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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Customer research repository · PUBLIC RESEARCH BRIEF

DovetailWhich evidence trail helps a stakeholder trust an insight without losing the source context?

Dovetail currently describes projects that connect raw research data, highlights and insights, plus AI-assisted summaries and answers that indicate AI contribution and trace outputs back to source material. This brief studies evidence navigation and provenance comprehension; it does not treat an AI-generated summary as a validated finding.

Updated 2026-10-07 · Simulation results not yet generated

Test a question in Gather ↗Explore fictional demo →

CHANGE ONE THING. LEARN WHAT MATTERS.

Three questions for the GTM team.

For a Dovetail insight, would an inline evidence map or a compact source drawer better help a stakeholder see which highlights support each statement before sharing it?

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When Dovetail AI contributes to a summary, would a sentence-level provenance cue or a single contribution badge better calibrate trust without overwhelming the reader?

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Before merging tags in a research taxonomy, would an affected-insights preview or a before-and-after search simulation better help a repository manager detect lost meaning?

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PROPOSED AUDIENCE

Who should weigh in?

North American research, product, design, customer-experience and insights teams using or evaluating Dovetail, including researchers who import and synthesize data, repository managers who maintain access and stakeholders who consume insights. Use fictional research material only. Proposed audience; no participant transcript or private customer evidence is included.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, test fictional projects containing interviews, survey responses, highlights, tags and AI-assisted summaries. Measure source retrieval, unsupported generalization, provenance comprehension and taxonomy-change errors. Use no real participant data.

Longer term · 3–12 months

Over 3–12 months, follow consenting teams in sandbox or de-identified repositories as contributors, projects and taxonomies evolve. Examine evidence reuse, stale insights, provenance loss and repository maintenance. Any claim about decision quality or research impact requires observed downstream outcomes.

What would make the result actionable?

Use versioned Dovetail documentation, synthetic transcripts and survey responses with a hidden evidence graph, tagged highlights, deliberately mixed-quality summaries and retrieval tasks. Blind reviewers to the intended answer, audit permissions, record AI and human edits separately, and require researcher and privacy review.

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

About Dovetail

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