AI engineering
Applications and internal systems where AI materially changes the outcome — not where it decorates a slide.
We start from the problem, not the model. We design the architecture, choose the models, build the data layer and the quality controls around the answers, then keep it running in production: cost, limits, observability, and behaviour when the model fails.
What that involves
- AI applications and internal tools
- LLM integrations, RAG and search over your own data
- Agents and task orchestration where they earn their place
- Quality controls: evidence, guardrails, human-in-the-loop
- Production-grade AI architecture