On-Premise AI Deployment
We design and deploy complete AI infrastructure directly on your hardware bare-metal servers, private cloud, or air-gapped environments. Every model, every pipeline, every byte of inference runs within your security perimeter.
Infrastructure Design & Audit
We start by auditing your existing hardware, network topology, and compliance requirements. From there we design an AI infrastructure that fits — GPU selection, storage architecture, network segmentation, and failover planning.
Containerized Deployment
All components are containerized with Docker and orchestrated via Kubernetes or Docker Compose. This ensures reproducible deployments, easy scaling, and clean rollback procedures.
Air-Gapped & Isolated Environments
For high-security environments, we support fully air-gapped deployments with zero internet connectivity. Models are pre-packaged, dependencies are bundled, and updates are delivered via controlled release packages.
Security & Compliance Hardening
Encryption at rest and in transit, RBAC for all services, audit logging, and integration with your existing identity providers (LDAP, Active Directory, SAML). Aligned with GDPR, HIPAA, ISO 27001, and SOC 2 controls.
Why On-Premise Matters
Cloud AI providers offer convenience, but they come with trade-offs that many enterprises cannot accept. When you send data to an external AI API, you are trusting a third party with your most sensitive information — customer records, financial data, proprietary processes, patient files. For organisations operating under HIPAA, GDPR, SOX, or internal data governance policies, this is often a non-starter.
On-premise deployment eliminates this risk entirely. Your data never leaves your network. There are no external API calls during inference, no third-party logging of your queries, and no dependency on another company's uptime, pricing, or policy changes. You own the models, the weights, and the compute. If a cloud provider deprecates a model version or raises prices, your operations are unaffected.
What We Deploy
A typical on-premise AI deployment includes multiple interconnected components, each containerized and orchestrated to run reliably on your hardware:
- —Model serving infrastructure: GPU-accelerated inference servers running vLLM, NVIDIA Triton, or Ollama — configured for your specific model sizes and throughput requirements. We handle quantization, batching, and memory optimization to maximise performance on your available hardware.
- —Vector databases: Qdrant, Weaviate, or FAISS deployments for RAG systems and semantic search. Configured with persistence, replication, and backup strategies appropriate for your data volume.
- —Application services: FastAPI or gRPC endpoints that connect your AI models to your business logic. Authentication, rate limiting, request routing, and response formatting — all running within your network.
- —Monitoring and observability: Prometheus and Grafana dashboards for real-time visibility into GPU utilisation, inference latency, request throughput, and model health. Alerting configured to your operational thresholds.
- —Identity and access management: Integration with your existing Active Directory, LDAP, or SAML provider. Role-based access controls ensure each user and service account can only access the AI capabilities they are authorised for.
Deployment Timeline
A standard on-premise AI deployment follows a structured timeline, typically completing in 4 to 8 weeks depending on infrastructure complexity and the number of AI services being deployed:
Week 1–2: Infrastructure audit and design
We assess your existing hardware, network topology, GPU availability, and compliance requirements. The output is a deployment architecture document with specific component specifications, resource allocations, and network diagrams.
Week 3–4: Environment setup and model deployment
Container orchestration is configured, model weights are transferred and loaded, inference endpoints are stood up, and initial performance benchmarks are run. Security hardening is applied at this stage — TLS certificates, firewall rules, RBAC policies.
Week 5–6: Integration and testing
AI services are connected to your business systems (ERP, DMS, CRM, internal APIs). End-to-end testing validates that queries flow correctly, responses meet quality thresholds, and access controls are enforced. Load testing confirms the system handles your expected concurrency.
Week 7–8: Handover and documentation
Your team receives operational documentation, runbooks for common maintenance tasks, and training on the monitoring dashboards. We validate that your IT team can independently restart services, update model weights, and scale resources.
Industries We Serve
On-premise AI deployment is particularly critical for organisations in regulated and data-sensitive industries where cloud AI is either legally restricted or operationally unacceptable:
Compliance Frameworks
Every deployment is designed to meet the specific compliance controls required by your regulatory environment. We have experience implementing AI infrastructure aligned with HIPAA (healthcare), GDPR (EU data protection), SOX (financial reporting), ISO 27001 (information security management), SOC 2 Type II (service organisation controls), and NHS DSP Toolkit (UK healthcare). The on-premise architecture inherently satisfies data residency requirements — your data stays in your jurisdiction, on your hardware, under your control.
Technology Stack
Core tools and frameworks used in on-premise deployments