and evaluation datasets that keep your models accurate as the business scales.
AI models are only as good as the data feeding them. Scattered sources, stale exports, and no evaluation dataset means model quality degrades silently and no one catches it.
We unify data into a queryable source of truth, build the pipelines that keep it current, and stand up evaluation datasets so quality drift shows up as a metric, not a customer complaint.
- Unifying scattered data sources into a single queryable store your AI systems can actually trust
- Building the daily regression run that catches model quality drift before customers notice
- Standing up a vector store and ingestion pipeline that stays current as your data changes
- Diagnosing whether a quality problem is the model or the pipeline feeding it — usually it's the pipeline
Most quality complaints turn out to be a pipeline problem, not a model problem — we diagnose before we prescribe.
Omos's per-product isolated knowledge bases and Sydence's live studio-data ingestion both run on this discipline — data models designed so retrieval stays fresh as the product moves.
In build / in useOmosTell us what you’re building and we’ll scope where Data Infrastructure fits.