A private LLM that never lets your data leave
Deploy capable language models and agents entirely inside your perimeter — on-prem, in your VPC, or air-gapped. No prompts, documents, or completions ever cross your boundary.
- Zero data egress by design
- Open-weight or enclaved models
- Full prompt & tool-call audit trail
- SOC 2 / HIPAA / GDPR-ready
Inference inside your boundary, end to end
Every layer of the stack — weights, runtime, vector store, and the agent's action layer — sits on infrastructure you own and control.
No data egress
Model weights and the inference server run in your environment. Egress-deny policies make leakage impossible to overlook, not just unlikely.
Open-weight models
Llama, Mistral, Qwen, and DeepSeek served with vLLM or TGI — frontier-class quality with weights you can hold.
Private retrieval
Your documents are embedded and indexed in a vector store you host. The corpus never trains anyone else's model.
Air-gap option
For the most sensitive workloads, run with no outbound network at all — updates arrive through a controlled, reviewed pipeline.
Complete audit trail
Every prompt, retrieval, and tool call is logged with identity and timestamp, ready for SOC 2 and incident review.
Policy & access controls
RBAC, PII redaction, prompt filtering, and per-tenant isolation enforced before a request ever reaches the model.
From data map to private inference
A deliberate path that puts compliance and the data boundary ahead of the model choice.
Map data & threats
We classify your data, define the perimeter, and agree the residency and retention rules every component must honor.
Size the model
We pick the smallest open-weight model that clears your quality bar, then quantize and benchmark it on your prompts.
Stand up the stack
GPU pool, inference server, private vector store, and the gateway with RBAC, redaction, and logging — all in your environment.
Harden & certify
We run egress and red-team tests, wire up audit exports, and hand your auditors a clean, documented data flow.
A control plane in front of every token
Private weights aren't enough on their own. Every request passes through a gateway you control: it authenticates the caller, enforces role-based access, redacts PII it isn't allowed to send, and records the full exchange before the model sees a single token.
The same plane governs the agent's action layer — high-stakes tool calls can require human approval, and every decision carries traceable lineage. Security and usefulness stop being a trade-off.
- RBAC & per-tenant isolation
- Inline PII redaction & prompt filtering
- Human approval gates on risky actions
- Immutable, exportable audit log
Public API vs. private LLM
The same model behavior, with the data boundary moved to where it belongs.
| A public model API | A private LLM deployment | |
|---|---|---|
| Data location | Sent to a third-party cloud | Stays inside your perimeter |
| Retention | Governed by vendor policy | Governed by your retention rules |
| Audit | Vendor questionnaire | Your own end-to-end logs |
| Air-gap | Not possible | Fully supported |
| Compliance | Inherited risk to assess | Inherited controls you already run |
Explore the secure-AI stack
Private inference is one piece — here's how it connects to the rest of your security posture.
Frequently asked questions
Does any of our data reach a third-party model provider?
No. A private LLM deployment runs the model weights and the inference runtime inside your boundary — on your hardware, in your VPC, or fully air-gapped. Prompts, documents, embeddings, and completions stay on infrastructure you control. We can prove zero egress with egress-deny network policies and traffic logs.
Which models can run privately, and how good are they?
Open-weight families like Llama, Mistral, Qwen, and DeepSeek now rival closed frontier models on most enterprise tasks, and they self-host cleanly. For regulated workloads we also support vendor-hosted models inside a dedicated, contractually no-retention enclave (e.g. a private VPC instance) when the math favors it.
How do you handle GPU cost and capacity?
We right-size to the smallest model that meets your quality bar, quantize where it's safe, and batch with vLLM or TGI to push utilization. On-prem, we plan capacity against your peak concurrency; in your cloud, we use autoscaling GPU pools so you pay for what you actually serve.
Can this pass a SOC 2, HIPAA, or GDPR review?
Yes — that's the point of keeping inference inside your perimeter. You inherit your existing controls: data residency, encryption at rest and in transit, RBAC, retention policies, and a complete audit log of every prompt and tool call. We hand auditors a clean data-flow diagram instead of a vendor questionnaire.
Keep the intelligence. Keep the data.
One working session to map your data boundary and the fastest path to a private LLM your auditors will sign off on.