Fix · AI pilot
“Our AI pilot demoed well and never went live.”
This is not a failure of nerve. The pilot stopped because somebody sensible asked what happens when it's wrong, and there wasn't an answer.
What is actually going on
A convincing demo takes about a week. It answers well on the questions it was tuned against, in a clean interface, in front of people who want it to work. None of that tells you what happens with a badly scanned PDF, an angry customer, a language nobody planned for, or a question it should refuse.
The gap between the demo and production isn't model quality. It's the unglamorous surround: what it does when it's wrong, how you'd notice it getting worse, what it costs at ten thousand calls a day, and who is accountable when an answer is confidently false.
Almost every pilot dies at that meeting. The idea was usually fine. What was missing was the part nobody demos.
What we do about it
The same idea, rebuilt with evaluation and guardrails, and then actually operated.
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We define wrong before we build right
What a bad answer looks like, what it must refuse to do, what it costs when it fails. That shapes the design instead of being audited afterwards.
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Evaluation comes with the build
A test set from your real inputs, scored on every change, so "it feels worse than last week" becomes a number instead of an argument.
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A human where it matters
Refusals, fallbacks and approval steps wherever an answer touches a real person. Confidently wrong is worse than visibly unsure.
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It gets operated, not launched
Cost, latency and answer quality watched the way uptime is. This is the step that keeps it alive after the enthusiasm fades.
Where we have done this
We run AI in production on our own systems: DigiSapiens staffs incidents on the systems StackWatch watches, under human approval.
See the case studies →Questions
What people ask next.
Can you use what we already built?
Often yes. The prompt work and the product thinking usually survive. What gets added is the part that makes it safe to put in front of people.
How would we know if it starts getting worse?
An evaluation set built from your real inputs, scored on every change. Without one, quality is a matter of opinion, and opinion drifts.
What happens when it gets something wrong?
That is decided before it ships: what it refuses, what it falls back to, and where a person has to approve.
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