AI pilots that stall before production

A working demo is not a production system. The gap between the two is where most AI pilots stall.

We see three patterns:

No eval harness. The pilot works in the demo, but nobody knows if it works on the long tail of real inputs. Without evals, the only way to verify behaviour is manual spot-checks. That does not scale to production.

No fallback plan. The model occasionally returns malformed output, hallucinates a reference, or times out. The demo handled it by rerunning the prompt. Production needs an answer: log and alert, fall back to a rule, route to a human queue, or reject the request cleanly.

No cost control. The pilot ran on a handful of test cases. Production means thousands of requests a day, and nobody approved a budget line for a system that might cost £40 or £4,000 a month depending on traffic.

The fix is not more model tuning. The fix is the scaffolding around the model: structured output, evals that run on every candidate prompt, fallback rules, and a cost envelope that triggers an alert before it triggers a surprise invoice.

When we inherit a stalled pilot, the first week is spent building that scaffolding. The model usually stays the same.

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Sample post — this reflects Sageware’s current positioning on applied AI delivery, but is marked as illustrative content.