AI services
RAG systems & AI search
Retrieval-augmented features for products that need trustworthy answers — not demo chat that invents policy.
100% Job Success · Top Rated · Projects typically start from $1,000
How this engagement works
RAG is only useful if retrieval is honest. I design chunking, hybrid search, citations, and evals so the model answers from your data — with cost controls so you are not paying for noise.
Typical 2–6 weeks depending on corpus size and integrations.
This is a fit if
- SaaS products that need search over docs or tickets
- Catalogs where keyword search fails intent queries
- Support teams that want grounded copilots, not demos
- Teams stuck with a PoC that never survived production
When to hire me for this
- Keyword search fails intent queries across docs or catalogs
- You have a RAG PoC that never survived production cost or quality
- You need citations, hybrid retrieval, and evals — not a demo notebook
01
Corpus
What to index, what to exclude, and how often it refreshes.
02
Retrieve
Hybrid keyword + vectors, with filters you can explain.
03
Generate
Grounded answers, citations, and refusal when evidence is missing.
04
Eval
A small harness so quality does not silently decay after launch.
What you get
- Corpus design and chunking strategy
- Hybrid retrieval (keyword + vectors)
- Grounded generation with citations
- Eval harness and cost controls
Start with a free audit
Send your URL. I reply with blockers and a fixed-price path — typically under 1 hour.
Frequently asked questions
What is RAG and why do I need it?+
RAG (Retrieval-Augmented Generation) combines your own data with AI to provide accurate, contextual answers — reducing hallucinations and irrelevant responses.
Which vector database do you recommend?+
For most Shopify stores, PostgreSQL with pgvector is cost-effective and easy to manage. Pinecone is a good fit for high-scale or multi-tenant applications.
Can RAG work with my existing product catalog?+
Yes. I chunk and embed your catalog, docs, or FAQs into a retrieval pipeline that serves accurate answers from your real data.
How do you handle hallucinations?+
Through grounded retrieval, citation-based responses, and an eval harness that tests answer quality before production deployment.
Related services: AI chatbots · AI & LLM systems · Free audit
Related guides: RAG implementation guide · RAG vs traditional search · pgvector AI search