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Scouts

What should a scout verify before introducing an AI application startup?

an AI application startup: Verify fit and basic facts before introducing a company, while remaining clear about what has not been investigated.

Verify fit and basic facts before introducing a company, while remaining clear about what has not been investigated. Confirm the founder's permission, the current fundraising situation, and the reason the opportunity might match the fund. Avoid promising investment authority the scout does not have.

Start with the decision

Prepare a short claim-and-source note. Separate direct observations, founder statements, and open questions. Ask the fund which evidence is necessary for an initial screen rather than overcollecting confidential material.

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 screen is a filter for a useful conversation, not a completed diligence process. A transparent open question is preferable to a confident claim that the scout cannot support. 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.

Worked example: an AI application startup
ItemIllustration
Decision questionIs the company within mandate?
Case-specific constraintSeparate 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 inspectRequest 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 preserveCompare the promised workflow with the delivered workflow. The relevant question is whether adoption and delivery economics improve together.
Unresolved case questionWho 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.

  1. Is the company within mandate?
  2. Is the founder seeking this introduction?
  3. What has been directly observed?
  4. What remains unverified?
  5. Who makes the next decision?

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 screen is a filter for a useful conversation, not a completed diligence process. A transparent open question is preferable to a confident claim that the scout cannot support.

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.