Track 04 · Deploying AI
What does it take to get an AI system into production and actually keep it there?
Most AI spend dies between the pilot and production, which is the most expensive place to stop: the budget is gone and nothing changed. Getting past it means deploying into your own cloud account rather than a vendor black box, attaching permissioning, approval gates, and logging on day one, then running a disciplined first 90 days against measures you agreed up front.
Shelfware is the default outcome, and it costs you the budget plus the credibility of the next request. This track goes from a governed design to a system people actually use: deploying into your own environment, the build in practice, the first 90 days, and how to tell whether it returned the hours it promised. The deepest lessons here are part of the full course.
The reading path
Start here, then this.
Common questions
Straight answers.
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Do you run our AI on your servers or ours?
Yours. We deploy into your own cloud account, so the data stays in your environment and the system stays yours if the engagement ends. You own the asset rather than renting a seat in someone else’s platform.
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How long until a governed system is in production?
A focused build is scoped at six to ten weeks, ending with a working system and audit-ready evidence in your environment. The 90 days after that are where the hours show up, because adoption is what turns a deployment into a return.
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How do we know it is working?
You agree the measures before the build starts: hours returned, error rates, cycle times. Usage stats only tell you people logged in, not that anything got cheaper or faster, which is why measuring what actually matters is its own lesson.
Ready to act on this?
The reading path is the self-serve version. When you want it applied to your own business, this is the next step.
Book an intro call Get a governed system running in your cloud
If AI is not the right tool for your problem, you will hear that from us.