AI startup MVPs need more than a model wrapper. The first version has to prove that the AI output is useful, trusted, and embedded in a workflow customers already care about.
The product should make the model output usable, reviewable, and connected to a real business or user workflow.
AI MVPs often fail because the demo is impressive but the product workflow is vague.
We would build around one repeatable AI-assisted workflow, then add measurement around usefulness, trust, and time saved.
Practical answers
Usually no. Most early products should start with proven APIs, retrieval, prompt design, and workflow validation before investing in custom models.
Define the task, the acceptable output standard, and a review loop. Quality has to be measured against the user's real decision or workflow, not just a demo.
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