Some clients arrive with an idea and need the whole product built. Others arrive with a system that already exists and one layer that is failing them. These are the seven shapes the work usually takes.
Where to apply AI, what to build, and what to buy.
Before a line of code: the map. We pressure-test the use case, size the honest return, and make the model, vendor and infrastructure calls that are expensive to reverse later.
Zero to a product in people's hands — not a pilot that stalls.
Most AI work dies as a promising prototype nobody could take further. We carry it the whole way: the application, the services behind it, the data layer and the deployment — one team from the first sketch to the launch, with nothing lost in a handover.
End-to-end design and construction of production AI systems.
RAG pipelines, multi-agent architectures and the evaluation frameworks that keep them honest. Built to be handed over — instrumented, documented, and legible to the team who inherits it.
Fine-tuning, distillation, and small language models.
A smaller model that knows your domain will beat a larger one that does not. We shrink the model until it fits the job — and the budget — without giving up the behaviour you actually needed.
Serving stacks engineered for throughput and cost.
Quantisation, batching strategy and GPU deployment. This is the layer where most of the bill is hiding, and where the gains are largest because almost nobody goes looking.
AI that survives procurement, security review, and scale.
On-prem and VPC deployments for teams whose data cannot leave the building. Compliance-shaped from the first diagram rather than retrofitted the week before audit.
When the answer isn't in a paper yet.
Short applied-research sprints aimed at the single question your roadmap is blocked on. A working prototype and a clear verdict — including the verdict that says don't build it. When the verdict is build it, the prototype is where our product work starts rather than where the engagement ends.
AI-first, not AI-only. Most of what makes these products work is ordinary engineering done carefully.
No. A prototype is how we start, not what we deliver. We take products from zero through to launch and the life they have afterwards — the application people use, the services under it, the deployment and the operations. Plenty of clients come to us precisely because a prototype somebody else built never became anything.
We are AI-first, not AI-only. Most of what makes an AI product work is ordinary engineering done carefully — applications, data pipelines, services, infrastructure, evaluation. We do all of that, because the model is rarely the hard part.
A consultation, then a short discovery. If the honest answer is that you do not need us, you will hear it in the first week rather than the third month.
Detailed case studies are available on request. Most of our work sits under NDA, so the specifics come in a conversation rather than on a public page.
Preferably. The best outcome is a team that no longer needs us — so we build alongside your engineers and hand over instrumented, documented systems.
That is a normal place to start. Describe the symptom — slow, expensive, unreliable, unshippable — and we will tell you which layer it comes from.
Typical reply within one working day.