Small AI products can look attractive because they have users, revenue and a working interface, but acquisition value depends on what survives after ownership changes. Buyers should diligence the underlying system, not only the dashboard.

1. Why do users stay?

Review cohort retention and identify whether usage is habitual, seasonal or driven by one viral launch.

2. Where does traffic come from?

Founder-led social accounts, SEO, app stores and paid ads have different transferability.

3. Which model dependencies exist?

Document providers, rate limits, prompts and features that may be difficult to migrate.

4. What are real gross margins?

Include inference, media generation, payment fees and human operations.

5. Who owns the data?

Confirm rights to customer data, training assets and licensed content.

6. Is the code maintainable?

Review deployment, tests, secrets, observability and key-person dependencies.

7. What happens if the founder leaves?

Identify relationships and processes that exist only in the founder’s head.

8. Are platform accounts transferable?

App-store, payment and advertising accounts may have restrictions that affect a transaction.

9. What liabilities exist?

Review privacy, refunds, intellectual property and unresolved customer commitments.

10. Where is the synergy?

An acquisition should have a clear value-creation thesis such as shared distribution or infrastructure. This extends our earlier AI roll-up strategy analysis.

Buy durable assets, not temporary metrics

The best small AI acquisitions transfer customers, technology, distribution or data that remain valuable after the deal. Due diligence should test that durability explicitly.