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. Confidence scores reflect internal AI response consistency, not statistical significance. Quantitative projections carry a ±15–20% margin of error. Treat findings as educated hypotheses to validate with real primary research, not as definitive measurements.

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Product development planning & execution · PUBLIC RESEARCH BRIEF

LinearWhich product signal deserves a place in the roadmap?

Linear's current features page describes work across the product-development cycle, including projects and initiatives, issue and cycle planning, AI-powered workflows, analytics, customer requests and workplace intake. This brief studies how a team turns scattered demand into a justified roadmap decision without treating issue volume as customer importance.

Sources checked 2026-09-26 · 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.

When a customer request enters Linear, would preserving the original request beside a normalized problem statement or showing only a concise issue summary help product teams make the more evidence-based prioritization decision? Use synthetic or approved de-identified requests.

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When scope changes during a Linear cycle, would an exception-focused update or a complete issue-by-issue status view help stakeholders identify the right intervention without overreacting to ordinary movement? Hold the underlying project state constant.

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For a proposed Linear roadmap item, would evidence grouped by customer problem or by account and request count lead decision-makers to test the more consequential assumption first? Include duplicate, contradictory and low-context requests.

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PROPOSED AUDIENCE

Who should weigh in?

North American software product teams evaluating or using Linear across product management, engineering, design and go-to-market. Include product managers, engineering leads, customer-facing request submitters, analysts and executives who consume roadmap updates. Recruit authorized adult employees. Proposed audience; no delivery-speed or adoption outcome is implied.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, run controlled intake, cycle-review and roadmap tasks with synthetic or approved de-identified requests, fixed project data and documented decision criteria. Measure evidence inspection, duplicate detection, correct escalation, prioritization consistency and revisions after missing context appears. Do not equate issue count with customer value.

Longer term · 3–12 months

Over 3–12 months, follow approved teams as projects, cycles and request sources change. Examine roadmap reversals, stale requests, duplicate issues, scope drift, stakeholder understanding and maintenance of intake rules. Velocity or customer-impact claims require observed delivery and outcome data across comparable work.

What would make the result actionable?

Use a versioned request set, explicit decision criteria, current workspace permissions and independent adjudication of duplicates and missing evidence. Remove unnecessary customer identifiers, restrict AI and automation permissions and require human owners for roadmap changes. Simulated preference is not proof of product demand or delivery impact.

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

Public sources

Linear features for planning, building, AI, insights and customer requests ↗Current product page; checked 2026-09-26