Minimum Viable Intelligence: Adding AI to Your MVP for Maximum Impact

Why Add AI to an MVP at All?#

The right AI feature can be the reason users choose you, but it adds cost, latency and risk. The goal is to learn quickly whether AI creates value in your product, not to impress with technology.

Good First AI Features#

• Summaries. Turn long documents, calls or threads into key points.
• Smart search. Let users ask questions in natural language.
• Auto-fill and extraction. Pull data from documents and emails into forms.
• Drafting. Generate first drafts users can edit.
• Recommendations. Suggest next actions or relevant items.

How to Build It Without Overspending#

• Use APIs first. Start with a hosted model instead of training your own.
• Ground it. Use your data with RAG so answers are relevant.
• Keep a human in the loop. Let users review and edit AI output.
• Measure. Track usage, edits and satisfaction for the AI feature specifically.
• Control cost. Cache results and set limits per user.

Common Pitfalls#

• Building AI before the core workflow works.
• Promising accuracy you haven't measured.
• Ignoring data privacy and model provider terms.
• Letting model costs grow unnoticed as usage increases.

Next Steps#

Pick one high-value AI feature, prototype it in a week or two, test it with real users, and decide based on data. Our MVP development and LLM integration services start from $1000.