An AI demo can answer a question, summarize a document, or trigger a workflow. An engineering team evaluating a development partner needs to understand how that behavior will hold up when the inputs, users, and operating conditions change.
The evaluation should make room for imperfect scenarios: incomplete records, conflicting instructions, unavailable dependencies, and requests that the system should decline.
GeekyAnts’ guide to choosing an AI product development company provides a starting framework. Its central recommendation is to request evidence across production experience, architecture, security, delivery, economics, and ownership.
The guide also proposes scoring answers from zero to three, progressing from missing evidence to claims, documentation, and production verification. It emphasizes operating costs, evaluation practices, and ownership of assets beyond source code.
For developers participating in vendor selection, these ideas become more useful when applied to a concrete workflow.
Start With One Workflow and Its Failure Conditions
Consider a hypothetical AI assistant that reads support tickets and prepares refund requests.
A basic demonstration might show a ticket arriving, a model interpreting the complaint, and a refund recommendation appearing on screen. That sequence leaves several engineering questions unresolved.
What happens when the ticket references two orders? What happens when a refund has already been issued? What happens when the customer requests an amount that differs from the transaction record?
An evaluation session could use a small collection of synthetic tickets:
| Scenario | Behavior to examine |
|---|---|
| A clear request linked to one order | Correct identification of the order and requested action |
| A ticket with missing transaction details | A request for clarification |
| A duplicate refund request | Recognition that the action may already be complete |
| Conflicting information across records | Escalation instead of an unsupported assumption |
| An unrelated instruction inside the ticket | Preservation of the intended workflow boundaries |
This exercise gives the discussion a shared reference point. Both the buyer and the proposed delivery team can explain what they consider acceptable behavior.
Separate Interpretation From Permission
In the refund example, understanding a customer’s request and authorizing a financial action are separate responsibilities.
A useful design discussion would examine where each responsibility sits. The model might propose an action, while application logic checks the account, transaction, permitted amount, and approval requirements.
The evaluation should follow the proposed action through those checks.
For example, a demonstration could deliberately submit a refund request for an order belonging to another account. The important observation is whether the surrounding system rejects it, records the attempt, and produces an appropriate response.
This creates a more concrete discussion than a general statement that the assistant has safeguards.
Examine What Happens After a Timeout
A successful response reveals only one path through the application.
Suppose the payment service accepts a refund, but the assistant does not receive confirmation before its request times out. A retry could create ambiguity about whether the action already happened.
The proposed delivery team should explain how it would represent that uncertain state. The discussion might cover request identifiers, transaction reconciliation, retry rules, and the message shown to the support agent.
A useful follow-up is to ask the team to sketch the sequence of events. That sketch can reveal assumptions that remain hidden in a polished interface.
Measure the Whole Task
A hypothetical comparison illustrates why isolated model metrics can mislead.
Assume two implementations each process 1,000 tickets:
- Implementation A costs $20 to run and produces 600 accepted resolutions.
- Implementation B costs $35 to run and produces 900 accepted resolutions.
The model cost per accepted resolution is approximately $0.033 for A and $0.039 for B. On that narrow measure, A appears cheaper.
However, A leaves 400 tickets requiring further work, compared with 100 for B. The comparison changes once the team includes the time needed to review, correct, or complete those cases.
These figures are illustrative. Their purpose is to show why the definition of “completed” should be agreed before a cost comparison begins.
Test Whether Another Engineer Can Continue the Work
A practical handover exercise could ask an engineer unfamiliar with the implementation to make one small change: add a refund category, update a business rule, or reproduce a failed ticket.
The exercise would expose whether setup instructions are sufficient, test fixtures are usable, and important decisions are documented.
It would also distinguish access from usability. Receiving a repository provides access to files; successfully making and validating a change demonstrates that another team can work with them.
Turn the Evaluation Into Sprint Acceptance Criteria
Vendor selection findings should influence the delivery plan.
If duplicate actions are a concern, the first implementation milestone should include a duplicate-request test. If ambiguous tickets require human review, that review path should be demonstrated alongside the successful path.
A focused evaluation leaves the engineering team with specific acceptance criteria, known uncertainties, and a clearer basis for deciding whether the proposed partner can deliver the intended product.
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