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AI Moat Scorecard.

Ten questions across the six tests we apply to every AI company: proprietary data, workflow depth, model independence, unit economics after inference costs, distribution and path to revenue. Answer from what the company has shown you, not from what it has promised. It is a preliminary screen to focus your diligence questions, not a verdict on the company.

Proprietary data
Where does the data behind the product come from?
Does product usage make the product measurably better?
Workflow depth
What would a customer lose by switching?
How do retention and expansion look?
Model independence
How dependent is the product on a single model provider?
Would a better general model help or hurt the company?
Unit economics after inference costs
What is gross margin after model, compute and human review costs?
Distribution
How does the company reach its customers?
Path to revenue
What revenue evidence exists today?
How quickly do pilots convert to paid contracts?
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How this works

  • Each answer scores 0 to 3. Each test is scored out of 100 from its one or two questions; the moat score appears only when all six tests are answered and is their average.
  • A company is only as defensible as its weakest test, so the advice focuses on the lowest scoring one. Any test below 34 is flagged as a critical gap whatever the overall score.
  • Bands: 75 and above, a defensible position worth a full diligence; 50 to 74, a partial moat with specific points to probe; below 50, wrapper risk.
  • The score reflects your answers, not an independent assessment of the company. It is a structured starting point for diligence questions, not investment advice or a recommendation on any security.

Do not enter confidential, personal or deal sensitive information. Results are indicative and intended to support an initial business discussion. Everything is calculated in your browser; nothing you enter is sent anywhere unless you choose to send us your results.