Services
Most engagements start with one of the first three and grow from there. Nothing here requires committing to the rest.
01 — 03 · Direction and modelling
A clear read on where AI creates value in your operation and where it quietly burns budget. We would rather tell you a project is not worth doing than bill you to find out slowly.
Training and continued pre-training on hardware we own. Because there is no hourly meter on our own machines, experiments that would be uneconomic on rented compute stay affordable.
Open-weight models adapted to your domain, tone, and task. Full-parameter or parameter-efficient depending on what the evaluation says you actually need.
04 — 05 · Running it
The part most teams underestimate. Serving a large open model well is an infrastructure problem before it is a machine-learning one.
Frontier open-weight models running inside your perimeter — your servers, your cloud account, your network. Sized, tuned, and documented so your team can operate it after we leave.
An OpenAI-compatible endpoint backed by hardware assigned to you alone. No noisy neighbours, no shared tenancy, and no retention of prompts or outputs.
Why this is unusual. Serving a 400–550 GB model requires far more system memory than a typical GPU host carries. Our platform pairs 192 GB of GPU memory with 768 GB of system memory precisely so these models run at usable speed — see the measured benchmarks.
06 — 08 · Verification and delivery
Public leaderboards measure public tasks. We build an evaluation around your work, so model selection rests on evidence from your data rather than someone else's.
Connecting models to the systems that already run your business — so the result is a capability inside your product, not a second stack for someone to maintain.
General software consulting and delivery, with or without an AI component. Backed by two decades of professional engineering experience, including at top tech firms.
That is what the scoping stage is for. Describe the problem and we will tell you where to start.