A model in production is a product with a lifecycle: retrained, monitored, approved and occasionally rolled back. We build the machinery for that, and the evidence pack that makes it defensible.
The failure is rarely the maths. It is a model that quietly got worse for six months, an undocumented change, or nobody able to say what data it was trained on.
We put models on the same footing as any other production system — versioned, tested, approved by a named person, monitored for drift, and reversible.
Part of Enterprise AI DevelopmentA focused engagement, with the parts that usually get skipped left in.
Retraining and release become routine, which is how models stay good.
Documentation produced as a by-product of the pipeline, ready when someone asks.
Structured for the EU AI Act, ISO/IEC 42001 and internal model risk policies.
Short cycles, visible progress, and honest checkpoints where you decide what happens next.
Book a discovery callModels in use, who owns them, and what evidence exists today.
Training, evaluation, approval and deployment, automated and versioned.
Live performance, drift, data quality and cost, with alerts that reach a person.
A review cadence and documentation that keeps up without becoming theatre.
A generative AI feature is only useful at work if it answers from your content, shows where the answer came from, and behaves sensibly when it does not know.
ExploreAn agent is a loop, not a chat window: it looks at a case, plans a step, uses a real tool, then checks whether it worked.
ExplorePrediction is only worth paying for if it changes a decision.
ExploreAI and analytics inherit whatever your data is.
ExploreTell us where you are and what you need it to do. We will be straight with you about the shortest route there.