Research

Measures how graduate students, postdocs, and faculty actually spend their research time, and how well funding, mentorship, protected time, and collaboration support their work. An AI follow-up interview digs into the single biggest obstacle each respondent names, reconstructing a concrete recent example instead of a vague complaint. Built for research offices, deans, and PIs benchmarking research support.

Measures how safe, fair, and supported people feel in their day-to-day work — trust in leadership, psychological safety, workload fairness, and manager impact — for HR and people teams tracking team health between engagement cycles. An AI follow-up reconstructs the specific moment behind each person's rating instead of settling for a number on a dashboard.

Benchmarks how customers perceive your brand against named competitors — consideration, head-to-head ratings, and switching likelihood — then uses an AI follow-up interview to surface the real story behind a preference or switch. Built for product marketing, competitive intelligence, and strategy teams tracking share of consideration.

Measure behaviors people won't admit directly: the list experiment (item count technique) asks only HOW MANY statements apply — never which — so individual answers stay genuinely deniable while group comparisons reveal the true rate. The native question type randomizes control and treatment lists for you.

A complete consent flow for research studies: plain-language study information, granular consent statements with a comprehension check, signature capture, and contact enrollment. Built for IRB-style requirements — participants confirm they understand, not just that they scrolled.

A clean recruiting screener: knock-out questions first, quota-ready demographics, an attention check, and an articulation check that verifies participants can give the rich answers qualitative research needs. Designed to disqualify politely and comply with prefer-not-to-say norms.

A true factorial vignette experiment: respondents judge realistic scenarios whose key details (price framing, messenger, wording) rotate systematically, so you can measure how each factor shifts judgment. The built-in vignette question type generates the scenario combinations for you.

A repeatable diary entry participants complete each time they use your product in real life: a photo of the moment, what they were trying to do, and what got in the way — with an AI probe on the day's friction. Run it daily or weekly to see usage where it actually happens, not in a lab.

A self-serve usability session: participants share their screen, complete guided tasks on your website or prototype while thinking aloud, and the AI moderator watches, prompts, and probes — then debriefs them. You get recordings, transcripts, and task-level analysis without scheduling a single session.

A voice AI discovery interview that follows Mom Test rules automatically: past behavior, not hypotheticals; specifics, not compliments. It asks about the last time the problem happened, what it cost, and what they've already tried — the questions founders forget to ask when a polite prospect starts flattering their idea.

The standard 10-item SUS questionnaire, scored 0–100 and benchmarkable against decades of published research — with correct alternating positive/negative wording preserved. An AI debrief interview follows the score, so you learn what to fix, not just where you stand.

An open-ended card sorting study for navigation and information architecture: participants group your content into categories that make sense to them, then an AI interviewer probes the logic behind their groupings — the part a drag-and-drop grid alone never captures.

A post-experience research survey evaluating the effectiveness of AI interview moderators across demographic groups. Measures overall experience quality, perceived empathy and understanding, cultural sensitivity, accessibility, and direct comparisons with human moderators. Designed to detect demographic moderation effects.

This survey explores researcher experiences with reproducibility, transparency, and data quality in AI-moderated research. It serves as a replication study template to understand current practices, identify barriers, and assess the role of platform transparency in enabling reproducible AI-moderated studies.

A meta-research coding instrument for researchers to systematically evaluate and compare data quality from AI-moderated interviews versus traditional qualitative methods. Designed for repeated use across multiple data sets. Estimated completion time: 12-15 minutes per data set evaluated.

Pre-qualifies potential stakeholder interviewees by assessing role fit, recent domain involvement, and scheduling availability for 45–60 minute research sessions.

Measures consumer purchase frequency, channel preferences, decision drivers, and trade-offs in online shopping. Designed for ecommerce researchers and product teams seeking actionable shopper segmentation insights.

Segments online shoppers by purchase behavior, experience drivers, and communication channel preferences. Use this to build data-driven buyer personas and optimize channel strategy for e-commerce brands.

Segments financial app users by usage behavior, feature priorities, trust levels, and demographics to build data-driven personas and inform product strategy.

Maps the complete journey of a customer's most recent e-commerce session—from intent and actions to friction points and blockers—to diagnose conversion drop-off and prioritize UX improvements.