Frontier AI.
Inside your fence line.
Your data can't leave the building. Fine, the machine moves in. NetRay deploys, fine-tunes, and operates frontier-class language models entirely inside your perimeter: your racks, your rules, zero egress. The models get smarter. Your data goes nowhere.
“No cloud” was never the real requirement.
The requirement was always “no leaks.” Somewhere along the way, that got translated into “no AI,” and your competitors who read the requirement correctly are now three years ahead on tooling. Public copilots are off the table, one pasted paragraph of export-controlled text into a consumer chatbot is a federal reporting event, not an oops. But the alternative was never abstinence. It's ownership.
We install the machine inside your walls.
Six service lines. One outcome: a model that knows your domain, runs on your iron, and answers to nobody outside your badge system.
Forty years of test reports, MRB dispositions, tech orders, and the folder called FINAL_v7_ACTUALLY_FINAL. Fine-tuning fixes that, without a single document leaving the building.
| Technique | When | Data appetite | Typical run |
|---|---|---|---|
| Continued pretraining | Deep domain vocabulary (avionics, metallurgy, MIL-specs) | 100M–1B+ tokens of corpus | days–weeks, multi-GPU |
| SFT, full fine-tune | Maximum quality, dense weights you own outright | 10k–100k+ curated pairs | days |
| SFT: LoRA / QLoRA | 90% of the win at 5% of the compute; per-team adapters | 1k–50k pairs | hours |
| Preference alignment (DPO) | Tone, safety, refusal behavior tuned to your policy | 5k–20k preference pairs | hours |
| Distillation → SLM | Push knowledge into a small model for edge/enclave use | teacher model + task set | days |
| Quantization (AWQ/GGUF/FP8) | Same brain, half the iron | none (post-training) | hours |
Every run ends the same way: a signed model card, a frozen eval score, and weights in YOUR registry. We don't keep a copy. We couldn't if we wanted to, we did the work inside your perimeter, remember?
The Fine-Tuning Lab
Configure a run. Watch the loss curve drop. Get a model card, the same artifact we hand you after a real engagement.
Base model
Technique
Corpus
Simulate your build
1 · What grade of data are you holding?
2 · Try to move it.
This data doesn't commute. Bring the machine to it.
Every grade you selected is restricted from commercial cloud. The model, the fine-tuning run, and every query stay inside your perimeter.
Illustrative. Your compliance officer outranks this widget, bring them to the assessment.
Rough hardware sizing
“Our NFF rate dropped 73% in the first quarter. We stopped shipping boards back that weren't actually broken, and none of the diagnosis data ever left our facility.”
Defense Contractor: PCBSpot deployment, 100% on-premise
Frequently asked
Bring the machine inside.
A 30-minute deployment assessment: your data grades, your hardware reality, a straight answer on what a fine-tuned on-prem stack costs , and what it saves. Run by people who've shipped it, not sold it.
Typically responds within 4 hours