Services
RAG & Knowledge Intelligence
Search and answers over your own documents, measured before and after every change.
Discuss this serviceBuilding intelligent knowledge systems that make organizational and external data searchable, retrievable, and accessible through natural language.
Capabilities
- Retrieval-Augmented Generation
- Vector database architecture
- Semantic search
- Embedding pipelines
- Knowledge assistants
- Retrieval evaluation and optimization
- RAG performance audits
Problems we’re brought in for
- A team that cannot find answers in its own documents.
- A RAG prototype that returns confident, wrong answers.
- No way to tell whether a change to chunking, embeddings or prompts made retrieval better or worse.
How we approach it
- 1
An audit starts with a question set drawn from real usage and a measurement of what the current system actually retrieves.
- 2
We then work through ingestion, chunking, embeddings, retrieval and ranking one variable at a time, re-measuring after each change.
- 3
You receive the evaluation harness along with the system, so quality can be checked whenever the data or model changes.