Evaluate an AI integration vendor on its ability to improve a defined workflow under real operating constraints. Ask for a baseline, representative evaluation cases, explicit data permissions, a plan for human review and a way to disable the feature safely. A polished chatbot demonstration does not answer those purchasing questions.
The strongest first engagement is often a bounded pilot with a clear decision at the end. It should reveal whether the proposed system is useful enough to continue, what it costs to operate and which failure modes require additional controls.
Define the task and the alternative
Describe the work in business terms before discussing a model. A hypothetical support team might need help drafting answers about order status from approved records. Its alternative could be the existing search interface plus a response template, not necessarily a different AI product.
Measure the current process using representative cases. Record completion time, correction effort and the types of mistake that matter. An AI feature that produces a quick draft but creates more review work may not improve the overall workflow.
Identify the users and the decisions the system is allowed to influence. Internal drafting, customer-facing advice and actions that alter business records need different acceptance criteria. Begin with the least autonomous workflow that can test the business hypothesis.
Ask the vendor to explain the complete system
The model is one component. Request a diagram showing the user interface, identity checks, retrieved data, external services, model calls, stored outputs and any actions the application can execute.
Ask what happens when a source document changes, an employee loses access or a customer record is deleted. These questions reveal whether the vendor has designed an operational integration or only a demonstration around a static sample.
The NIST Generative AI Profile is a voluntary risk-management resource for generative AI across its lifecycle. Use it to frame evaluation and ownership questions. A reference to NIST is not a certification or a substitute for evidence from your own use case.
Commission a reusable evaluation set
Give the pilot an evaluation set that reflects the actual work, with permission to use the underlying examples. Separate cases used for development from cases reserved for evaluation so the team is not only demonstrating answers it has repeatedly tuned.
The set should include:
- ▸Common requests with an agreed satisfactory answer.
- ▸Requests with incomplete, contradictory or outdated source records.
- ▸Questions that the system should decline or escalate.
- ▸Records that the requesting user is not authorized to access.
- ▸Inputs in the languages your intended users actually need.
- ▸Attempts to redirect the system using instructions inside retrieved material.
- ▸Long or unusual cases that stress the proposed operating limits.
For each case, define the rubric and reviewer. “Accurate” might mean that every asserted order fact is supported by an authorized source and that uncertainty is made visible. The rubric must reflect the task rather than reward answers merely for sounding confident.
Evaluate permissions and actions separately
A useful answer should not reveal information that the user could not otherwise retrieve. Ask the vendor to demonstrate how authorization applies before records reach the model and how cached or indexed content respects changes in access.
If the integration writes to another system, define a narrow set of permitted operations. Establish validation, approval and audit requirements for those operations. A draft message and a refund are different actions even if both can be described in natural language.
The OWASP GenAI Security Project documents security risks in generative and agentic applications. Use such guidance to structure adversarial testing, while asking the vendor to show the behavior of your proposed application.
In the support example, the first pilot might draft an answer for an employee to approve while having no ability to change an order. That creates a useful test without quietly expanding its authority.
Make human review operational
“Human in the loop” needs a workflow. Name the reviewer, show the evidence they receive and explain what happens when they reject an output. A queue that nobody has time to inspect is not a meaningful control.
Decide which cases must be escalated and how the system signals uncertainty. Record corrections so the team can identify recurring problems, but establish appropriate retention and access rules for review data.
Test the experience with the people expected to use it. Ask whether checking an answer is easier than doing the task themselves. Also test what happens when the AI feature is unavailable; the business should have an agreed fallback process.
Compare cost under representative usage
Ask for the cost model behind the pilot: requests, input size, output size, retrieval, storage, external tools and human review. Include the cost of failed or repeated attempts. Use representative workloads rather than treating a short demonstration as a usage forecast.
Set spending alerts and identify who can change limits. Document how a switch of model, provider or retrieval configuration triggers a new evaluation. A cheaper model is not automatically economical if it materially increases correction work.
For cross-border teams, map where each provider processes and stores data and where support staff can access it. Review the applicable terms and requirements for the actual arrangement. Geography in a sales headline is not sufficient evidence.
Define the decision and handover
Agree pilot acceptance before development: the evaluation method, acceptable behavior, operating budget, required controls and unresolved risks that would prevent expansion. Keep any numeric thresholds specific to your workflow and agreed baseline.
At completion, request the evaluation results, configuration, application code within the agreed rights, data-flow documentation, monitoring instructions and rollback or disable procedure. Specify who owns future evaluations and incident review.
Use the software quote worksheet to compare the complete integration scope. To discuss a bounded pilot with TuniCyberLabs, explore software engineering services and send the workflow, data sources and proposed user actions.