Production AI on hardware you own.
Every prompt you send to the cloud is data you no longer control. We spec, build, and deploy AI systems that run entirely on your premises — sized to your actual workload by people who run their own GPU clusters in production.
Needs first. Hardware last.
Nobody talks about GPUs until we have measured the actual load.
Needs gathering
What tasks, what volume, what accuracy bar, what compliance constraints. We interview the people doing the work and measure the actual load before anyone talks hardware.
The right-sized spec
The exact build for your workload — GPUs, memory, storage, power, cooling — and nothing more. We don’t oversell hardware; an over-specced server is just money idling in a rack.
Model selection & tuning
Open-source model selection benchmarked on your task, fine-tuned on your data where it pays. A focused model on your hardware routinely matches cloud accuracy on the task you actually run.
Deployment & handoff
Installed on your premises — air-gapped if required. Monitoring, documentation, and team training included. You own the stack; no recurring dependency on us.
Our own products run on our own GPUs.
This isn’t theory we picked up from a certification course. Our voice agents and automation pipelines run in production on multi-GPU infrastructure we designed, built, and operate ourselves — speech recognition, language models, and monitoring included.
When we spec your build, it’s informed by what actually happens when real workloads hit real hardware: thermals, memory pressure, failure modes, and all the things a sales sheet leaves out.
What a right-sized spec covers
and nothing more- GPUs sized to the measured workload, not the sales sheet
- Memory and storage for the data you actually process
- Power and cooling the room can support
- Open-source models benchmarked on your task
- Monitoring, documentation and training for your team
The cloud AI fine print is getting expensive.
Lawsuits and enforcement actions, as reported. Each row links to its source.
P.M. v. OpenAI — CIPA wiretapping class action alleging billions in damages for using chat data to train models without consent.
National Law Review →Samsung engineers paste proprietary chip source code into ChatGPT. Data ingested into the training pipeline. Samsung bans all GenAI company-wide.
Bloomberg →Italy fines OpenAI €15M — the first GenAI-specific GDPR penalty. Training on personal data without legal basis.
Euronews →Thele v. Google — class action alleges Gemini silently enabled scanning of all Gmail, Chat, and Meet content.
ClassAction.org →Perplexity AI class action — user prompts and personal data allegedly shared with Meta and Google for ad targeting, even in “Incognito mode.”
Bloomberg →The hardware pays for itself, then it’s just electricity.
Cloud AI bills scale with your success — every document, every call, every month, forever. A right-sized local build is a one-time cost that typically pays back in months on high-volume workloads.
For focused tasks, a fine-tuned open model on local hardware matches or beats cloud accuracy — without per-token pricing or rate limits.
When cloud is the right call.
Local wins where it matters most: high-volume work on data you can’t afford to have anywhere else. If your workload is cloud-shaped, we’ll say so.
Prototyping
Proving a concept before investing in hardware.
General reasoning
Diverse domains needing frontier capability.
Non-sensitive, low volume
Public data where privacy isn’t a factor.
Want to know what your build would look like?
Tell us the workload. We’ll tell you the hardware, the cost, and the payback window — and whether local makes sense at all.
Book a free assessment