Deployed model inventory
Approved model names, readiness, owner, access posture, and model-cache status in one operating view.
The private AI platformfor enterprises.
Deploy private models, expose one governed endpoint, observe requests, and monitor runtime health inside environments you control.
Product evidence your reviewers can inspect.
Representative pilot outputs for model inventory, gateway policy, and request trace review.
Gateway route policy
A governed endpoint with route policy, keys, rate limits, trace callbacks, and sanitization status.
Request trace evidence
Trace ID, team attribution, latency, policy outcome, and linked workflow context for review.
Readiness and infrastructure-health samples are included in the reviewable artifact previews.
Review the pilot packageFour layers. One platform. Inside your environment.
Clustra deploys into customer-controlled infrastructure and gives platform teams one operating model for model deployment, access, observability, and monitoring.
Deployment layer
Clustra Deploy
Deploy and manage private AI inference inside your environment. Model lifecycle, capacity allocation, scaling, and updates are handled by the platform.
Access layer
Clustra Gateway
One governed access layer between your applications, agents, and private models. Authentication, rate limiting, routing, and usage tracking happen at a single entry point.
Operations layer
Clustra Monitor
Platform health, accelerator utilisation, inference latency, and runtime metrics. Know the operational state of your private AI infrastructure at all times.
Observability layer
Clustra Observe
Request-level observability, response quality tracking, cost attribution, and reviewable trace history. See what your models are doing — not just whether they are running.
Four-week private AI pilot.
A practical path for CTOs: one environment, one first workload, one governed access path, and enough operating evidence to decide what production requires.
Week 1
Discovery
Confirm the first workload, data boundary, target environment, access model, and success criteria.
Week 1-2
Architecture review
Map the network path, identity assumptions, model access, logging, and operational ownership.
Week 2-3
First model
Deploy the first private model workflow and publish an approved model name for applications.
Week 4
Readiness report
Validate traces, health signals, usage attribution, risks, and the production hardening backlog.
First deployment outcomes
Usage attribution
Applications, teams, and owners are mapped to model traffic.
Infrastructure health
Readiness, capacity pressure, latency, errors, and runtime signals are visible.
Audit history
Access decisions, model actions, and platform changes are captured for review.
Production readiness
Controls, owners, open risks, and hardening work are summarized.
First technical call
Review environment type, data boundary, identity path, first workload, deployment assumptions, and pilot success criteria.
Pilot deliverables
Pilot plan, security checklist, reference architecture, and production-readiness report.
Security review is part of the product path.
The platform story should be reviewable by security teams before production: data boundary, identity, access, audit evidence, retention, and network isolation.
Customer-owned retention
Logs, traces, and operational evidence stay where customer policies apply.
Access policy review
Approved model access, application usage, and team attribution are part of the operating model.
Private model boundary
Application teams use one governed gateway instead of direct public exposure for each model runtime.
You own
- Infrastructure, data, identity, logs, retention, and production approval
- Model assets, prompts, responses, access policy, and operating priorities
Clustra handles
- Deployment workflow, gateway access, private runtime operations, and model cache
- Observe/Monitor evidence, readiness reporting, and the production hardening backlog
Reviewable artifacts, not just claims.
Compact previews for the materials architecture, security, and platform teams can inspect during evaluation.
Sample
Pilot Plan Outline
A representative outline for a private AI pilot: scope, owners, timeline, validation steps, and success criteria.
Four-week pilot structure
- Discovery and architecture review
Sample
Security Review Checklist
A sample checklist security teams can use to review data boundary, identity, policy, retention, network isolation, and evidence ownership.
Controls security can inspect
- Data boundary and retention
Sample
Reference Architecture Preview
A reference topology showing how applications, agents, Clustra Gateway, private model deployments, observability, and customer infrastructure fit together.
Deployment topology
- Applications and agents
Sample
Production-Readiness Report Outline
A sample report outline for the end of a pilot: validated outcomes, unresolved risks, operating controls, and production hardening backlog.
Pilot decision evidence
- Validated deployment outcomes
Built for industries where data control decides AI approval.
Finance, government, healthcare, defence, and energy teams often need private AI because residency, retention, auditability, and operating control are part of the approval path.
Keep AI under your governance. Start with a private pilot.
Whether you are evaluating private AI for the first time or ready to deploy next quarter, we will meet you where you are.