Every engagement starts from the same question: which layer is your problem actually living in? These are the six shapes that answer 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.
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.
AI-first, not AI-only. Most of what makes these systems work is ordinary engineering done carefully.
We are AI-first, not AI-only. Most of what makes an AI system work is ordinary engineering done carefully — data pipelines, services, infrastructure, evaluation. We do that work too, because the model is rarely the hard part.
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.