Koji vs a Controlled Synthetic Experiment: Recruited Interviews or Fast Filtering First
A product marketing or consumer insights lead facing a launch, message, or pricing decision usually has to choose between two different research jobs, not two competing brands. One job is recruiting real people for interviews that produce quotable, transcript-level evidence. The other is running a controlled experiment against a defined synthetic population to filter a long list of candidate ideas down to a few worth testing with real people. Koji is built for the first job. A causal simulation approach is built for the second.
What does Koji actually do?
Koji is an AI-native interview platform that runs and moderates customer discovery calls with real, recruited participants, then synthesizes the resulting transcripts into themes and recommendations. It is one of several automated interview platforms that a recent industry roundup of nine tools grouped together as software-run discovery research (Koji, "Best AI Interview Software in 2026: 9 Platforms Compared"). The job it does well is scaling discovery-interview volume: moving a team from roughly 10 manually moderated calls per quarter to roughly 50 per month, without adding headcount to recruiting, scheduling, or synthesis.
That output has a property a simulation cannot substitute for: a transcript from a real person that a stakeholder can read and trust as evidence of what an actual customer said.
What does a controlled synthetic experiment test instead?
A causal simulation approach does not recruit anyone. It defines a synthetic population that stands in for a target audience, then runs a controlled experiment that compares candidate actions, messages, prices, or concepts against each other. Subconscious runs this kind of experiment to compare actions and estimate which one is more likely to move a defined behavioral outcome, with uncertainty reported where the study design supports it. The output is a causal comparison across the options tested, not a transcript.
A transcript answers "what did this person say about the idea?" A controlled experiment answers "which of these alternatives is more likely to change behavior, so the team can stop testing the rest?"
Where each method wins
| Dimension | Recruited interviews (Koji) | Controlled synthetic experiment |
|---|---|---|
| Respondent type | Real, recruited participants | Defined synthetic population instrument |
| Primary output | Interview transcripts, themes, quotes | Causal comparison across actions, with uncertainty where supported |
| Provenance for stakeholders | Real-person quotes and transcripts | No transcripts or quotes; not a substitute for recruited evidence |
| Best use | High-stakes validation that needs citable, real-person provenance | Filtering many candidate messages, prices, or concepts before committing research budget |
| Escalation path | Confirms or overturns what a filtering pass surfaced | Narrows the field before recruited interviews begin |
When recruited interviews are the right starting point
Choose recruited interviews first when the output needs to be cited to stakeholders as real-customer evidence, when the decision carries high-stakes investment behind it, or when the team is exploring a genuinely new market segment where no population definition yet exists to simulate against.
When a controlled experiment is the right starting point
Choose a controlled synthetic experiment first when the team has more candidate messages, prices, or concepts than traditional exploratory market research can responsibly work through in the 3 to 4 weeks that kind of study usually takes, when the decision needs a same-week directional read before it can commit to a longer recruiting cycle, or when a marketing, product, or growth team without dedicated research operations needs to narrow a long list before asking for real-human research budget.
How the two connect in one workflow
A controlled experiment can run upstream, comparing many candidate options and surfacing the two or three worth carrying forward. Recruited interviews then run downstream, on that narrowed shortlist, to validate the finalists with real customers before the team commits budget behind one.
How Subconscious fits into that workflow
Subconscious can also test or validate studies with real human participants, and can run controlled studies against a person-level audience graph covering 800 million real people when scale of population definition matters to the decision. Neither claim substitutes for the interview transcripts a platform like Koji produces; both describe how a filtering pass and a validation pass can share the same decision framing.
Limitations and failure conditions
Subconscious does not moderate live interviews, does not produce human-respondent transcripts or quotes, and does not replace recruited real-human research when a decision requires that kind of provenance. Treat any specific sample size, panel configuration, or turnaround number described for a particular research vendor's own product as a feature of that vendor's setup, not a universal limit of every simulation-based method.
Next step
See how Subconscious runs these experiments, review prior case evidence, or read the current research before deciding which starting point fits the decision in front of you. Teams ready to scope a specific decision can book a session.