Where projects stall
None of these are model quality problems. They are engineering problems, and they are what our AI engineers spend their time on.
Four practice areas
Custom and edge models
Model selection, fine-tuning on proprietary data, quantisation and distillation, deployment to embedded targets, industrial PCs and on-premise GPU. No inference leaving your infrastructure.
LLM agents and workflow automation
RAG pipelines on enterprise data, agents with explicitly bounded action sets, human-in-the-loop approval paths, complete decision logging. Predictable behaviour, which matters more than clever behaviour when the agent touches a regulated process.
Anomaly detection and predictive maintenance
Detection on real equipment signal, tuned against your false-positive tolerance rather than a public benchmark. Deployed at the edge, with drift monitoring.
AI governance and assurance
Model inventories, evaluation harnesses in CI, decision records, and evidence packs assembled for the supervisor you actually answer to.
How we staff AI work
Same three engagement models. ML engineers into your existing team, a dedicated AI squad, or full delivery of a defined system.
Most AI engagements start with a scoping phase, because AI scope written before the data has been examined is fiction.
Tell us what you need staffed.
Send the role, the stack, the engagement model and the start date. We reply within two business days with whether we can cover it and who would be on it.
Thanks. We will come back within two business days.
You will get a yes or a no on coverage, and the names of the engineers we would put on it.