What we build
Production AI systems designed around real workflows: assistants and copilots inside your own products, document and knowledge systems, data-categorisation and analysis tools, ticket and reporting workflows, and AI built on top of CRM, PSA and document stores. If it touches your operations and your data, it is in scope.
How the engagement works
A senior team
Tech lead, senior engineer and part-time AI ops engineer. The people who scope it are the people who build it — no handovers.
A two-week cadence
Working software every fortnight. You see real progress early instead of waiting months for a big reveal.
On your stack
We build inside your tools, your cloud and your identity — Microsoft 365, Google Workspace, your CRM and databases — behind your auth.
Built for production
Monitoring, testing, permissions, reliability and support are designed in from day one, not bolted on at the end.
Multi-model, no lock-in
We choose the right model and architecture for the workload — not the provider with the loudest marketing. Systems are designed so workloads can move between models and providers as cost, quality and capability change. The architecture matters as much as the model, and it is what stops a build going stale six months after launch.
Timelines and who it's for
Build projects typically run four to twelve weeks depending on scope, integrations and data readiness. They suit SMEs, multi-site businesses and international organisations that have a clear AI opportunity — often identified through an AI Readiness Diagnostic — and want it delivered properly without standing up an in-house AI team first.
What it costs
Build projects are scoped around the value, complexity and integration involved. Smaller production builds typically start in the tens of thousands. We agree scope and pricing before work begins. Book a call and we will give you an honest range.
Common questions
How quickly will we see something working?
We ship on a two-week cadence from the start, beginning with a scoped proof of concept that proves the idea in practice. You review working software every two weeks — not slideware at the end of a quarter.
Do you build on our stack or yours?
Yours. We work in your tools, on your cloud tenancy where practical, behind your authentication, with data handling agreed in the statement of work before anything is built.
What happens when the build is finished?
Your choice: we hand over cleanly with documentation and training, or we keep running it under Managed AI Operations — monitoring, evals and model updates. Many clients start with a few months of Operate and then take it in-house.