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The Readiness Score

Twelve questions, five minutes. Find out whether your cloud and data foundation can actually carry AI at production scale — the same review we run with clients, before we run it.

Scored in your browser as you go. Your result is never gated behind a form.

Cloud & AI Platform Diagnostic

Most AI pilots do not fail on the model. They stall on the plumbing underneath — infrastructure that cannot scale, data nobody trusts, a use case with no cost baseline, and no one accountable once it ships. These are the 12 questions we ask before recommending a build.

12 questions~5 minNo account needed
01

Cloud architecture & scalability

Whether the infrastructure underneath can actually carry the load.

Can your compute and storage scale up or down automatically as AI workload demand changes, without someone manually provisioning it?

Can your compute and storage scale up or down automatically as AI workload demand changes, without someone manually provisioning it?

Do you have real-time visibility into what AI/cloud workloads are costing you, broken down by project or team?

Do you have real-time visibility into what AI/cloud workloads are costing you, broken down by project or team?

Is your current cloud environment documented well enough that a new engineer could navigate it in a day?

Is your current cloud environment documented well enough that a new engineer could navigate it in a day?

02

Data foundation

AI is only as good as what it's allowed to see.

Is the data your AI use case would need already clean and in one place — or scattered across systems nobody fully trusts?

Is the data your AI use case would need already clean and in one place — or scattered across systems nobody fully trusts?

Do you have data governance in place — who owns each dataset, where it came from, who can access it?

Do you have data governance in place — who owns each dataset, where it came from, who can access it?

Are your data pipelines automated and monitored, or hand-run and ad hoc?

Are your data pipelines automated and monitored, or hand-run and ad hoc?

03

Use case clarity & cost baseline

The single biggest reason AI pilots fail to show ROI.

Do you have one specific, named workflow you want AI to improve — not “AI in general” — with a defined success metric?

Do you have one specific, named workflow you want AI to improve — not “AI in general” — with a defined success metric?

Do you know today's cost of doing that workflow the current way — staff time, error/rework cost, cycle time?

Do you know today's cost of doing that workflow the current way — staff time, error/rework cost, cycle time?

Has someone in the business signed off on what “success” looks like in dollar terms?

Has someone in the business signed off on what “success” looks like in dollar terms?

04

Operational & security readiness

What happens after launch, not just before it.

Do you have monitoring in place for how an AI system performs once deployed — not just building it and hoping?

Do you have monitoring in place for how an AI system performs once deployed — not just building it and hoping?

Are your security, compliance, and data-privacy controls already mapped to how AI would use your data — not just traditional IT?

Are your security, compliance, and data-privacy controls already mapped to how AI would use your data — not just traditional IT?

05

Team & ownership

Someone has to actually own this.

Is there one accountable owner for this initiative who can make decisions, or is it spread across teams with no single owner?

Is there one accountable owner for this initiative who can make decisions, or is it spread across teams with no single owner?

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