The full answer
An AI MVP focuses on one high-value use case, such as an assistant for a specific team, a document extraction workflow or a single predictive feature. Narrow scope makes it possible to reach real users quickly and measure whether the AI delivers value.
The timeline depends most on data and integrations. If the content or records the AI needs are already accessible, work moves quickly. If data has to be cleaned, consolidated or connected from several systems, that preparation adds time.
Evaluation is part of the MVP, not a later phase. Creuto builds an evaluation set early, so by launch you know how accurate the system is on realistic cases and where it needs human review.
Work runs in two-week sprints with working software at the end of each, so you can try the AI on real examples well before launch. Regulated or multi-system AI programmes are delivered in phases, with each phase adding capabilities on a proven base.
Once the MVP is live, the next steps are usually guided by data: which questions or tasks users bring, where accuracy needs to improve and which adjacent use cases would add the most value. Post-launch iteration continues in the same sprint rhythm.