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RAG systems on top of your own data
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RAG Systems
Turn your documents, tickets, transcripts and knowledge base into an answer engine — grounded, cited and always up to date.
What you get
- Document ingestion, chunking & embedding pipelines
- Hybrid search (vector + keyword + filters)
- Source-grounded answers with citations
- Auto-refresh on data changes
How I build it
- 1Inventory data sources & access rules
- 2Build ingestion + embedding pipeline
- 3Tune retrieval (chunks, hybrid, rerank)
- 4Ship answer UI + evaluation harness
Retrieval is the product
RAG quality lives or dies on retrieval. A great LLM with mediocre retrieval gives confident wrong answers. We invest 80% of the effort in chunking, hybrid search and reranking — and only 20% on prompts.
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Citations or it didn't happen
Every answer ships with sources users can click. No citations means no trust — and no trust means the feature gets disabled the first time it hallucinates.
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Permissions are non-negotiable
RAG without row-level security is a data leak waiting to happen. We enforce permissions at retrieval time — never trust the LLM to keep secrets it has already seen.
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Tools & stack
Supabase VectorPineconeOpenAI EmbeddingsCohere RerankLangChainLlamaIndex
Typical use cases
- Internal knowledge assistants
- Customer-facing help & docs search
- Compliance & policy Q&A