Start with a task your users already perform. Record its inputs and the result they consider acceptable before selecting a model.
Bound the change
Pick one journey, such as preparing a document summary. Identify permitted data and the reviewer. Keep unrelated product redesign outside the first engagement.
Establish a comparison
Prepare representative cases and measure the current work. Include difficult cases and missing information. Evaluate generated results against the same requirements.
Make a delivery decision
Review correction effort, latency and total operating cost. Ask for integration code, tests and operating instructions. A demonstration can establish feasibility, but the first production decision needs evidence from your actual task. Expand only after the limitations and ongoing ownership are understood.
Prepare a buyer brief
Describe the existing journey and the point where people spend avoidable effort. Include the permitted input, expected output and a representative difficult case. Identify the product owner who can decide whether the output is usable.
For example, a document-summary task needs a definition of the relevant period, source permissions and the facts that must be preserved. A model comparison without these conditions can reward readable text that misses the business requirement.
Compare the complete integration
| Area | Evidence to request |
|---|---|
| Product fit | A working journey in your application |
| Permissions | Allowed and denied access cases |
| Evaluation | Reference cases and observed failures |
| Reliability | Invalid-output and service-outage behavior |
| Operations | Source updates and investigation procedures |
| Ownership | Configuration and maintainable code handover |
The supplier should explain assumptions before offering an implementation price. An undocumented integration or an unresolved data-use decision can require a separate investigation. Keep that work visible in the proposal.
Keep authoritative work in existing services
Where the application already calculates values or validates business states, retain those controls. The AI function can explain or propose using their results. Server-side checks still apply when a user accepts generated content.
A structured response is useful for integration, but valid structure does not establish true information. Evaluate both. Missing data should remain recognizable rather than being converted into a plausible field value.
Plan a limited release
Choose a cohort with representative work and available reviewers. Keep a manual path where the task allows. Review correction effort, error types and support questions before expanding.
A release decision should use observed results and known limits. Provider pricing, model behavior and the application's documents can change, so assign ownership of re-evaluation. Ask the partner to demonstrate how another engineer can update and diagnose the feature from the handover.
Your first engagement then produces an integrated function and evidence for the next decision. It also leaves a usable result if the evaluation shows that this particular task should remain manual or needs better source data first.
Related reading : Evaluate AI features without using customer corrections as automatic training data. Discuss your project scope with Sky Vaults.