LLM & Generative AI

Most LLM projects look great in a demo and fall apart in production. Wrong answers slip through, latency spikes under load, and nobody has a reliable way to measure quality. I build systems with evaluation built in from day one — so you know it works before it ships.

What I help with

RAG pipelines

Grounded answers over your own knowledge base — with citations, access control, and measurable quality. Full stack: ingestion, chunking, retrieval, re-ranking, and an eval harness that gates deployments on quality metrics.

Agents & automation

Multi-agent systems for document processing, information extraction, ticket triage, and internal assistants. Agency scoped tightly, guardrails included, human in the loop where it matters.

Evaluation & guardrails

Every system ships with a regression suite: golden datasets, LLM-as-a-judge, and production monitoring for hallucination, cost, and latency. No guesswork about whether it got worse after an update.

Fine-tuning & adaptation

When prompting isn't enough, I fine-tune or adapt open models on your domain data — on infrastructure you control.

Typical outcomes

• Document processing running 10× faster than manual review
• Internal assistants handling thousands of employees with measurable accuracy
• Legal extraction replacing near-shored manual labour, cost-effectively

Tools I work with

AWS Bedrock, Anthropic Claude, OpenAI, LangChain, LangGraph — provider-flexible, no vendor lock-in.

See examples in practice · Discuss your production AI system