An AI proof of concept demonstrates a possibility under selected conditions. Production requires a contract for ordinary use, failure, access, cost and support. The transition is easier when the team explicitly lists what the demonstration did not test instead of treating a successful demo as a nearly finished release.
The contract to define first
Build an evaluation set from the intended task and its failure cases. Add authorization, data lifecycle, observability and deployment ownership. Define a rollout boundary and a stop condition. A production readiness review should check the integrated workflow, not only model output quality, because a correct answer can still trigger an unauthorized or duplicate action.
A worked scenario
A hypothetical assistant summarizes workouts accurately in a demo but fails when data is missing. Before launch, the team needs a missing-data response, an evaluation fixture and a way to detect the failure. If the assistant also writes plans, its action and recovery contract need separate acceptance.
An implementation and review sequence
- List assumptions and untested paths from the proof of concept
- Define integrated evaluation and operating ownership
- Roll out a bounded task with observable stop conditions
Failure cases worth testing
| Condition or input | Expected behavior |
|---|---|
| Model output is correct but access is wrong | The workflow fails acceptance |
| Inputs are missing | The feature responds honestly rather than inventing facts |
| A release exceeds its cost budget | The owner has a defined response |
Download the illustrative acceptance checklist (JSON)
Tradeoffs and limits
Hardening every imaginable capability can delay learning. Narrow the first production scope so the team can evaluate a real workflow with explicit limits.
Use the result to make a decision
The practical output is straightforward: identify evaluation security ownership and rollout gaps in a working demo. Keep the assumptions with the artifact so a product owner, implementer or prospective partner can review the same decision. A useful handoff states the supported scope, the evidence collected and the remaining dependency instead of treating a successful demonstration as proof of every operating condition.
Continue with related implementation detail
Related guide: Evaluate the tool action, not just the answer.
Rewrite versus incremental modernization
SaaS build estimate risk register
References and current platform requirements
thoughtbot: customer discovery
The linked references provide platform or process context. The worked scenario and acceptance matrix are original examples; adapt them to the actual product, supported environment and documented responsibilities.
Discuss the workflow
Bring one customer workflow and its constraints to define a reviewable discovery scope.
Explore the relevant AptixLabs service
What separates an AI proof of concept from production?
An AI proof of concept demonstrates a possibility under selected conditions. Hardening every imaginable capability can delay learning.
Which failure cases should be included in the review?
Include these explicit cases: Model output is correct but access is wrong: the workflow fails acceptance; Inputs are missing: the feature responds honestly rather than inventing facts; A release exceeds its cost budget: the owner has a defined response.