Machines & stack
Hardware + software
The machines, the GPUs, and the software to run them.
We spec, source, and configure local inference machines and edge devices, then install the complete software stack on top. GPU sizing is based on measured workload requirements, not brochure numbers, so you buy enough hardware and no more.
Deliverables
- Workload analysis and GPU sizing recommendation
- Hardware bill of materials at two or three price points
- Configured inference stack with monitoring and alerting
- Edge-device setup for on-site or offline use
- Benchmarks proving the machine meets the requirement
Signs you need this
- A vendor quote smells like it was sized to their margin, not your load
- You need inference at the edge, on-site, or fully offline
- Cloud GPU bills are becoming a line item finance asks about
- You want to own the capability instead of renting it forever
Work wanted
Tell us what you are trying to do and we will say whether this is the right notice.
Write to the labOther notices
AI audit
Find out if the AI you already run deserves to stay.
Right-tool consulting
LLM, decision model, classic ML, rules, or a human. We map which.
Local AI implementation
AI that never lets your data leave the building.
Hybrid architecture design
Every task routed to the cheapest intelligence that can do it well.
Human-in-the-loop workflows
AI proposes. Humans verify and decide.
AI governance & training
Written rules for using AI, including when not to.