AI vendor evaluation should examine the complete product workflow: data handling, tool permissions, evaluation, failure recovery and operating ownership. A model benchmark or a fluent demonstration does not answer those questions. Ask for evidence that matches the actions and data the proposed feature will actually use.
The contract to define first
Request clear answers about retained inputs, provider dependencies, access boundaries, output validation and human escalation. Separate contractual commitments from product configuration and informal assurances. Ask how changes to models or tools are evaluated and communicated. Current pricing, terms and legal obligations need their own qualified review rather than assumptions from a technical demo.
A worked scenario
A hypothetical vendor offers an assistant that can update customer records. The buyer should inspect how a user authorizes a change, what happens after an ambiguous timeout and who resolves a failed action. A successful summary-only demo does not demonstrate those write-path controls.
An implementation and review sequence
- Define the actual data and actions in scope
- Request evidence for permissions evaluation and recovery
- Assign ongoing model-change and incident ownership
Failure cases worth testing
| Condition or input | Expected behavior |
|---|---|
| Vendor cites a benchmark | Check relevance to the actual workflow |
| An action times out after submission | The recovery contract is explicit |
| A model changes | The product has an evaluation and communication process |
Download the illustrative acceptance checklist (JSON)
Tradeoffs and limits
A long questionnaire can produce vague answers. Prioritize the risks of the intended workflow and ask for a small number of inspectable examples before requesting broad documentation.
Use the result to make a decision
The practical output is straightforward: ask about data retention tool permissions evals failures and human ownership. 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: AI startup technical due diligence: evidence beyond the demo
AI feature readiness assessment
B2B SaaS accessibility procurement
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 should an AI vendor demonstrate beyond a model benchmark?
AI vendor evaluation should examine the complete product workflow: data handling, tool permissions, evaluation, failure recovery and operating ownership. A long questionnaire can produce vague answers.
Which failure cases should be included in the review?
Include these explicit cases: Vendor cites a benchmark: check relevance to the actual workflow; An action times out after submission: the recovery contract is explicit; A model changes: the product has an evaluation and communication process.