RAG agents
Search and answer over your own knowledge base — internal docs, contracts, transcripts, whatever you've accumulated.
Retrieval-augmented generation done properly is mostly about getting the data layer right. I index your sources, set up the right chunking and embeddings, and put a search interface on top that returns answers grounded in your actual documents — with citations.
- Source ingestion (Notion, Google Drive, SharePoint, S3, custom)
- Vector store setup (Pinecone, Supabase pgvector, or self-hosted)
- Chunking and embedding pipeline tuned to your content
- Search interface — standalone web app, Slack bot, or API
- Permissions: who can search what
Teams sitting on years of documents nobody can find — legal, ops-heavy companies, anyone with thick institutional knowledge.
Companies whose knowledge fits in one Notion page — you don't need RAG yet, you need a better wiki.
Audit & scope
We start with a conversation about what you actually do day-to-day. I map the workflows, identify what's worth automating and what isn't, and write up a scoped proposal with a fixed price.
Design
Architecture, data flow, integration points. You see the full system on paper before I touch any code — so we catch the wrong call cheaply, not after it's built.
Build
I build, iterate, and test against real data from your business. You see progress weekly in a shared environment, not as a single reveal at the end.
Deploy & handoff
We launch together. I document everything in your tools (Notion, your repo, wherever you keep things), and I stay on call for the first few weeks while your team gets comfortable.