Collect Empirical Inputs across Stakeholders and Systems
Systematically harvest evidence, technical documentation, customer interview data, and market benchmarks to ground each assumption in reality. The founder reviews existing technical specs, data room assets, and user feedback to assess the current confidence level for every listed dependency.
Gathering multi-source inputs grounds theoretical product hypotheses in verifiable empirical evidence. This elevates the map from a list of founder opinions into a rigorous, data-backed risk analysis.
A comprehensive source log cross-referencing each assumption with real-world inputs such as user interview transcripts, API documentation, prototype analytics, or expert technical reviews. The founder must provide explicit confidence scores backed by traceable evidence.
Five questions an expert would ask when reviewing your output
Use these to challenge assumptions, pressure-test your logic, and check the quality of this action's output in the context of the parent task and wider venture development.
- 1
What proportion of these assumptions is backed by primary customer data rather than internal founder opinions?
- 2
Where is the hard evidence supporting your claim that this external API will handle your forecasted transaction volume?
- 3
How recently was this customer feedback gathered, and does it reflect the exact product workflow mapped here?
- 4
What conflicting data points emerged during your review, and why were they excluded from the primary assessment?
- 5
Which external technical benchmarks have you used to validate your performance assumptions?