What is synthetic research?

Synthetic research uses AI-powered personas that simulate how real buyers, customers, or market segments would respond to your research questions. It produces directional signal — insights to sharpen your hypotheses — before you invest in real primary fieldwork. Gather Synthetic runs AI-moderated interviews across any audience archetype in minutes, not weeks.

How is synthetic research different from real research?

Synthetic research generates directional signal using AI personas, while real primary research collects data from actual people. Synthetic pre-research is faster (minutes vs. weeks), cheaper, and ideal for hypothesis generation and stakeholder alignment. It is not statistically valid and should be used to focus and prioritize real fieldwork, not replace it. Gather offers both synthetic pre-research and real AI-moderated interviews with actual respondents.

What types of research can Gather Synthetic run?

Gather Synthetic supports 14 study types: Brand Perception, Product Feedback, Concept Testing, Competitive Intelligence, CX/NPS Deep Dive, Messaging & Claims Testing, Win/Loss Analysis, Market Segmentation, Pricing Research, Ad & Creative Testing, Customer Journey Mapping, Feature Prioritization, Churn & Retention Analysis, and Audience Discovery.

How much does synthetic research cost?

The first 5 synthetic interviews are free. After that, B2C audience interviews cost $0.50 each and B2B audience interviews cost $5.00 each. There is no subscription required.

How accurate is synthetic research?

Synthetic research is directional, not statistically accurate. Predictive accuracy and a statistical interval are only shown when established for the specific result. Treat findings as educated hypotheses to validate with real primary research, not as definitive measurements.

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AI-powered collective conversations · PUBLIC RESEARCH BRIEF

RemeshCan group consensus surface agreement without hiding minority insight?

Remesh describes live and asynchronous online conversations in which participants respond in their own words and can vote on others’ responses, while AI surfaces themes and patterns during and after a session. Voting can reveal broad agreement, but consensus may also obscure rare needs, subgroup conflict or ideas that appear late.

Updated 2026-10-28 · Simulation results not yet generated

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CHANGE ONE THING. LEARN WHAT MATTERS.

Three questions for the GTM team.

For Remesh, when should participant voting be interpreted as broad agreement, and what sample, timing and subgroup checks should be required before reporting consensus?

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How should a moderator protect a low-frequency but consequential response from being buried when the group prefers a more familiar explanation?

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When live AI surfaces a theme, what evidence should determine whether the moderator probes it immediately, waits for more responses or treats it as an analytical suggestion only?

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PROPOSED AUDIENCE

Who should weigh in?

North American market-research, consumer-insights, brand, innovation and employee-listening teams evaluating Remesh. Include moderators, discussion-guide designers, analysts, sample owners and stakeholders who act on consensus measures. Proposed audience only; no Remesh session, participant response, vote, theme, transcript or private organization data is included.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, run synthetic live and asynchronous conversations with seeded majority views, late-emerging ideas, subgroup conflict and coordinated voting. Compare moderation choices, consensus summaries and the recovery of consequential minority perspectives.

Longer term · 3–12 months

Over 3–12 months, vary group composition, anonymity, topic sensitivity, session format and voting order. Study conformity, repeat-participant effects, moderator adaptation and whether themes replicate across independent groups.

What would make the result actionable?

Retain response and vote timestamps, subgroup membership, exposure order and moderation interventions. Report distributions alongside ranked ideas, review low-frequency responses deliberately and replicate important conclusions in an independent sample or complementary method.

A Gather simulation returns hypothetical customer reactions. Quantifying revenue, traffic or retention needs actual business inputs and validation against observed behavior.

About Remesh

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