Most Enterprise AI Never Leaves the Lab
40% of agentic AI projects are expected to be cancelled by 2027 due to cost, unclear value and weak controls (Gartner, 2025).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.
Without AI Hub
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AI starts without ownership or approval
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Every team follows a different delivery path
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Cost, evidence and governance are fragmented
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Governance arrives late in the lifecycle
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AI scales inconsistently across the enterprise
With AI Hub
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Every use case is registered and approved first
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One governed lifecycle for every AI use case
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One trace. One audit baseline
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Governance is built in from day one
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AI scales through a repeatable production model
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%+
Gartner, 2025
of agentic AI projects expected to be cancelled by 2027 due to cost, unclear value and weak controls.
95%
MIT NANDA, 2025
of enterprise GenAI pilots show no measurable P&L impact.
Shadow AI
MIT NANDA, 2025
~90% of staff use personal AI tools. Only ~40% of firms have sanctioned ones.
Built for Teams Taking AI into Production
AI Hub is designed for enterprises where AI pilots are already moving across teams, platforms and governance boundaries and where every stakeholder needs a controlled route from pilot to production.
Platform & AI Teams
Reusable environments, governed endpoints and native Microsoft + Databricks workflows, less repeated setup, less plumbing and faster delivery.
Governance & Risk Teams
Approval gates, validation evidence, traceability and release history built into the AI lifecycle, not added after the build.
Business, Leadership & Cost Owners
Clear visibility into live AI use cases, usage, cost and value, so AI can scale without becoming shadow AI or uncontrolled spend.
​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 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 model access via gateway
Implement
Built in your native stack
Microsoft, Databricks
Validate
Evidence, evals, safety and cost
Promote
Controlled release to production
Operate
Monitor cost, drift and audit
Govern & Evaluate: one trace runs beneath all seven stages: cost · policy · evidence · drift · evaluation.
The audit baseline. No shadow AI.
One Audit Trail Across Every AI Use Case
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.
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
A governed multi-agent workflow, delivered on the retailer's own Microsoft + Databricks stack, inside their tenant with evidence and trace captured at every step.
Weeks to Hours
Research cycle time, on the governed workflow
100% Auditable
Outputs backed by captured evidence and trace
In Your Tenant
No data leaves the environment. Client-owned assets
Why Teams Choose AI Hub and Not a Build From Scratch
One Trace Across Microsoft + Databricks
The Microsoft and Databricks seam neither vendor crosses natively. We do.
Register Before 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.
Implementation Support When you Need it
Cloudaeon brings engineers who understand the AI Hub foundation and your stack, so you can scale faster without rebuilding from scratch.
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.
Two cheques, full transparency. Our fee and your platform consumption are separate, never marked up.
Partner-friendly · Runs in your tenant · You own the code.
FAQ
Questions, answered
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.
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.
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.
It produces the trace, logging and evidence that support audit and record-keeping. It is an evidence support, not a compliance guarantee.