AI Systems Engineering
We design and build LLM and multi-agent systems — and the cloud architecture that keeps them fast, observable, and cost-controlled once real users arrive.
Built with
Capabilities
Most engagements start in one and grow into the next — from a proof of concept, to a system in production, to the data work that keeps improving it.
LLM applications that hold up outside the demo — grounded in your data, measured, and guarded against the failure modes that surface in production.
The unglamorous half that decides whether an AI feature survives launch: services, pipelines, deployment, and infrastructure you can hand to a team.
Evidence before opinion. Modelling, experimentation, and benchmarking that tell you which approach is actually worth building.
How we work
No open-ended discovery. Each phase ends in something you can review, run, or hand to your team.
A working session on the problem, data, and constraints. You leave with an architecture and a fixed-scope proposal.
A narrow end-to-end slice on your real data, with an evaluation set — so the decision to continue is based on numbers.
Production hardening: infrastructure, CI/CD, guardrails, cost controls, and the tests that keep quality from drifting.
Documentation, dashboards, and a walkthrough with your engineers. Ongoing support is optional, never a dependency.
Selected work
Client names withheld under NDA. Happy to walk through the architecture and trade-offs on a call.
Enterprise · Support operations
A retrieval layer across scattered internal documentation, surfaced where support agents already work rather than in yet another tool.
Product · Research automation
A LangGraph orchestration where planning, web search, and summarisation agents hand work between each other under an explicit state machine.
Who we work with
Get in touch
Tell us what you're building and where it's stuck. We reply within one business day.