RAG architecture
Chunking, embeddings, retrieval and re-ranking tuned for accuracy on your actual content.
Pc
◢ hire · rag + search
RAG & Vector Search Consultant
I build retrieval-augmented generation (RAG) systems so your AI answers from your data — accurately and with citations. Chunking, embeddings, vector search and evals done right.
- rag consultant
- vector database expert
- pinecone consultant
- chat with your documents developer
- semantic search expert
01 — scope
How I can help
Concrete, hands-on rag & vector search consultant work — scoped to your goals and shipped to production.
Vector database setup
Pinecone (or the right alternative) configured for scale, cost and fast, relevant results.
Grounded answers + citations
Responses that cite their sources and refuse to hallucinate when the answer isn't in your data.
Retrieval evals
A measurement harness so you know retrieval quality is improving, not just changing.
02 — you get
What you walk away with
- A production RAG pipeline over your documents
- Tuned vector index with cost + latency benchmarks
- Answer quality eval suite with citations
- Ingestion pipeline for keeping data fresh
03 — process
How we work together
- 01 Discovery call
A free 30-minute call to map your goals, current stack and where rag support will move the needle fastest.
- 02 Scoped plan
You get a clear, fixed-scope proposal — milestones, timeline and pricing. No vague retainers, no surprises.
- 03 Build & ship
I build in the open with you, shipping working increments weekly so you can use it (and steer it) the whole way.
- 04 Handoff & training
Clean code, docs and a walkthrough so your team owns it. Optional ongoing support if you want me on call.
04 — faq
Common questions
Which vector database should I use?
It depends on scale and budget. Pinecone is a great managed default; for smaller or self-hosted needs I'll recommend a leaner option. I benchmark before committing so you don't overpay.
Will the AI stop making things up?
That's the whole point of RAG done well. I ground answers in your retrieved data with citations and add refusal behaviour, so the model says 'I don't know' instead of inventing an answer.
Can it stay up to date as my data changes?
Yes. I build an ingestion pipeline that re-indexes new and changed content automatically, so your AI always answers from the latest version of your data.
◢ execute
Let's build it together.
Book a free 30-minute discovery call. I'll tell you honestly whether I can help, what it'll take, and what it'll cost.
$ aidevguy --consult --start_