case study · 2024
Agentic RAG Chatbot
A local agent-driven RAG system that combines LLM capabilities with tool integrations to deliver accurate, contextual AI responses without relying on external APIs.
What needed solving
Most RAG chatbots are black boxes running on third-party APIs — you can't see why an answer was produced, and sensitive content has to leave your infrastructure to get an answer at all.
How I built it
Built a 5-step reasoning pipeline where each stage is inspectable, so any answer can be traced back through retrieval and reasoning.
Ran the full stack locally with no external API dependency, keeping content on-premise.
Integrated tools so the agent could act on retrieved context rather than only summarising it.
Where it landed
A fully local, auditable RAG assistant — the same architecture I now use as the foundation for client chatbot work where data residency or explainability matters.
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Hiring for this kind of work?
I'm open to full-time AI Engineer roles. Grab my resume, or email me and I'll walk you through how this was built.