Private Inference
AI models running on machines you control — inside your own walls or your private cloud — so sensitive data gets AI help without ever leaving the building.
Some documents never leave the office — you don't mail the payroll ledger to an outside service, however good they are. Private inference is the same rule for AI: for sensitive work, the model comes to your data, running on hardware you control, instead of your data going out to the model.
The problem, in plain words
Your team finds a dozen places where AI would genuinely help — and half of them are off-limits, because the data is regulated: patient records, payroll, client financials, things that legally or contractually cannot be sent to an outside service. So those workflows stay manual while everything else gets faster, and the gap grows. Or worse: someone, meaning well, pastes the sensitive thing into a public chatbot, and now you have a different problem entirely.
What we set up
We deploy open-weight models — models whose files you can download and run yourself (Llama, Mistral, Qwen, DeepSeek, Gemma, or whichever fits the task) — on infrastructure you control: your own servers, or a locked-down private section of your cloud (a VPC). Serving software (vLLM, Ollama, or TGI — or private endpoints on SageMaker or Vertex) runs the model. Crucially, it plugs into the same gateway as everything else: calls marked sensitive route to the private model automatically, and the same logging, quality checks, and approval gates apply. The hardware is sized to your real usage — expected simultaneous users and volume of text — not to a guess.
How it works, step by step
- Sensitive work is identified
Workflows and datasets are labeled by privacy class, so 'this must stay inside' becomes a machine-readable fact, not folklore.
- An open-weight model is chosen
Picked per task from the open catalog — a compact model for routine extraction, a larger one where reasoning matters.
- It runs on your hardware
Served inside your walls or your private cloud, sized to real concurrency and volume. The data never crosses the boundary.
- The gateway routes by privacy
Calls marked sensitive go to the private model automatically; everything else still uses the best available cloud model. One doorway, two directions.
- The same rules apply inside
Logging, evaluation, and human-approval gates work identically on the private route — private doesn't mean unsupervised.
What changes for you
Before: regulated data means no AI, so the most valuable workflows are the ones stuck being manual. After: sensitive work gets AI help inside the boundary, non-sensitive work still gets the best frontier models, and the routing between the two is an auditable rule rather than a series of individual judgment calls. What it won't do: an open model on your hardware won't match the largest frontier models on every task — that's the honest trade. For most bounded, well-defined jobs it is more than enough, and the quality checks will tell you when it isn't.