A focused pilot for enterprise AI orchestration.
We work with a small number of design partners to turn internal AI prototypes into governed, usable enterprise services — under a clear 30-day pilot scope.
Who is a good fit.
- Organizations with sensitive internal data
- Companies already experimenting with local models, RAG, agents or internal AI tools
- Teams with multiple departments asking for AI access
- IT leaders who need governance, audit, routing and operational visibility
- Companies with GPU servers or workstations they want to mutualize
- Organizations that want to avoid uncontrolled AI tool sprawl
- A team that only wants a simple chatbot
- A single-user experiment
- A company with one model, one server and no governance needs
- An organization that wants only model training rather than AI operations
A focused four-week pilot.
One to three priority workflows. 10 to 30 pilot users. Private deployment. Measured success criteria.
Scope & readiness
- Data perimeter and users
- Success criteria defined
- Infrastructure readiness
- Governance assumptions
Deploy & onboard
- Private deployment
- Capability configuration
- Pilot workflows
- Logs, monitoring, routing demo
Measure & decide
- Usage and performance review
- Adoption and security review
- ROI assumptions revisited
- Go / no-go recommendation
Go / no-go report
- Pilot usage metrics
- Risk and issue log
- Scale-up recommendation
- Hand-off to production scope
Pilot scenario
- · 10 to 30 users
- · One to three priority workflows
- · Private deployment
- · Internal data perimeter defined
- · Success criteria agreed upfront
- · Measured routing accuracy, response quality, adoption, latency and user satisfaction
- · Go / no-go decision after 30 days
Pilot deliverables
- Working private AI orchestration environment
- Configured AI capabilities
- Routing dashboard
- Access and data-zone rules
- Pilot usage metrics
- Risk and issue log
- Go / no-go report
- Scale-up recommendation
From AI prototypes to an internal AI service.
Three common starting points, compared across the dimensions enterprises actually have to operate.
Fast to adopt, but often limited by external data processing, per-seat or token-based costs, vendor dependency and constrained governance.
- Data control
- External processing, limited zoning
- Routing logic
- Single vendor model
- Cost model
- Per-seat / per-token
- Internal data access
- Connector-bound, often partial
- Governance
- Vendor-defined
- Observability
- Vendor dashboards only
- Resilience
- External outages propagate
- Path to scale
- Vendor lock-in
Strong control and customization, but often fragmented, hard to expose to multiple teams, and missing routing, monitoring, fallback and operational ownership.
- Data control
- Strong, but per-project
- Routing logic
- Manual or hard-coded
- Cost model
- Internal compute, unclear unit economics
- Internal data access
- Often siloed per prototype
- Governance
- Project-specific, fragmented
- Observability
- Ad-hoc logs
- Resilience
- No fallback path
- Path to scale
- Re-architecture required
Turns internal models, RAG systems, agents and compute nodes into a governed AI capability network with routing, context handoff, visibility and a path from pilot to scale.
- Data control
- Data zones and role-aware routing
- Routing logic
- Capability-aware, explainable
- Cost model
- Reuse of internal compute, controlled fallback
- Internal data access
- Declared per capability and zone
- Governance
- One control plane, audit trail
- Observability
- Routing decisions, node health, fallback
- Resilience
- Fallback paths between capabilities
- Path to scale
- Add capabilities and nodes incrementally
Pilot economics should be measured from real usage: adoption, response quality, routing accuracy, avoided external API consumption, infrastructure utilization and operational readiness. We do not promise guaranteed savings or certifications — we propose a structured way to measure them.
Private evaluation demo
A live evaluation environment exists, but it is not linked publicly. Access is shared directly with selected reviewers and design partners.
The public website intentionally does not expose the demo endpoint.
Suggested evaluation flow during a private demo
- · Ask a contract question
- · Ask an HR policy question
- · Ask a code question
- · Ask a commercial pilot question
- · Ask a product roadmap question
- · Ask a follow-up
- · Switch capability
- · Review the routing decision panel
The public website intentionally does not expose the demo endpoint. Access is shared directly with selected reviewers and design partners.
Ready to turn AI prototypes into an internal AI service?
Your AI is the engine. ClusterAI is the operating layer that makes it usable across the enterprise.