Capabilities
RAG and Knowledge Systems
We build systems that answer from your data, with citations and permissions, so users can trust the answer and see where it came from. Retrieval quality is treated as an engineering problem, not a prompt.
01
Problems we solve
- Answers need to come from private or domain data, not the model's general knowledge.
- Users cannot trust an answer they cannot trace back to a source.
- Different users should only see what they are permitted to see.
02
What's included
- Ingestion pipelines
- Chunking and indexing
- Retrieval design
- Graph and vector retrieval
- Permissions
- Citations
- Evaluation and observability
03
How we approach it
- 01
Ingest
Build reliable pipelines for the sources that matter.
- 02
Retrieve
Design chunking, indexing, and graph or vector retrieval for the domain.
- 03
Ground
Enforce permissions and attach citations to every claim.
- 04
Evaluate
Measure grounded-answer quality and watch it in production.
04
Representative technologies
Vector databasesGraph databasesLlamaIndexEmbeddingsHybrid retrievalEvaluation tooling
05
Frequently asked
- How do you stop the system from making things up?
- We ground answers in retrieved sources, attach citations, and evaluate whether responses are actually supported by those sources.
- Can it respect our access controls?
- Yes. Permissions are enforced at retrieval so users only ever see answers drawn from content they are allowed to access.
- Do we need a graph database or is vector search enough?
- It depends on the data and questions. We use vector, graph, or hybrid retrieval based on what the domain actually needs.
Have a project that needs RAG and Knowledge Systems?
Tell us what you are building. We will show you what it takes to ship it to production, and reply within one business day.