Attest vs User Intuition: AI Interviews and Pricing

Attest’s Measure product supports quantitative research, while Explore conducts adaptive AI voice interviews. Compass assists with planning and analysis. That combination is useful when a team wants to explore a question qualitatively and measure patterns through surveys within the same platform.

User Intuition’s consumer research workflow centers on the explanation: what happened, why it mattered and which experiences qualify the finding. A team can investigate a purchase barrier, review the evidence behind a proposed change and commission a targeted follow-up when a consequential question remains unresolved.

Attest Explore supports qualitative conversations, automated summaries and follow-up questions. User Intuition’s approach emphasizes tracing an explanation through the specific occasion and the trade-off. If a shopper says convenience matters, the interview can investigate which inconvenience they are avoiding and what they would sacrifice to avoid it.

User Intuition’s laddering approach is informed by Reynolds and Gutman’s laddering method, which connects product attributes to consequences and personal values. For consumer research, an attribute such as easy preparation can lead to a practical consequence, such as getting dinner ready after work, and a deeper priority, such as feeling dependable at home. That chain helps teams choose a meaningful product benefit and the evidence needed to communicate it.

User Intuition can recruit through your own participant list or a 4M+ vetted panel. Findings arrive as quality interviews finish, so research teams can begin examining evidence while the study continues. Screening, recruitment and participant response affect the time required to complete a particular audience.

For consumer research across markets, consistent learning goals help a team compare explanations while preserving local context. A purchase made for a household, a workday or a special occasion can have different priorities even when the stated preference is the same. Interview depth makes those differences available for analysis.

User Intuition charges $30 per quality voice interview. Incentives for your own participants are separate, and specialist audiences require a recruiting quote. A team can start a defined study without purchasing a yearly research allowance, then decide whether the next unresolved question justifies another project.

Attest’s pricing packages research around credit allowances and service options. Explore’s per-study credit fee can simplify budgeting within that program. User Intuition’s advantage is a published cash price for quality interviews with no required subscription, which fits research teams and agencies whose briefs and client budgets vary through the year.

For consumer research, the right demographic profile does not by itself explain a purchase. User Intuition checks whether the interview contains detailed answers and addresses the brief before charging. That makes substantive participant evidence part of the study’s billing unit and reduces manual work over failed sessions.

Attest also applies AI and human quality checks, including within Explore. Its research plan and credit terms define the purchase. User Intuition explicitly ties interview billing to Length, Depth and Coverage, then separately reviews the resulting evidence to help the team decide what a finding supports and what to investigate next.

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 consumer decisions, User Intuition uses these principles to examine how a finding varies by habit, occasion and competing explanation. It can recommend analyzing existing interviews when they contain the missing detail, or reaching a different consumer group when that evidence could alter the proposed action.

A shopper study illustrates how this evidence review works. 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.

Attest’s MCP documentation describes access to raw survey results and excludes creating or amending surveys. That makes it useful for bringing completed survey data into an assistant. Compass provides a separate in-platform experience for helping teams develop and analyze research.

With User Intuition’s agentic research workflow, a strategist can ask an assistant to investigate an unresolved consumer objection. It can search earlier evidence, prepare an interview brief and estimate the cost. Your team reviews the plan and approves spending, then can ask the same assistant to explain the findings and their sources.

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 a consumer team, individual grounding helps preserve differences between routines and priorities as the brief develops, making the next interview more specific and useful.