RAG development: AI that answers from your own data
Retrieval-augmented generation gives a language model a reliable memory. We build RAG systems that ingest your documents and data, retrieve the right context, and answer with citations your team can check.
01Is this you?
- People waste hours searching documents, wikis and tickets for answers.
- A chatbot over your docs gives confident but wrong answers.
- Answers must respect who is allowed to see what.
- Your knowledge lives in PDFs, databases and tools that do not talk to each other.
02What we build
Ingestion pipeline
Parsing, cleaning and chunking for PDFs, docs, web pages, tickets and databases, kept in sync.
Retrieval
Embeddings, hybrid search and re-ranking tuned on your own questions.
Grounded answers
Responses built only from retrieved context, with citations back to the source.
Access control
Document-level permissions so users only retrieve what they may see.
Evaluation
A test set of real questions to measure accuracy before and after every change.
03Typical stack
- Python
- pgvector
- PostgreSQL
- Embeddings
- Hybrid search
- Re-ranking
- Next.js
- OpenAI
- Anthropic Claude
See how this comes together in selected work and the engineering approach.
04Questions
What is RAG?
Retrieval-augmented generation: before the model answers, the system searches your own data for relevant passages and gives them to the model, so answers are grounded in your content rather than the model’s memory.
How do you reduce hallucinations?
Better retrieval, answers restricted to retrieved context, citations, refusing when the answer is not in the data, and measuring accuracy on a fixed set of real questions.
What data sources can a RAG system use?
PDFs, Word and Markdown documents, websites, wikis, support tickets, SQL databases and most systems with an API.
Can the data stay private?
Yes. The system can run in your own cloud, and open-source models can be used where data must not leave your environment.
Tell us what you're building.
A short message is enough to start. You'll hear back from Vijay directly at hello@collabwithvijay.com.