Key takeaways
- Give competing vendors the same representative task set.
- Score review effort and failure handling alongside quality.
- Check data export and operational ownership before committing.
The demo is smooth. A document goes in, a polished answer comes out, and the person presenting knows exactly which question to ask next. Your team’s work is messier: missing attachments, unclear requests, inconsistent records, and people who do not know the product’s preferred phrasing.
A useful AI vendor evaluation brings some of that mess into the room. You are choosing something your team will operate, so the comparison needs to cover the work around the answer as well as the answer itself.
Give every option the same job
Choose one bounded task. If you need invoice extraction, compare invoice extraction. If you need answers from a policy collection, use that collection. A long checklist of unrelated AI features makes it easy for an option to win points for things you will never use.
Prepare a set of normal cases and recurring exceptions with expected outcomes. Remove sensitive details or use realistic substitutes. Keep a portion for the final review, so the result is not based entirely on examples that vendors have already tuned against.
Watch someone correct the output
Correct answers are important. So is what happens when an answer is wrong. Can a staff member see the source, fix the result, and continue? Or do they have to copy everything into another tool and start again? Review time belongs in the score.
Record how each trial was configured. One vendor may use a default setup while another spends several days tailoring the workflow. Both may be reasonable options, but those are different delivery efforts and should be visible in the comparison.
| Area | What to observe |
|---|---|
| Output quality | Correctness, completeness, and unsupported claims. |
| Staff effort | Time spent checking, correcting, and handling exceptions. |
| Integration | Authentication, permissions, and behaviour during failures. |
| Operations | Monitoring, support ownership, costs, and data export. |
Ask who helps on a bad Tuesday
Suppose the assistant starts using an outdated source or a connected system is unavailable. Who notices? Who investigates? Can your team pause it? Ask to see the controls and support process rather than treating them as details for after procurement.
Get clear written answers about how information is stored, retained, exported, and deleted under the actual configuration and agreement you would use. A broad product promise may not explain the settings, service tier, or additional components in your proposal.
Price a completed task
Usage charges are only one line. Include setup, integration, staff training, review, monitoring, and rework. Model an ordinary month and a busy one using assumptions your team can explain. A low charge per request can become expensive if many requests need a person to redo them.
Also ask what you can take with you if you change providers. Source documents, configuration, evaluation results, and business records have different export paths. Finding the awkward dependencies now is easier than finding them after a year of use.
Choose the option that meets the quality threshold and fits the way your team can run it. If the difficult part is judging integration or delivery effort, ask Ceyentra to review the workflow alongside the trial outputs.
Frequently asked questions
How many test cases should an evaluation include?
Use enough cases to cover the important task types and recurring exceptions. Coverage and clear expected outcomes matter more than an arbitrary sample count.
Should the cheapest AI tool win?
Compare total cost per completed task, including integration, review, support, and rework, after confirming that the required quality and controls are met.
