RAG and Knowledge Systems
AI that answers from your own data, accurately
Generic AI guesses. A RAG system answers from the truth. We build retrieval pipelines over your documents and data so your team and customers get accurate, sourced answers instead of confident fiction.
Book a build call ↗What you can do with it
Internal knowledge assistant
One interface over wikis, Slack, Drive, and tickets, cutting time lost to search by up to 35%.
Support over your docs
Deflect routine tickets with answers grounded in your live help center and policies.
Contract and policy Q&A
Ask plain-language questions across contracts and compliance docs, cited back to the exact clause.
Research and analyst assistant
Synthesize across internal reports and data into sourced briefs analysts can verify.
Onboarding and enablement
Let new hires self-serve answers instead of interrupting senior staff.
Sales and RevOps knowledge layer
Reps query pricing, battlecards, and past proposals grounded in approved content.
What we build
Grounded in your data
Retrieval over your docs, policies, and systems so answers come from your source of truth.
Evaluated for accuracy
We build eval harnesses and citations so quality is measured, not assumed.
Private and secure
Your data stays yours, with access control and secure infrastructure.
Tools and stack we use
- Pinecone
- pgvector
- Qdrant
- Weaviate
- LlamaIndex
- LangChain
- hybrid search
- Cohere Rerank
- BGE
- OpenAI embeddings
- Ragas
- LangSmith
- Arize Phoenix
Enterprises are shifting from a model-centric to a data-centric posture, grounding answers in their own verifiable data instead of trusting raw output. The discipline now called context engineering enforces quality at retrieval time, with teams holding targets like retrieval precision above 85% and faithfulness above 90%. Grounding plus evals turns proprietary data into a citable, measured asset.