AI projects fail because they stop at the demo. We take models all the way to production — with the data pipelines, MLOps, and monitoring that make them earn their keep over the years that follow.
We work across the full AI/ML stack — from raw data through to deployed agents. Every project ships behind dashboards that show what the system is actually doing in production.
Every model we deploy comes with: data lineage, drift detection, A/B testing harness, prompt versioning (for LLMs), human-in-the-loop tooling, and cost monitoring per inference.
We've shipped AI agents into Energy, Banking, and Insurance — see the rotating showcase on our homepage.
Stripped of NDAs, here are the kinds of systems we've built and run for the last few years.
Energy major · autonomous demand-response agent saving £14k+ per dispatch event.
Tier-1 bank · graph + ML hybrid catching mule networks, £8M+ recovered annually.
Insurer · vision + policy reasoning, FNOL-to-settlement in under 5 minutes for cat 1-2.
Compliance team · invoice and contract extraction, 94% straight-through processing.
Retailer · SKU-level forecasting, reducing stockouts by 32% and overstock by 19%.
Enterprise B2B · agent assist with retrieval over 14yr knowledge base, AHT down 41%.
We've seen too many AI projects stall at the demo. Our process is built specifically to get past the 'proof of concept gravity' that traps so many teams.
Define the metric you're trying to move. If we can't quantify it, we don't build it.
Smallest possible model on smallest possible scope, behind a flag, in front of real users.
MLOps, monitoring, evaluation harness, runbooks. Now it's real software, not a notebook.
Monthly model reviews, drift remediation, capability extension. AI is a programme, not a project.