Solution · AI Operations
From AI pilot to production: the function that crosses the chasm where 95% of pilots die —and keeps them alive after
The pilot isn't the problem; the leap to production is. That leap isn't a final push, it's a continuous function: industrializing the pilot, making it perform on real data and keeping it alive. We run it for you, so your pilot doesn't join the statistic of the ones that stayed a demo.
The problem
You have pilots that work in the demo room and never reach production. It's not bad luck: production is a different discipline.
- The pilot wowed in the demo, but plug it into real data and real volume and the edge cases nobody anticipated show up, and it stalls halfway.
- There's nobody to harden it: error handling, guardrails, monitoring, cost per case —the 70% of the work the demo skips and nobody budgeted for.
- Every stalled pilot becomes another data point in the statistic: the 95% of generative AI pilots that never scale.
- Even if it reaches production, nobody has the mandate to sustain it, so it degrades until it stops adding value and goes back in the drawer.
Cost of staying the same
A stalled pilot doesn't read as a loss: it reads as a project "in progress" that hasn't moved in months. But the cost is real —the budget spent on the demo that doesn't pay off, the team that lost interest, and the competitive edge that never arrives because AI is still a boardroom promise. What kills the pilot isn't the model; it's that nobody operates the trip from demo to production as the continuous function it is.
The solution
We run the function that takes your pilots to production and keeps them there —industrialization, measurement and on-call— without you building an internal team
- 1We start from your current pilots: what they promise, where they break on real data, and what they lack to handle volume —the engineering and operations work the demo skipped.
- 2We industrialize them: exception handling, explicit guardrails, human-in-the-loop where the error costs, and cost per case under control. Not a bigger prototype, a system that holds.
- 3We make them perform on data, not intuition: we measure cycle times, failure rate and off-script cases, and iterate before the problem reaches the customer.
- 4We sustain them as a continuous function: someone with the mandate to keep the system performing, escalating what needs human judgment and documenting every decision.
What changes
What you stop losing
Your pilots stop dying in the chasm between demo and production: the function that makes that crossing —the one almost nobody budgets— becomes operated, not improvised.
Mechanism
The leap to production stops depending on a heroic push from whoever built it: it runs as a continuous discipline, with hardening, measurement and on-call.
Mechanism
You don't build an AI Operations team to cross one pilot: you contract the function already running and decide later whether to internalize it.
Mechanism
What we measure: pilots that reach production vs the ones that stall, time from stable pilot to production, failure rate under real volume, and coverage of systems with an operational owner.
What we measure
Spec sheet
- Work it removes
- having nobody to run the trip from pilot to production: the hardening, measurement and sustaining the demo skips
- Typical setup
- 2–4 weeks the diagnosis; phased production
- Input
- your current AI pilots and access to the real data and systems where they have to perform
- Output
- the pilot industrialized and in production, with monitoring, iteration and an operational owner —or the honest diagnosis of why it shouldn't scale
- Works with
- AI pilots & systemsData & monitoring stack
- Can connect to
- The AI Operations salary calculatorThe Implementa roles framework
- What we measure
- pilots that reach production vs the ones that stalltime from stable pilot to productionfailure rate under real volumecoverage of systems with an operational owner
- Good fit for
- companies with one or more AI pilots that work in demo and never cross to production, needing the function that makes that leap without building an internal team
- Not a fit for
- anyone who doesn't have a pilot yet and is after a first implementation from scratch —that's a build project, not an industrialization one
Frequently asked questions
Because production is a different discipline. The demo is tested on chosen cases; production faces dirty data, real volume and edge cases nobody anticipated. The trip between the two —hardening the system, handling exceptions, measuring and sustaining it— is the 70% of the work the demo skips and almost nobody budgets. It's not that your pilot is bad; it's that the function that industrializes it is missing.
AI Operations as a service runs what's already in production. This is the step before: the function that takes a pilot that hasn't crossed yet and brings it to production —hardening and industrialization included— then sustains it. If your system is already in production and you just need someone to run it, that's the other service; if it's stuck in pilot, this one.
We tell you, and that's part of the value. Not every pilot deserves production: some solve a problem that no longer exists, others have a cost per case that doesn't add up. We'd rather save you the spend of industrializing what won't pay off than charge you to scale a mistake. The goal is that the ones that should cross do, not all of them.
Want it running in your business?
You’ve pinned the problem. We ship the fix and leave it measured.