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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Consumer AI assistant & personalization · PUBLIC RESEARCH BRIEF

Google GeminiWhich Gemini control makes personalization feel useful and understood?

Google's current Gemini help materials describe personalization based on past chats, connected Google app content or activity and user instructions. They also describe custom Gems built with instructions, optional files and a preview before saving. This brief tests comprehension and control, not answer accuracy or productivity impact.

Updated 2026-09-29 · 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 enabling Gemini personalization, would a preview of the context categories or a just-in-time explanation at first use better help people understand what can shape a response? Use synthetic history and no connected personal data.

Set up this study →

When creating a custom Gem, would structured fields for role, task, context and format or a conversational setup flow make the intended behavior easier to review? Use fictional files and do not save the Gem.

Set up this study →

In a personalized Gemini response, would visible context chips or an expandable context drawer better help users verify which connected source types influenced the answer? Do not connect an account or evaluate high-stakes advice.

Set up this study →

PROPOSED AUDIENCE

Who should weigh in?

North American adult Gemini users with personal Google Accounts who vary in AI familiarity, connected-app use and willingness to personalize an assistant. Exclude supervised accounts and high-stakes medical, legal or financial tasks. Proposed audience; no adoption, productivity or retention outcome is implied.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, test personalization, Gem creation and context-disclosure prototypes with synthetic chats, instructions and files. Measure control comprehension, mistaken data assumptions, editability and task completion. Do not connect accounts, upload personal files or save settings.

Longer term · 3–12 months

Over 3–12 months, follow approved adult cohorts as instructions, connected apps and assistant use change. Examine trust calibration, stale personalization, context surprises, control discovery and recovery after an unwanted response. Adoption or productivity claims require observed behavior and controls.

What would make the result actionable?

Use synthetic accounts and content, versioned feature descriptions and known-correct context provenance. Randomize interface treatment while holding the task and response constant; include unavailable and no-personalization cases; test accessibility and require privacy and safety review before live use.

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

About Google Gemini

Visit the company website ↗