AI that removes real work, with costs, accuracy and data protection measured rather than assumed.
The problem
The hard part of AI is rarely the model. It is choosing a use case worth automating, getting reliable data to it, and knowing when it is wrong. Without those, pilots stall, costs are hard to predict and trust erodes.
Start with a readiness review
In about a week we look at where time is lost, what data you hold and which tasks AI can safely take on. You get a ranked list of opportunities with effort, running cost and payback estimated, and one pilot recommended, before anything is built.
What we build
- AI agents and workflow automation that complete multi-step tasks across your systems
- Assistants grounded in your own documents, with sources cited for every answer
- AI features inside your products: drafting, summarising, classifying and extracting
- Evaluation and monitoring, so accuracy is measured before and after launch
Built responsibly
Every AI feature is tested against real examples before launch. We design for UK GDPR, keep a human in the loop where decisions matter, and choose models from leading providers, including Anthropic's Claude, on accuracy, cost and data-handling terms.
Who it is for
Leadership teams being asked "what is our AI plan?", operations with high-volume repetitive work, and product teams adding AI to software customers already use. For smaller businesses, see AI for small businesses.
