Founders
How to build a bottom-up market estimate for AI code review tools
AI code review tools: Build a bottom-up market estimate from a defined buyer population and a plausible purchasing unit.
Build a bottom-up market estimate from a defined buyer population and a plausible purchasing unit. Start with the segment the company could actually serve. Separate reachable accounts from an entire industry's headline spending.
Start with the decision
Show the count source, inclusion rules, annual spending assumption, adoption constraints, and sensitivity to each input. Use ranges where evidence is weak. Keep the initial obtainable segment distinct from an expansion thesis.
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.
Separate a market assumption from its arithmetic
Assume the fictional team has $160,000.00 available and uses $28,000.00 each month. Dividing gives 5.7 months under a constant-burn assumption. That quotient ignores payment timing, hiring, one-time bills, and uncertain collections. It is a starting point for a dated cash plan, not a prediction of when the company will run out of money.
The market side of the same exercise assumes 320 reachable accounts, each spending $18,000.00 annually. Multiplication explains the model; it does not establish that those accounts exist, can be reached, or will pay. Replace each invented input with sourced evidence and preserve a smaller-segment scenario.
Connect the two models only after identifying the customer milestone the budget can support. Compare actionable findings and reviewer effort on similar changes. A tool that produces more comments is not automatically finding more consequential problems. A financing plan should show the work and decision dates between current evidence and that milestone. Keep future financing and market expansion conditional, and ask a qualified finance professional to review formal forecasts.
| Item | Illustration |
|---|---|
| Fictional cash available | $160,000.00 |
| Assumed monthly net cash use | $28,000.00 |
| Simple runway quotient | 5.7 months |
| Assumed annual customer spending | $18,000.00 |
| Illustrative reachable accounts | 320 |
| Account count × annual spending | $5,760,000.00 |
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.
- Who is counted?
- Why are they eligible buyers?
- What annual unit is assumed?
- Which source supports the count?
- What constraint limits reach?
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
The estimate is useful when a reader can replace an assumption and understand the result. Arithmetic does not validate a buyer count or willingness to pay; those inputs need separate evidence.
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.