Angel investors
How should an angel examine early traction claims from an AI application startup?
an AI application startup: Examine traction metrics through their definition, denominator, period, and supporting records.
Examine traction metrics through their definition, denominator, period, and supporting records. A growth percentage can describe a tiny base or a changing cohort. Bookings, cash collected, revenue, active use, and pipeline should not be treated as interchangeable.
Start with the decision
Reconstruct a small sample from the underlying records when authorized. Ask about exclusions, refunds, services, incentives, and changed definitions. Have the finance owner or an independent specialist address formal accounting questions.
The illustrative scenario
This is an educational workbook. The company or fund and all numerical inputs below are fictional. They are not ADIN results, customer claims, market benchmarks, or a forecast.
The fictional team sells an AI assistant to small support departments. A demonstration works on familiar examples, but the product is still testing performance on new customers' queues. Founder time currently covers many exceptions.
Separate model output from completed customer work. A software revenue claim needs a matching view of model usage, human delivery, customer support, and rework.
Turn the scenario into an evidence decision
Consider a review of an AI application startup in which a confident explanation arrives before the supporting records. Start with the first question in the table. Write down what an answer would change: the next interview, the experiment's scope, a request for specialist review, or a decision to wait. A question without a consequence can produce a longer document while adding little understanding.
Next, inspect the record named in the table and record its date, owner, permission boundary, and limitations. Request a permissioned evaluation sample, customer usage cohorts, an exception log, and a cost model that includes human work. Keep actual results distinct from roadmap features. If the record contradicts the explanation, preserve both accounts and ask which definition, period, or workflow differs. Do not silently select the version that makes the opportunity look strongest.
Finish with a conditional statement: if the specified evidence supports the claim under these conditions, take the named next step; otherwise investigate the unresolved question. A metric is useful when it answers a relevant question and can be interpreted consistently. A precise number with an unclear definition can create more confidence than the evidence warrants. This worked exercise is about the decision process. It does not assert that a fictional company has met a real diligence standard or that a template can authorize an investment.
| Item | Illustration |
|---|---|
| Decision question | What is the metric definition? |
| Case-specific constraint | Separate model output from completed customer work. A software revenue claim needs a matching view of model usage, human delivery, customer support, and rework. |
| Record to inspect | Request a permissioned evaluation sample, customer usage cohorts, an exception log, and a cost model that includes human work. Keep actual results distinct from roadmap features. |
| Comparison to preserve | Compare the promised workflow with the delivered workflow. The relevant question is whether adoption and delivery economics improve together. |
| Unresolved case question | Who reviews failures? |
Use this worksheet
Answer the questions with a source, date, owner, and next action. Mark an unknown as unknown. Use public or permissioned information and involve a qualified specialist when a question requires expertise beyond the team’s competence.
- What is the metric definition?
- What is the denominator?
- Which period is covered?
- What was excluded?
- Can the figure be reconciled?
Questions specific to this scenario
- Who reviews failures?
- How much founder work supports each account?
- Which results hold on unseen examples?
Request a permissioned evaluation sample, customer usage cohorts, an exception log, and a cost model that includes human work. Keep actual results distinct from roadmap features.
A concrete next test
Observe a permissioned workflow where the assistant fails or needs founder intervention. Record the human time and what the customer sees. Compare that case with the demo so the diligence discussion addresses delivered work rather than only the best familiar example.
Interpret the result
A metric is useful when it answers a relevant question and can be interpreted consistently. A precise number with an unclear definition can create more confidence than the evidence warrants.
Compare the promised workflow with the delivered workflow. The relevant question is whether adoption and delivery economics improve together.
Sources and method
These primary resources provide background for the concepts. The fictional scenario, calculations, and worksheet are ADIN educational examples; the sources do not report or validate those invented results. Reviewed October 11, 2026.