Aurelius vs User Intuition: Research Analysis and Interviews

Aurelius’s analysis workflow supports notes, tags, insights and recommendations, with links back to supporting material. It is useful when a researcher wants hands-on control over synthesis and a shared place to make findings available to colleagues.

User Intuition’s Customer Intelligence Hub sits within the interview workflow. A team can find earlier evidence, identify a missing experience and commission the interviews needed to explain it. For an agency, this keeps the client’s research history connected to the next brief rather than treating each study as a separate deliverable.

User Intuition can ask a missing follow-up while the participant is still in the interview. If a user says a tool feels unreliable, the moderator can explore the incident they remember, the work it disrupted and what reassurance would change their view. The analysis begins with a more developed account.

User Intuition’s laddering approach is informed by Reynolds and Gutman’s laddering method, which connects product attributes to consequences and personal values. For product decisions, that chain helps distinguish a requested feature from the problem the user is trying to avoid. Aurelius’s goal-guided AI Assist is useful for synthesizing existing evidence; User Intuition also shapes the conversation that produces it.

For a team with a backlog of recordings, improving synthesis may be the immediate priority. Aurelius provides transcription and analysis tools for that material. For a team missing the right customer accounts, User Intuition can conduct the new interviews and produce findings as quality conversations finish.

User Intuition supports your own participants and a 4M+ vetted panel, with interviews in 80+ languages. Recruitment and participant response affect turnaround. Running interviews across segments no longer requires adding a moderator for every session, and the research hub makes those findings available for future questions.

User Intuition’s platform fee is $30 per quality voice interview, including moderation and analysis. Incentives for your own participants are separate, and specialist audiences are recruited to order. For a new study, this gives your team a clear starting budget for generating the evidence.

Aurelius’s pricing lists Premium at $259 monthly or $199 per month billed yearly, with capabilities such as universal search and linked recommendations. Aurelius offers a 30-day trial. Its unlimited-user model can suit broad research sharing; budget separately for the work of recruiting and conducting new interviews.

User Intuition checks the conversation it conducts before charging the platform fee. For example, an interview about workflow reliability needs more than a rating: it should develop the incidents and consequences relevant to the brief. Failed checks do not require a researcher to submit a refund request.

Aurelius’s subscription buys analysis and repository access, so it is a different billing unit from a newly completed research interview. User Intuition’s advantage is a quality condition on the interview spend itself, combined with evidence review before your team acts on the findings.

User Intuition’s evidence review is informed by Malterud et al.’s information power framework and Hennink et al.’s meaning-saturation research. These approaches connect sample adequacy to relevant information and distinguish identifying a theme from understanding it in depth. In a repository workflow, User Intuition applies these ideas to separate an unanswered question from an answer that has not yet been analyzed. It can recommend deeper use of existing interviews when the relevant detail is present, or targeted interviews when a missing role, experience or counterexample could change the conclusion.

For example, we interviewed 43 Walmart shoppers to understand purchase decisions, trip friction and return intent. Thirty-six said they would shop at Walmart again. User Intuition recommends recontacting shoppers after their next comparable trip and comparing those who returned with those who chose another store. The follow-up tests which experiences turn stated intent into a store choice. Explore the Walmart study, two recommended follow-ups and sample presentation.

For example, a product team may have extensive feedback from administrators but little evidence from the employees affected by a workflow change. An assistant can use User Intuition to review that imbalance, draft a brief for the missing audience and estimate the recruiting cost.

The agentic research workflow continues after the interviews: retrieve the resulting findings, ask which quotes support them and compare them with earlier studies. This connects the research archive to an actionable next step while keeping the team responsible for the scope and budget.

User Intuition can help explore how people with distinct work habits might interpret a proposed product change. A frequent user and an occasional administrator may react differently even when both previously requested the same feature. Their individual experiences help the researcher refine the question behind that request. User Intuition’s synthetic participants help your team refine hypotheses and choose the questions to investigate next.

This design is informed by Park et al. (2024). Their simulations of 1,052 people reproduced General Social Survey answers 85% as accurately as those people reproduced their own answers two weeks later. The study informs User Intuition’s emphasis on individual grounding and repeated calibration. For a team maintaining a research archive, the benefit is a way to explore new hypotheses from person-specific evidence and identify where the next study should go deeper.