AI startup MVPs should solve one workflow end-to-end — not showcase every model feature. Start with retrieval-augmented search, a focused copilot, or document automation with measurable accuracy targets.
Evaluation-first AI development
Define KPIs upfront: accuracy, latency, cost per request, and human escalation rate. Build ground-truth test sets before tuning prompts or models.
Common AI MVP patterns that ship
- Internal knowledge copilot (RAG over docs)
- Customer support assistant with human handoff
- Document extraction and classification
- Workflow agents for ops teams (not open-ended chatbots)