RAG & Knowledge AI
Generic LLMs don't know your business. We build the retrieval layer that does — over your knowledgebase, product docs, ticket history, internal wikis, codebases — with citations, eval, and the infra to keep it fresh as your content evolves.
Concrete outputs — not vibes.
Ingestion pipeline
Connectors to Notion, Confluence, Drive, GitHub, Slack, Zendesk, your KB — chunked and indexed continuously.
Retrieval layer
Hybrid search (BM25 + vector), reranking, scoping by audience and permissions.
Generation with citations
Answers anchored to source passages. Confidence thresholds. Refusal patterns.
Permissions model
User-aware retrieval — answers respect who the user is and what they can access.
Evaluation harness
Golden Q&A pairs, scored against retrieval quality and answer quality separately.
Freshness
Re-index pipelines and stale-content alerts so the knowledge stays current.
Five steps from brief to production.
Content map
Where the knowledge lives, who can see what, how often it changes.
Ingestion
Connectors, parsing strategy, chunking, metadata extraction.
Retrieval
Vector store, hybrid scoring, reranking, permission filters.
Generation
Prompt design, citation format, refusal patterns, fallback behavior.
Eval & ship
Golden set, continuous eval dashboard, then deploy with monitoring.
The tools we typically reach for.
These aren’t prescriptions — we adapt to what you already run. Worth knowing what we’re fluent in.
Questions we get about RAG & Knowledge AI
ChatGPT's file upload works for ~50 pages. Real RAG handles thousands of documents, respects permissions, retrieves only what's relevant, gives citations, and lets you measure quality. It's a system, not a feature.
No. We design permission-aware retrieval — what the user can see in their normal access stays what the AI shows them. Audit logged.
Continuous ingestion with change-detection. New docs flow in, deleted ones flow out, summaries get refreshed on a schedule.
Yes. Every claim links back to a chunk, which links back to the source doc and a passage range.
Less than people think. Postgres + pgvector is plenty for most use cases. Pinecone / Weaviate / Qdrant become worth it past ~10M chunks or specific latency targets.
Yes. We've built RAG layers on Snowflake, BigQuery and Databricks. Architecture changes but the principles don't.
AI Chatbots & Agents
Chatbots and agents grounded in your knowledgebase, CRM and product — not just "chatGPT in a widget."
AI Product Development
Transform your AI idea into a real-world platform — built to ship, scale and integrate.
AI Automation & Workflows
Reduce repetitive work with intelligent automation that operates inside your existing stack.
Send a brief — we reply in 4h.