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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Enterprise knowledge & AI search · PUBLIC RESEARCH BRIEF

NotionWill people trust the answer enough to stop asking around?

Notion describes Enterprise Search across a workspace and connected apps, with source citations and controls over search scope. Its help documentation also tells users to check AI answers for accuracy. This brief asks what would turn a retrieved answer into a useful work decision, rather than treating an AI response as proof of correctness.

Sources checked 2026-09-19 · 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 Notion Enterprise Search, would showing the answer first or emphasizing its cited source first make a marketing or product teammate more willing to use it for a handoff? These are proposed presentation tests, not claims about a shipped interface. Keep the answer and source material identical.

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Would a Notion Enterprise Search pilot focused on one recurring handoff, such as finding the approved product message, be more credible than a general search-everything demonstration? Ask end users what they would verify and administrators which sources would have to be connected.

Set up this study →

What would make a team continue using Notion Enterprise Search after it encounters a stale or incomplete answer? Compare proposed practices for source ownership and escalation to a colleague. Do not assume the product automatically corrects the underlying document.

Set up this study →

PROPOSED AUDIENCE

Who should weigh in?

North American marketing, product, knowledge-management and IT teams at companies with 200 or more employees whose work spans multiple information tools. Include new hires, experienced subject-matter experts, content owners and administrators from the same accounts. Proposed research audience only; no claim about the prevalence of knowledge gaps.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, recruit paired end users and content owners to complete realistic lookup tasks with an agreed source of truth. Compare successful task completion, citation checking and the need to ask a colleague. Measure answer correctness separately from how confident the user feels.

Longer term · 3–12 months

Over 3–12 months, follow repeat use as staff, documents and permissions change. Test whether maintaining clear source ownership makes the service useful after the initial demonstration. More search activity alone would not establish better decisions or less duplicated work.

What would make the result actionable?

Build a permission-appropriate evaluation set from documents supplied by a consenting pilot team. Have subject-matter experts assess answers and sources. Track task outcomes, verification time, escalations, stale-document fixes and connector coverage by account; never publish the underlying private documents in this brief.

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

Public sources

Notion Enterprise Search product overview ↗Current product page; checked 2026-09-19

Notion help: Enterprise Search, citations and source controls ↗Current product page; checked 2026-09-19