Discovery sprint
A fixed-scope fortnight to rank use cases, audit your data, measure candidate models and size the hardware. It ends with a written plan, a configuration sheet and a quote for the build.
Tell us the models and the users. We return a configuration sheet and a quote.
Spec a system →Model serving, access control and monitoring on hardware you own.
See the platform →
Thirty minutes with an engineer: your workflow, your data, and whether custom AI fits.
Book a scoping call →
Two weeks of discovery, a fixed-scope build measured against your own cases, a hardware burn-in, installation on your site, and a handover that leaves you owning all of it.
Each phase ends with a review and a go/no-go. A first system is typically in production 8–12 weeks after discovery begins.
Pick the workflow, audit the data, size the hardware, write the evaluation set.
Software built and scored against the evaluation set, reviewed weekly with your team.
Hardware assembled and run under sustained load on our bench, with the software loaded.
Racked, cabled, networked and joined to your identity provider, on your site.
Your team runs it with our runbooks, or we run it for you. Models updated as they improve.
Pick the shape that fits your team. Most projects use the first two, and some add the last two.
A fixed-scope fortnight to rank use cases, audit your data, measure candidate models and size the hardware. It ends with a written plan, a configuration sheet and a quote for the build.
Agreed deliverables, built against an evaluation set and demonstrated weekly. Scope changes are written down and re-quoted, not absorbed silently.
Our engineers work alongside yours in your repositories, so your team learns the stack while it ships, and owns it afterward.
After launch we monitor, update models, re-run evaluations and handle hardware issues, for teams who would rather not staff it.
Before we write the system, we write the test: real examples from your business with the answers your experts would accept. Every model, prompt and change is scored against it, and the scores decide what ships. You see the numbers every week.
We assume your security team will review the system, so we prepare for it from the first week: an architecture diagram, data-flow description, access model, logging and retention settings, and a list of every permission the system holds.
You own what we build: the code, prompts, evaluation sets and any fine-tuned weights, delivered to your repositories and storage, plus runbooks and training for the people who will run it.

A ranked list of use cases, a data audit, model test results on your examples, a hardware configuration sheet, a build plan and a quote. If the numbers don't support a build, the plan says so.
Weekly: a working demo and the current evaluation scores. Nothing is held back for a big reveal.
Yes. Each phase ends with a go/no-go, and you keep everything delivered up to that point.
Tell us the models you want to run, how many people will use them and where the hardware should live. An engineer replies with a first configuration and the questions that decide the quote.