Genway vs User Intuition: AI Interview Depth and Pricing

Genway’s research platform includes contextual follow-ups, Figma prototype testing, surveys and automated themes. A team running usability tasks alongside interviews may value that combination when the participant needs to interact with a design during research.

User Intuition’s research workflow focuses on the explanation behind a decision or experience. Researchers can commission a study, inspect the accounts supporting each finding and assess which unresolved question merits another investigation. Published interview prices make it practical to fund a targeted follow-up as the team learns.

A natural interview can reveal an explanation the original brief did not anticipate. User Intuition follows the participant’s account through concrete examples and counterexamples, such as the last time a feature changed a decision and a similar situation when it did not. That detail gives researchers more to work with than an isolated reaction.

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 research, a request for automation can stand for fewer repetitive steps, reduced error risk or more confidence in meeting an important obligation. Tracing the consequence helps a team decide what to build and explain why the improvement deserves priority.

Genway provides AI-led collection and real-time research analysis across its methods. User Intuition supports a 4M+ vetted panel, your own participants and interviews in 80+ languages. A research team can explore the same hypothesis across customer groups while each conversation follows the individual experience in depth.

Panel studies return 200+ interviews in 24 hours; with your own participants, interviews run in parallel as they respond. User Intuition findings become available as quality interviews finish; adding participants does not create a sequential moderator schedule. Teams can start with a focused question and expand to another audience when evidence review identifies a missing experience that could change the recommendation.

Genway’s AWS Marketplace Enterprise offer includes 2,000 interview credits and five user seats for that annual price. It is one specific package, not a minimum for every Genway customer. Teams evaluating it should budget around the credits and research methods they will use.

User Intuition charges $30 per quality voice interview. Incentives for your own participants are separate, and specialist audiences are recruited to order. Its pay-as-you-go model lets researchers commit to one brief, examine the findings and purchase the next study when the unresolved question warrants additional work.

A useful interview needs more than a completed recording or a set of automatically generated themes. User Intuition checks the substance of the answers against the learning goals before billing for the conversation. That helps teams maintain depth while running many interviews at the same time.

Genway’s tools support AI collection and analysis across several research formats. User Intuition makes the failed-interview billing outcome explicit within the study workflow. Evidence review then assesses what the accepted accounts support, including exceptions and competing explanations, so the team can connect research quality with the decision it plans to make.

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. For product research, User Intuition uses these principles to assess whether the team understands the consequence behind a theme. It can recommend further analysis of existing interviews or a targeted study of users whose different experience could alter the proposed improvement.

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.

With User Intuition’s agentic research workflow, an assistant can identify what prior feedback already explains and prepare questions for the remaining uncertainty. The researcher reviews the audience, learning goals and study plan, then approves spending before launch.

After the study, an assistant can retrieve source-linked findings and help prepare a follow-up that tests a competing explanation. For example, an apparent onboarding problem may reflect mismatched expectations before sign-up. A focused next study can determine which intervention addresses the cause and which customer group needs a different approach.

User Intuition builds synthetic participants from real, opted-in respondents, using their individual traits, habits and behaviors. It repeatedly compares synthetic responses with the same respondents’ real answers to calibrate the models. That grounding preserves differences in experience and priorities that generic LLM feedback flattens.

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 study planning, individual grounding helps researchers identify useful contrasts and formulate questions about real situations, improving the focus of the next interview investigation.