Methodology & trust
A useful result starts with a clear distinction between observed human preferences and AI-generated interpretation. A/B Testing keeps those concepts separate and makes the limits of each explicit.
When a study collects votes from people, we report those votes as human responses. The result describes the people who actually completed that study; it is not automatically representative of a country, market, customer base, or demographic unless the study was explicitly recruited and designed that way.
AI Intelligence adds AI-generated interpretation to completed study results: an executive summary, implications, caveats, and suggested follow-up experiments. It does not silently replace human participants with AI personas, and AI-generated analysis is not counted as respondent votes.
Publishing a survey is immediate, but human response collection depends on sample size, eligibility, distribution, and how narrow the requested audience is. Timing varies by study. If you ask us to recruit respondents, we will confirm availability and an estimated completion time for your audience.
A/B preference results tell you which option received more votes in this study. They do not by themselves prove conversion lift, causation, market-wide preference, or future commercial performance. Use the result to make a better-informed decision and validate high-stakes choices with behavioral or market evidence.
How to read a result
Was the evidence collected from human participants? If AI analysis is present, treat it as analysis rather than additional votes.
Look at completed responses and the audience definition before generalizing the preference beyond the people who participated.
Preference is not conversion. Use preference testing for directional decisions; use behavioral experiments when you need evidence of real-world lift.
