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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AI orchestration & workflow automation · PUBLIC RESEARCH BRIEF

ZapierWhich automated action deserves trust first?

Zapier's current AI page describes connecting AI to business tools, building agents that take actions, applying checks to sensitive inputs, and governing access through managed authentication, audit logs and policy controls. This brief studies which low-risk action a team should automate first and what approval boundary makes expansion credible.

Sources checked 2026-09-25 · 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 routine Zapier workflow, would the first AI step only classify and route an item or also draft the downstream update create the better balance of usefulness and review burden? Use synthetic tickets or requests and prohibit external sends.

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Before a Zapier agent acts across connected apps, would a field-by-field approval preview or an exception-only review queue help authorized staff catch more harmful mistakes? Include incomplete, duplicate and sensitive inputs.

Set up this study →

When an automation fails or produces uncertainty, would a visible stop-and-escalate state or an automatic retry with logged context lead to safer recovery? Test under controlled app errors and compare duplicate actions, lost context and operator understanding.

Set up this study →

PROPOSED AUDIENCE

Who should weigh in?

North American small-business and enterprise operations, IT, revenue-operations or support teams evaluating or using Zapier for cross-application AI workflows. Include workflow builders, process owners, frontline users, approvers and administrators. Recruit authorized adult employees. Proposed audience; no automation, productivity or revenue outcome is implied.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, pilot one reversible internal workflow with synthetic or approved de-identified records, sandbox credentials and explicit action restrictions. Measure classification accuracy, reviewer edits, duplicate or missed actions, safe escalation, recovery quality and oversight burden. Do not send external messages or alter production systems.

Longer term · 3–12 months

Over 3–12 months, follow approved workflows as apps, owners and policies change. Examine credential and permission drift, silent failures, duplicate actions, exception backlog, shadow automations, audit-log use and maintenance cost. Productivity or revenue claims require observed downstream outcomes and comparable baselines.

What would make the result actionable?

Use current app and action inventory, least-privilege sandbox credentials, versioned rules, synthetic or de-identified inputs and independently reviewed outcomes. Log every attempted action, test failure modes and require human approval for external or consequential changes. Simulated willingness is not evidence that autonomous execution is safe or valuable.

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

Public sources

Zapier AI orchestration, agents, actions and governance overview ↗Current product page; checked 2026-09-25