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Service 02

RAG & Knowledge Systems

Retrieval-augmented generation pipelines that surface the right information at inference time, reliably.

The problem

Off-the-shelf RAG demos work on clean documents. Enterprise document sets are messy — mixed formats, competing revisions, permission boundaries — and generic pipelines hallucinate or leak.

Our approach

We build retrieval pipelines against your real document set: ingest, chunking that respects semantic boundaries, permission-aware indexes, and evaluation datasets you can regression-test against.

Use cases
  • Grounding an internal assistant in policy, compliance, or product documentation that changes often
  • Adding permission-aware retrieval so users only ever see answers sourced from documents they're allowed to read
  • Replacing a brittle keyword search with retrieval that understands intent, not just terms
  • Building the evaluation set that catches retrieval regressions before your users do
Representative stack
pgvector, Pinecone, or Weaviate depending on scaleCustom chunking and ingestion pipelinesPermission-aware indexing tied to your identity providerRegression evaluation harnesses on a held-out document set
How we engage

We start with your messiest real documents, not a clean sample — that's where generic RAG pipelines actually fail.

Where you can see it

Inside Sydence, Omos AI reads live studio data — briefs, standups, project state — to answer questions and draft client updates. It's a retrieval system running against a moving document set in production.

LiveSydence
Have a problem shaped like this?

Tell us what you’re building and we’ll scope where RAG & Knowledge Systems fits.

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