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.
AI MVPs start at €7,000. The first version is usually one AI-assisted workflow with honest guardrails — not a platform. Model costs are budgeted per user before launch so unit economics are visible from the first week.
Most AI MVP failures are product failures: the model works, but the workflow does not save the user real time. We validate the job-to-be-done first and treat the model as an implementation detail.
Practical answers
MVP development starts at €7,000 for two core product features, with login, security, architecture, deployment, and 30 days of post-launch support included. UI/UX design and branding adds €2,000, and products with three or more features get a custom quote.
You do, from day one. The repository, cloud accounts, app store listings, and analytics all live under your ownership — there is no lock-in if you later hire in-house or switch teams.
Every build includes a 30-day post-launch window: we monitor errors and analytics, fix issues, and turn the first real usage into a prioritized iteration roadmap before handing over or continuing.
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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