AI Infrastructure

Private AI engineered for real operational environments.

Convoi helps organizations translate AI objectives into practical infrastructure: GPU platforms, model-serving architecture, security boundaries, observability, and operations designed around enterprise and government constraints.

PRIVATE AI

From concept to operating platform

AI is a systems engineering problem.

Successful private AI requires more than selecting a model. Compute, storage, networking, orchestration, identity, model serving, data handling, monitoring, and lifecycle operations must function as one supportable system.

ObjectivesUse cases, data boundaries, performance and mission needs

PlatformGPU compute, storage, network and virtualization

OrchestrationKubernetes, scheduling and workload isolation

InferenceModel serving, routing, scaling and optimization

OperationsSecurity, monitoring, governance and lifecycle

Architecture domains

GPU & Compute Architecture

Capacity planning and platform design based on model size, concurrency, context length, latency, precision, and operational availability.

GPU selection and density
Memory and throughput planning
Bare metal, virtualized, or containerized deployment
Growth and lifecycle strategy

Model Serving & Inference

Serving patterns that balance usability, performance, isolation, and cost.

vLLM and compatible inference engines
OpenAI-compatible endpoints
Continuous batching and KV-cache planning
Routing, quotas, and model lifecycle

Private & Disconnected AI

Architectures for organizations that cannot send sensitive workloads to public services.

On-premises and restricted environments
Controlled model acquisition
Offline update and validation workflows
Data-boundary enforcement

Kubernetes & Platform Operations

Operational foundations for repeatable deployment and support.

GPU worker pools and scheduling
Persistent storage and model distribution
High availability and scaling
Monitoring and operational handoff

Security & Governance

Controls that align AI services with enterprise security and accountability.

Identity, RBAC, and network segmentation
Prompt and response logging strategy
Model provenance and approval
Auditability and change control

User Experience & Integration

Practical access to AI services through familiar, controlled interfaces.

Web interfaces and API integration
SSO and directory integration
Usage policies and guardrails
Knowledge retrieval and application integration

Delivery framework

A disciplined path to private AI.

1. DiscoverClarify use cases, users, data sensitivity, operational constraints, and success criteria.

2. AssessEvaluate current infrastructure, security controls, skills, dependencies, and readiness.

3. ArchitectDefine the target platform, model-serving pattern, integrations, controls, and operating model.

4. ValidateProve performance, security, usability, and supportability with representative workloads.

5. ImplementDeploy with documented configuration, testing, automation, and change control.

6. TransitionDeliver operational documentation, knowledge transfer, and a sustainable lifecycle plan.

No one-size-fits-all stack. Convoi approaches private AI as an architecture decision shaped by workload, risk, environment, and mission—not as a predetermined product sale.

Planning a private AI environment?

Start with the workload, data boundary, and operational requirement. We can help define the platform that follows.

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