Hard-won lessons on RAG, agents, fine-tuning and getting AI to behave in production — no hype, just what works.
You cannot improve what you cannot measure, and you cannot ship what you cannot trust. A practical guide to evaluation harnesses that turn LLM development from guesswork into engineering.
Autonomous agents are thrilling in a notebook and terrifying in production. Guardrails, observability and gradual rollout are the boring engineering that turns one into the other.
LLM applications introduce a new attack surface that traditional security tooling does not cover. A defence-in-depth playbook for building AI systems that survive contact with adversaries — and auditors.
Teams reach for fine-tuning when they need retrieval, and for retrieval when they need fine-tuning. A clear framework for telling the two apart — and knowing when you need both.
An AI feature that delights in a pilot can quietly become unaffordable at scale. The levers that cut inference cost by an order of magnitude — without cutting the quality your users notice.
Tell us the problem. We'll propose the smallest first step that proves real value — usually within a week.
