How to show design-partner traction for AI code review tools before revenue

Show pre-revenue traction as a sequence of observable commitments and use. Interviews, signed pilot agreements, live usage, paid delivery, and recurring contracts answer different questions. Keep their definitions and dates visible instead of compressing them into one traction number.

Start with the decision

Create an evidence ladder with the claim, supporting record, responsible customer role, and unresolved step. Show what changed since the previous update. Include stalled or ended engagements so the reader can understand the conversion process.

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 service suggests review comments on pull requests for an engineering team. Maintainers remain responsible for accepting a finding and deciding whether a change is ready. Review traffic varies with release cycles.

Too many plausible but unhelpful comments can increase reviewer work. A severity label has little meaning unless the team agrees which findings require action.

Turn the scenario into an evidence decision

Consider a review of AI code review tools 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. Keep a labeled sample of true defects, false alarms, dismissed suggestions, and defects missed by both workflows. Record repository and change type so aggregate numbers remain interpretable. 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. Progress can be real without being recurring revenue. A clear account of learning and commitment supports a better discussion than inflating a weak signal into a stronger metric or suggesting a universal fundraising threshold. 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.

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. What actually happened?
2. Who confirmed it?
3. When did it occur?
4. What remains conditional?
5. What is the next observable milestone?

Questions specific to this scenario

• What makes a finding actionable?
• Which changes are excluded?
• Who adjudicates reviewer disagreement?

Keep a labeled sample of true defects, false alarms, dismissed suggestions, and defects missed by both workflows. Record repository and change type so aggregate numbers remain interpretable.

A concrete next test

Ask two maintainers to label the same permissioned set independently before looking at the tool's findings. Investigate disagreements about severity and actionability. A shared rubric gives the next comparison more meaning than counting comments that nobody would act on.

Interpret the result

Progress can be real without being recurring revenue. A clear account of learning and commitment supports a better discussion than inflating a weak signal into a stronger metric or suggesting a universal fundraising threshold.

Compare actionable findings and reviewer effort on similar changes. A tool that produces more comments is not automatically finding more consequential problems.

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.

• First Round Review: Sierra’s design partner strategy
• NIST: AI Risk Management Framework