Production AI for business: what procurement actually asks

Procurement does not buy ChatGPT wrappers

Businesses searching for an AI software developer are not shopping for another chatbot screenshot. They ask: Where does our data go? What happens when the model is wrong? Who pays when usage spikes?

If you cannot answer those, you do not have an AI product—you have a risk.

Questions serious buyers ask

1. Is customer data sent to a third-party model? Under what contract?
2. Can we turn AI off per tenant or per role?
3. What is the human review path for high-stakes outputs?
4. How do you cap cost and latency under load?
5. How do you log prompts/outputs without leaking secrets into logs?

Ship AI as a feature inside a real system

The winning pattern for companies is not “an AI app.” It is AI inside CRM, support, underwriting, or ops workflows—where the model proposes and the product already has permissions, audit, and a system of record.

• Start with one job-to-be-done that saves measurable time.
• Store structured results in your database, not only chat transcripts.
• Keep the UI honest: show confidence, sources, and a way to correct.

If the feature cannot fail gracefully without an engineer on a call, it is not ready for customers.

Stack that stays maintainable

I typically wire AI into React.js / Next.js products with NestJS / Node or Python services, queue workers for long jobs, and DevOps so you can roll back a bad prompt release. See also how I approach software engineering for business outcomes.

Send the workflow and data constraints to faizan.codes—I will tell you what belongs in v1 versus a pitch deck.