Most Enterprise AI Never Leaves the Lab
The gap isn’t the model. It’s getting one use case live, governed and kept running. That’s where pilots die: fragmented delivery, governance bolted on late and no single view of what’s running, what it costs or whether it’s safe.
40%+
of agentic AI projects expected to be cancelled by 2027 due to cost, unclear value and weak controls.
Gartner, 2025
95%
of enterprise GenAI pilots show no measurable P&L impact.
MIT NANDA, 2025
Shadow AI
~90% of staff use personal AI tools. Only ~40% of firms have sanctioned ones.
MIT NANDA, 2025
2-MINUTE OVERVIEW
See How AI Hub Moves AI Pilot to Production
AI Hub is Cloudaeon’s governed delivery and operating model for taking AI use cases from pilot to production across Microsoft and Databricks.
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Register the use case before building
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Apply policies and model access controls
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Validate evidence before production
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Operate with one trace across platforms
One Governed Path for Every AI Use Case
The same seven stages, from the first use case to the hundredth. Standardisation by default, not by discipline, a repeatable AI lifecycle your teams can trust.
Register
Use case, owner, value, risk
Configure
Model, limits, policies, guardrails
Provision
Governed access, environments & monitoring
Implement
Built in your existing native stack
Validate
Evidence, thresholds & human sign-off
Promote
Controlled release through approval gates
Operate
Monitor cost, quality, drift and audit
Govern & Evaluate: one trace runs beneath all seven stages across cost, policy, evidence, drift and evaluation. The audit baseline. No shadow AI.
Why Teams Choose AI Hub and not Building From Scratch
One Trace Across
Microsoft + Databricks
AI Hub joins platform telemetry into one operational view of cost, policy, evidence, drift and evaluation.
In Your Tenant,
You Own it
Runs on your native stack. Client-owned code and runtime, with no proprietary lock-in.
Register Before You Build
Every use case is business-owned, approved and inventoried before engineering begins.
Governed and Repeatable
The same path from the first use case to the hundredth. Standardisation by default, not by discipline.
Why Teams Choose AI Hub and not Build From Scratch
Enforce where we own the control point. Observe where native platforms produce the best telemetry. The result is one governed view across your whole AI systems.
One Trace Across Microsoft + Databricks
The Microsoft and Databricks seam neither vendor crosses natively. We do.
Register Before
You Build
Every use case approved and inventoried first, no shadow AI.
In Your Tenant,
You Own it
Client owned code on mainstream platforms, with no proprietary runtime lock-in.
Governed and Repeatable
The same governed path. Standardisation by default, not by discipline.
Governed by Default: One Trace Across Microsoft + Databricks
Enforce where we own the control point. Observe where native platforms produce the best telemetry. The result is one governed view across your whole AI systems.
Enforce
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Approval before any build
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Gateway auth, rate limits and logging
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Allowed models & tools only
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Validation gate before production
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Policy-based blocking
Observe
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Model & token usage and cost
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Foundry & Databricks / MLflow traces
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Evaluation scores & drift
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Policy breaches, failures and feedback
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Audit & release history
One trace across Microsoft + Databricks = a single audit baseline. Neither vendor crosses that seam. We do.
Trusted By
Platform Partners
Trusted by
Platform Partners
What Each Team Gets
Every stakeholder gets measurable value from the same governed AI platform.
Platform Team
Up to 70% faster environment setup, reusable foundation, controls and observability. No rebuilding every time.
AI Team
Up to 2× more time implementing use cases and less time assembling common production plumbing.
Governance Team
Approval gates, evidence and traceability built into the lifecycle rather than added later.
Business Sponsor
A packaged route for moving one serious use case into production.
Leadership
One portfolio view of live use cases, ownership, cost and operational status.
CFO/ Cost Owner
Cost visibility and control at use-case level, without marked-up platform consumption.
Proof
Proven in production with a leading UK retailer, on Microsoft + Databricks, inside their own tenant.
Backed by Cloudaeon’s Data, Cloud & AI Engineering & Ops across the UK & India.
This Isn't Theory. It Runs Live.
Built by engineers who operate this method in production, not a slide-ware.
A Leading UK Retailer Moved from Manual Research to Governed, Evidence-Backed Al Intelligence.
Cloudaeon's Engineering Teams
Backed by Cloudaeon’s Data, Cloud & AI Engineering & Ops. UK & India.
100% Auditable
Outputs backed by captured evidence and trace.
In Your Tenant
No data leaves the environment. Client-owned assets.
Backed by Cloudaeon’s end-to-end engineering teams
AI Hub provides the governed foundation. Your team builds on it and when you need to scale, Cloudaeon’s Data, Cloud, AI and Operations specialists step in to build, run and expand your production capability.
Three Steps, Fixed and Transparent
Discover
from £25k
2–3 weeks, fixed. Scopes the first use case. Credited against production setup.
Production Setup
fixed price
Scoped in Discovery. We set up the governed foundation in your tenant. Your engineers build on it.
Run
from £8k / month
Monitored, governed operations on a 12-month term.
FAQ
About AI Hub
A production control system for enterprise AI, a governed delivery method that takes every AI use case from pilot to live production, in your own tenant, on your existing Microsoft or Databricks stack.
A productised delivery model with reusable engineering and operating layers, not a black-box software platform you subscribe to.
No. You build in your native stack. AI Hub governs around it and drives more real consumption of those platforms. Your tools and stack stay.
Technical
In your own native stack, Databricks, Azure AI Foundry, Copilot Studio or custom code. AI Hub provides governed endpoints, policies and lifecycle control. It doesn’t build the app.
Each platform records its own best telemetry. AI Hub ingests it and stitches it on a shared trace key, giving one audit baseline across Microsoft and Databricks.
No. It runs in your tenant and governs access to data where it already lives. It doesn’t copy or hold your business data. It records trace metadata and evidence. Payloads are captured only where you choose, by policy.
Yes. Existing apps can be registered and brought under the same governed path, endpoint, policies, evaluation and trace, all without rebuilding them.
Governance & Lock-in
It produces the trace, logging and evidence that support audit and record-keeping. It’s evidence support, not a compliance guarantee.
No lock-in. AI Hub is deployed in your tenant on mainstream platforms, with client-owned code and runtime assets, so there is no proprietary runtime to be trapped in.
You can. The question is whether you want to rebuild common foundations before reaching the outcome and you’ll need a support vendor regardless. Better one that built the solution and knows your stack, typically at lower cost.