Private AI Deployment

What "Private AI Deployment" means

Private AI Deployment is the pattern that takes a working AI system and ships it to production inside your security boundary. That means:

• Inference runs on infrastructure under your control (your VPC, your data center, your air-gapped enclave).
• Data — prompts, responses, retrieval context — never traverses a third-party model API.
• Encryption keys are customer-managed.
• Audit logging is yours, retained per your policy.
• The model provider does not see your data.

We've shipped this pattern six times across different regulated industries. Each deployment is customer-specific, but the architecture skeleton is mature, tested, and refined.

When this is the right engagement

• You already have a working AI system (built by us, your team, or another vendor) and need to deploy it inside your security perimeter.
• You're building a security or compliance product and need to embed AI in a way that doesn't break your customers' data sovereignty.
• Your industry doesn't allow sending data to third-party model APIs (healthcare PHI, legal privilege, classified or sensitive security telemetry, etc.).
• You're consolidating multiple business units onto a single AI platform with multi-tenant isolation.

Deployment shapes we ship

• Customer VPC (AWS, GCP, Azure): model and inference inside your cloud account
• On-prem with managed GPUs: dedicated infrastructure in your data center
• Air-gapped: open-weight model, signed artifacts, no external dependency
• Hybrid: control plane in customer VPC, inference at the edge or on-prem
• Multi-tenant SaaS: hardware shared, data and key material strictly isolated

Architecture pattern

A typical Private AI Deployment includes:

• Inference plane: vLLM, TGI, or sglang servers behind your load balancer. GPU autoscaling tuned to your latency and cost targets.
• Retrieval plane: vector database (Qdrant, Weaviate, or a managed equivalent) inside your perimeter, with embedding compute also internal.
• Data plane: customer-managed KMS, encrypted at rest and in transit, no shared keys across tenants.
• Network plane: private subnets, deny-all egress unless explicitly allowed, optional service-mesh for east-west encryption.
• Compliance plane: prompt-level audit log, response-level audit log, role-based access at retrieval time, PII redaction guardrails, configurable retention.

Compliance documentation is delivered as part of the engagement, including control mapping for SOC 2, HIPAA, and (where relevant) FedRAMP and ISO 27001.

Why customers pick us for this

Most AI vendors will sell you a model. Some will sell you an integration. Very few have built and shipped the full multi-tenant private-deployment pattern across regulated verticals. The architecture we deliver is not a first-time build — it's the consolidated lessons from prior engagements.