For Infrastructure Engineers & Technical Leaders

Deploy Intelligence.
Own the Stack.

Air-gapped, containerized, zero-egress. We engineer high-availability AI appliances designed for data sovereignty and offline-first inference — built for environments where a public API dependency is an unacceptable risk.

zero-egress containerized RAG pipeline edge inference air-gapped capable no vendor lock-in on-premise

Stack Architecture

Every Layer. Fully Controlled.

No SaaS dependencies. No managed services calling home. The entire inference pipeline runs on hardware you own.

L5 API Gateway & Interface REST / WebSocket Authenticated endpoints for internal clients. No external exposure.

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L4 RAG Orchestration LangChain / Custom Context retrieval, prompt construction, and result synthesis.

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L3 Vector Database Chroma / Qdrant Semantic embeddings of your proprietary corpus. Local disk only.

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L2 Inference Engine Ollama / vLLM On-device model serving. Quantized LLMs from 7B to 70B parameters.

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L1 Container Orchestration Docker / Podman Modular, versioned service containers. Isolated per workload.

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L0 Hardware Layer Bare Metal Purpose-built appliance. No hypervisor tax. Full GPU/CPU access.

Deployment Philosophy

Engineered for Sovereignty

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Zero External Dependencies

No API calls to OpenAI. No telemetry to Anthropic. No model weights fetched at runtime. The system is fully self-contained and functional in a Faraday cage if required.

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Containerized Modularity

Each component of the stack ships as an isolated, versioned container. Swap the inference engine, upgrade the vector DB, or roll back a pipeline change — without touching adjacent services.

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Offline-First by Design

Internet connectivity is optional, never required. Initial provisioning can be air-gapped via physical media. Runtime inference requires zero network egress.

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Horizontal Scalability

The appliance architecture supports multi-node clustering for high-availability deployments. Add compute nodes to scale inference throughput without re-architecting.

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Model Agnostic

Deploy Llama, Mistral, Phi, Gemma, or custom fine-tuned models. The stack is model-agnostic — switch or run multiple models simultaneously based on task requirements.

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Full Observability

Every inference request is logged locally: model used, token count, latency, source document citations. Complete audit trail. No data leaves your environment.

Technical Specifications

What You're Deploying

// Inference Tier

Model sizes7B → 70B params

QuantizationQ4_K_M, Q8_0, F16

Local latency< 50ms TTFT

ConcurrencyMulti-session

GPU accelerationCUDA / Metal

CPU fallbackSupported

// RAG Pipeline

Document typesPDF, DOCX, TXT, MD, HTML

Chunk strategySemantic + overlap

Embedding modelLocal (nomic-embed)

Vector storeChroma / Qdrant

RetrievalMMR + cosine sim

Source citationAlways included

// Data Sovereignty

Egress policyZero by default

At-rest encryptionAES-256

Auth layerJWT / LDAP / SSO

Audit loggingFull local trail

Air-gap capableYes

Compliance postureHIPAA / SOC2 ready

// Operations

Deployment methodOn-site + remote

Update mechanismPull-based (opt-in)

MonitoringLocal dashboard

Backup strategySnapshots + cold

Uptime target99.9% on-prem

SLACustom per engagement

Deployment Process

From Spec to Production

A structured engagement model designed to minimise your team's time-on-task.

Discovery & Scope

We map your existing data sources, network topology, hardware constraints, and compliance requirements. Output: a signed scope document with hardware BOM, stack configuration, and rollout timeline.

Hardware Provisioning

We source, configure, and burn-in the appliance. Containers are built, base models are pulled and quantized, and the RAG pipeline is configured. Delivered ready-to-ingest. Typical lead time: 5–10 business days.

On-Site Installation & Integration

Physical rack/shelf installation, network integration, and auth layer configuration. If you have existing SSO or LDAP, we integrate. API endpoints are documented for your internal tooling team. Estimated on-site time: 2–4 hours.

Corpus Ingestion & Validation

Your document corpus is chunked, embedded, and indexed into the vector store. Retrieval quality is validated against a ground-truth Q&A set you provide. Embedding pipeline is repeatable for continuous corpus updates.

Handoff & Ongoing Support

Full runbook delivered. Your team is trained on corpus management, model swaps, and observability dashboards. Support tiers available from pure break-fix to fully managed. You own the hardware; we back the stack.

Deploy Intelligence. Own the Stack.