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.
- Step 01
Map data & threats
We classify your data, define the perimeter, and agree the residency and retention rules every component must honor.
- Step 02
Size the model
We pick the smallest open-weight model that clears your quality bar, then quantize and benchmark it on your prompts.
- Step 03
Stand up the stack
GPU pool, inference server, private vector store, and the gateway with RBAC, redaction, and logging — all in your environment.
- Step 04
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.
| Criterion | 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.