01 · Practice

AI infrastructure for regulated industries.

We deploy production-grade AI systems with self-hosted and on-prem infrastructure that maintains complete data sovereignty. From vLLM optimization and model serving to enterprise-grade observability, we ensure your AI infrastructure scales securely while meeting ISO 27001, FCA, and healthcare compliance requirements from the foundation.

01 · Overview

End-to-end deployment, built for compliance.

Kubernetes-native architecture with horizontal autoscaling, load balancing, and GPU-optimized scheduling. Support for Llama, Mistral, and custom fine-tuned models with sub-200ms latency targets.

01

Model serving infrastructure

Production deployment using vLLM and SGLang for optimized inference. Kubernetes-native architecture with horizontal autoscaling, load balancing, and GPU-optimized scheduling. Support for Llama, Mistral, and custom fine-tuned models with sub-200ms latency targets.

02

Self-hosted and on-prem deployment

Air-gapped and private cloud deployments that keep your data within your infrastructure. VPC isolation, network policies, and private endpoints. Offline model updates and secure weight distribution for sensitive environments.

03

RAG system implementation

Hybrid retrieval pipelines combining dense and sparse methods with cross-encoder reranking. Vector database integration (Qdrant, Milvus, pgvectorscale) with optimized embedding pipelines. Citation generation and source attribution for auditable responses.

04

Inference optimization

GPU utilization optimization with continuous batching, KV-cache management, and speculative decoding. Quantization strategies (AWQ, GPTQ) for cost-efficient deployment. Model routing for budget-aware workload distribution across model tiers.

05

Enterprise observability

Full-stack monitoring with Prometheus, Grafana, and custom AI metrics. Token-level cost tracking, latency percentiles, and throughput dashboards. Anomaly detection with automated alerting and incident response integration.

02 · Architecture

Production AI, inside your perimeter.

Production AI systems deployed entirely within your infrastructure with complete data sovereignty.

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flowchart TB
  subgraph YourInfra["Your Infrastructure"]
    subgraph Client["Client Layer"]
      API[API Gateway]
      Auth[Authentication]
    end
    subgraph Serving["Model Serving Layer"]
      vLLM[vLLM / SGLang]
      Router[Model Router]
      Cache[KV Cache]
    end
    subgraph RAG["RAG Pipeline"]
      Embed[Embedding Service]
      VectorDB[Vector DB]
      Rerank[Cross-Encoder Reranker]
    end
    subgraph Infra["Compute Infrastructure"]
      K8s[Kubernetes]
      GPU[GPU Cluster]
    end
    subgraph Observability["Observability Stack"]
      Metrics[Prometheus]
      Dash[Grafana Dashboards]
      Alerts[Alerting & Incidents]
    end
    subgraph Security["Security Layer"]
      VPC[VPC Isolation]
      Encrypt[AES-256 / TLS 1.3]
      RBAC[RBAC & Audit Logs]
    end
  end
  API --> Auth
  Auth --> Router
  Router --> vLLM
  Router --> Cache
  vLLM --> GPU
  API --> Embed
  Embed --> VectorDB
  VectorDB --> Rerank
  Rerank --> vLLM
  K8s --> GPU
  vLLM --> Metrics
  Metrics --> Dash
  Dash --> Alerts
  VPC --> Auth
  Encrypt --> vLLM
  RBAC --> API
      
03 · Approach

Structured pilot to production.

Structured pilot-to-production methodology designed for on-prem deployments in regulated environments.

01 · Discovery & planning

Threat modelling, data audit, and workflow mapping. We assess your infrastructure requirements, compliance constraints, and performance targets. Baseline metrics definition and success KPIs aligned to your business objectives.

02 · Pilot implementation

60 to 90 day structured pilot with constrained scope and clear success criteria. Evaluation harness development, security review, and production planning from day one. Stakeholder UAT and iterative refinement based on real workload testing.

03 · Production & scale

SLO definition and enforcement, autoscaling configuration, and comprehensive runbook development. Model routing implementation, cost optimization, and continuous evaluation pipelines. Handover documentation and team training for operational independence.

04 · Security posture

VPC with private subnets and no public endpoints. Zero-trust network policies. Service mesh encryption with mTLS. Air-gapped deployment support for highest-security environments. HSM integration for key management.

04 · Fieldwork

Shipped, behind the firewall.

Related case study

An air-gapped LLM assistant for claims handlers.

Llama 3 70B on dedicated GPU with vLLM, fine-tuned on anonymised claim interactions, deployed with zero external network dependencies. Manual load down 80%, citations on 100% of responses, GDPR Article 28 processor posture.

Read the case →
05 · Questions

Infrastructure questions.

01

Can you deploy air-gapped?

Yes. Self-hosted, private cloud (AWS, Azure, GCP VPC), or fully air-gapped. Kubernetes with vLLM or SGLang, integrated with your IdP (OIDC or SAML), RBAC, VPC isolation. Air-gapped estates run on offline model updates we co-sign with your security team.
02

What latency targets do you hit?

Sub-200ms P50 for typical RAG responses on a well-tuned cluster. P99 targets are workload-specific; we commit to them in the SoW and enforce via SLO dashboards with canary rollback.
03

Do you support GPU pooling across tenants?

Yes, via Kubernetes namespaces with node pools and priority classes. Token-level usage attribution is emitted to your metering system so chargeback is auditable.
06 · Engage

Scope an infrastructure engagement.

30-minute call. Engineering discovery memo within five working days. A go or no-go on week two.