AI that works. Secure by design.
Most AI assistants stall at the demo. We build production AI that puts your own data to work — helping your teams sell, find answers instantly, and get things done for themselves — with every access secured and audited. Behind the work: twenty-five years of engineering at Microsoft and GitHub, shipping software used daily by billions.
- 25+ years engineering
- Microsoft · GitHub
- US patent holder
- Shipped to billions
What we build
One governed tool layer — built on the Model Context Protocol — connects your ERP, documents and operational data to every AI surface you use: Microsoft Teams, M365 Copilot, Claude, and whatever comes next. Build the tools once; every assistant gets them.
AI agents & assistants
Assistants your staff actually use — quoting from live stock and customer pricing, answering from your own policies and product data, and letting people do for themselves what used to need a phone call. Cited, evaluated, governed.
Trusted hybrid environments
Cloud AI reaching on-premises data over private networks — no public exposure, no shared secrets, every access authorised as a real identity and audit-logged.
ERP & systems modernisation
Modern, secure API platforms in front of the systems your business already runs on — with existing integrations kept working, byte for byte.
Cloud-native engineering
Azure, AWS and Google Cloud, infrastructure as code, full observability — and engineered for cost as deliberately as for capability.
AI evaluation & economics
We use AI to evaluate AI: judged test suites built from real usage prove every change before release — and tune fast, inexpensive models until they match premium-model quality. The same measured answers at a fraction of the running cost, for any AI application, not just chat.
Selected work
Case study — logistics
Two-way confirmation that ended failed deliveries
A multi-branch building-materials distributor was paying dearly for failed deliveries: lorries arriving loaded with timber and nobody on site to receive them. We built a notification platform that runs the whole delivery lifecycle over two-way SMS and email — confirming dates ahead of time, flagging when the lorry leaves the yard and when it’s one stop away, and writing every confirmation straight back into the ERP.
Large deliveries now arrive expected and attended — safely, with someone on site. The API platform behind it serves roughly 670,000 requests a day, and the legacy API it replaced was kept byte-for-byte compatible: not one existing integration broke.
Case study — enterprise AI
A company-wide AI assistant. No data leaves the network.
For a US enterprise, we built an AI assistant deployed company-wide in Microsoft Teams: cited, retrieval-grounded answers across training, HR, supplier and product sources — plus live stock and customer pricing queried from the on-premises ERP at the moment of asking. Every backing service sits behind private endpoints; even embeddings run inside the tenant boundary.
Frontline staff get instant, cited answers in the flow of work — under governance enterprise IT signed off on. No shared secrets anywhere, per-user authorisation with full audit trails, and every agent change regression-tested against a versioned evaluation suite before release.
The standard
Anyone can demo an agent. Shipping one that still works on a wet Tuesday in week forty is engineering.
- Grounded, not guessing. Answers are retrieved from your own sources and cited back to them. Where there's no source, the agent says so — it doesn't improvise.
- Measured before it ships. Agent behaviour is regression-tested against versioned evaluation suites — every change proven against a golden set before it reaches your staff, and tuned so fast, inexpensive models deliver premium-model quality.
- Secure by design. Private networks, no public exposure, no shared secrets — every access authorised as a real identity and audit-logged. Your data stays on your network.