Private AI Architecture
On-Premise LLM Deployment for Private Enterprise AI
Deploy capable language models inside your own data centre or private cloud, with predictable inference, controlled data movement, and operational ownership.
Schedule a technical consultationThe enterprise problem
Designed around the risk you need to remove.
Public model APIs can create unacceptable exposure for regulated data, internal workflows, and proprietary knowledge. An on-premise LLM gives security and technology leaders a clear boundary: prompts, model weights, retrieval context, and audit records remain inside infrastructure they control.
Technical methodology
A system boundary you can inspect.
The architecture is decomposed into explicit layers so data movement, authorization, operational ownership, and failure behavior remain visible.
- 01
Enterprise sources
Document stores, SQL systems, identity providers, and operational APIs remain within the client network.
- 02
Policy gateway
Authentication, input validation, routing, rate limits, and redaction are enforced before inference.
- 03
Private model serving
Open-weight models run on dedicated GPU nodes through vLLM or TensorRT-LLM services.
- 04
Evaluation and audit
Prompts, retrieved context, outputs, feedback, and operator actions are logged for review.
Security & compliance
Controls belong in the design.
Security is not a deployment afterthought. It is expressed through identity, isolation, data handling, auditability, and the ability to recover safely.
- On-premise, private VPC, or fully air-gapped deployment
- Zero required telemetry or third-party inference API
- Role-based access and network segmentation
- Encrypted model weights, secrets, and audit data
- Reproducible container and model promotion process
What this enables
Built for ownership, trust, and the next stage of growth.
- Predictable latency and infrastructure cost
- Data residency aligned to internal policy
- A model platform that can evolve without vendor lock-in