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The AI vendor market for mid-size companies in LATAM is full of impressive demos and ambiguous contracts. Before signing, there are technical and commercial questions most buyers never ask, and those questions separate a solution that will work in production from one that only works in the pitch.
Evaluating a traditional software vendor is relatively straightforward: you ask for references, watch a demo, review the contract, negotiate the price. The product is static. What you see is what you buy.
With AI the problem is different. The system behaves differently in production than it does in the demo. Edge cases emerge after the first months. Quality depends on both the model and the system design, and the client cannot always tell which one failed. And the vendor has more information than the buyer about how well it will actually work in the client's specific context.
This does not mean all vendors are bad. It means the evaluation process needs to be more rigorous than for standard software.
Who owns the code when the project ends?
If the answer is not immediate and clear, that is a warning sign. Some vendors deliver systems that only run on their infrastructure, through their APIs, with their credentials. When you decide to change vendors or the vendor disappears, you lose the system.
What happens if you decide not to renew the contract tomorrow?
Concrete question: can you take the code and run it on your own infrastructure? Which part of the system depends on the vendor's APIs that you would no longer be able to use?
Does the system use proprietary models or third-party APIs?
There is no correct answer to this, but you need to know. If the system uses GPT-4 or Claude via API, your operation depends on OpenAI or Anthropic maintaining that service and that price. That is not inherently bad, but it should be in the contract and in the cost analysis.
How many weeks did the last similar project take?
Do not ask for a generic estimate. Ask for the most recent case similar to yours: which company, which process, how many weeks from the first kickoff to the system being in production processing real data.
How many adjustment cycles happened after launch?
Every system needs adjustments after going live. An honest vendor will tell you how many and why. A vendor that says "none" or has no data on this is a vendor that has not reached real production or does not measure.
How involved does my team need to be during implementation?
If the answer is "very little," be skeptical. Systems that work in production are built with knowledge of the client's process, which only the client has. If the vendor does not need deep access to your operation to implement, they are probably building something generic that will not work in your specific context.
What happens when the system does not know what to do?
Every AI agent has cases it cannot resolve alone. The question is not whether it fails, but how it fails. Does it escalate to the right human? With enough context for that person to act quickly? Or does it simply do nothing and the task gets lost?
Who monitors the system after launch?
Ask them to explain how they find out when the system starts making errors. Are there automatic alerts? Is there periodic human review of the outputs? Or is the client responsible for detecting problems?
How does the system improve over time?
Is there a defined process to refine the agent's behavior as new cases emerge? Is it included in the project cost, or is it additional work?
Can I speak with someone from a company similar to mine that already has the system in production?
Not in demo. In production, processing real volume, for more than three months. If the vendor cannot facilitate that conversation, it may be that they do not have that case, or that existing clients are not happy.
The difference between a vendor that delivers and one that does not usually comes down to whether they have a documented delivery process or whether they improvise case by case. Ask about the process, not just the result.
Do not ask for an impressive demo. Demos always work, by definition: they are built to work. What matters is what happens when a case arrives that nobody anticipated in the design.
Do not ask for the lowest price. A poorly implemented AI system costs more in team time, production errors, and correction work than it costs to implement it correctly from the start.
If you are evaluating an AI project and want a second opinion on the right scope, the right vendor, or what a reasonable cost looks like for your company, book a diagnostic session.
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