RAG Systems & AI Knowledge Assistants

What this service covers

Most retrieval projects fail on retrieval, not generation. If the wrong passages come back, no amount of prompt tuning saves the answer, it only makes the model confidently wrong. So the work starts with how documents are parsed, chunked and embedded, and with measuring retrieval quality instead of assuming it.

I build RAG systems over your content: ingestion, vector storage in Supabase, pgvector or Pinecone, semantic search, reranking, and the generation layer on top. Where the product is multi-tenant that also means per-tenant isolation of data and embeddings, so one customer's documents never surface in another customer's answers.

RAG inside a voice agent is a harder problem than RAG in a chat window, and it is the version I most often get asked for. A chat user will wait two seconds for a retrieval round trip. A caller will not. That means aggressive caching, retrieval running in parallel with the model's opening words, and a fallback answer ready for when the search is simply too slow to wait for. See where voice latency actually goes.

On Trainizi this pipeline turned corporate training documents into structured mini-courses for roughly 10,000 learners and about 5,000 generated courses.

What you get

• Document ingestion, parsing and chunking pipeline
• Vector storage and semantic search with reranking
• Retrieval evaluation against a real question set, so quality is a number not an opinion
• Latency-aware retrieval for voice: caching, parallel lookup, and a fallback path
• Multi-tenant isolation of documents and embeddings
• Citations in answers, so users can check where a claim came from