Expertise
RAG & Retrieval Systems
A model that has read your documents still has to find the right passage before it can answer. Retrieval is where accuracy is actually won or lost, which is why this work is mostly chunking, hybrid search, and reranking rather than prompting.
In practice
In practice: answering questions from scattered PDFs, wikis, and internal records, including in places where the data is not allowed to leave the building.
What it gets used for
- Assistants over internal knowledge bases
- Contract and policy analysis
- Product Q&A for customers
- Private AI for sensitive data
Stack
PineconepgvectorHybrid SearchOllamavLLM
See it running
The projects and case studies show this work in production, with the architecture and the results.