Freelance RAG development
Ground your AI in private documents, knowledge bases and user session context, with retrieval you can trust.
What you get
Retrieval-augmented generation (RAG) lets a model answer from your own data. These are the parts I build, modelled on the architecture behind Memory.
- Hybrid semantic retrieval
- High-recall vector search combined with keyword matching, so the right passage is found even when the wording differs.
- Chunking and re-ranking
- Documents split along their own structure, with retrieved passages re-ranked against the question before the model sees them.
- Session memory
- Multi-turn memory across visits, so the system remembers what a user was working on.
- Anti-hallucination checks
- Verification that an answer is supported by the retrieved sources before it is shown.
How it works
Four moves, from brief to production- i.
Scope
Tell me which documents or knowledge base the AI should answer from. You get a scope and timeline estimate, with a direct reply within 12 hours.
- ii.
Architect
Chunking, embeddings, retrieval and re-ranking are planned around your documents before the first line of code is written.
- iii.
Ship
The retrieval pipeline and the interface around it go live, with streaming answers.
- iv.
Harden
Verification checks, token budgets, observability and model fallbacks keep answers grounded and the system up.
Related work
Built and live todayQuestions people ask
- What is RAG?
- Retrieval-augmented generation (RAG) gives a language model the relevant passages from your own documents at the moment it answers. The model writes the reply from those passages rather than from memory alone, so answers can be grounded in, and traced back to, your data.
- When should I use RAG instead of fine-tuning?
- RAG is usually the better starting point when knowledge changes often or must stay private, because you update the documents instead of retraining a model. Fine-tuning suits changing how a model behaves more than what it knows.
- How do you reduce hallucinations?
- Retrieval brings in the source passages, re-ranking keeps the most relevant ones, and verification checks confirm an answer is supported by those sources before it is shown.
- How long does a RAG build take?
- Typically 2–3 weeks for an enterprise RAG and memory layer, depending on how many document sources are involved and how clean they are.
- Can the system remember earlier conversations?
- Yes. Multi-turn session memory across visits is part of the build, in the way Memory keeps context for each page.
Have documents your AI should answer from?
Use the scope estimator for an instant timeline, or send a brief. You get a direct reply within 12 hours.