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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Collaborative app building & operations · PUBLIC RESEARCH BRIEF

AirtableWhat helps a cross-functional team adopt one operating workflow?

Airtable's current platform page describes shared relational data, durable workflows, open surfaces, automations, interfaces, governance and agents working within permissions and an audit log. This brief studies how a team introduces one bounded operating workflow without recreating the spreadsheet sprawl it meant to replace.

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 cross-functional Airtable workflow, would an interface tailored to each role or a shared base view help occasional contributors update the right fields with fewer errors? Keep the underlying data and task set constant.

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During initial Airtable setup, would activating an automation immediately or waiting until ownership and exception rules are confirmed create fewer silent failures and manual workarounds? Test a reversible notification or routing flow in a sandbox.

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For leadership review, would a dashboard organized around exceptions or a complete status table produce better decisions without hiding stale or missing data? Include records with uncertain ownership and require participants to inspect source fields.

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PROPOSED AUDIENCE

Who should weigh in?

North American product, marketing, operations or program teams evaluating or using Airtable for a cross-functional workflow. Include builders, occasional contributors, approvers, reporting consumers and administrators with different data and permission needs. Recruit authorized adult employees. Proposed audience; no adoption or productivity result is implied.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, pilot one reversible workflow with synthetic or approved operational records, a documented schema and current access roles. Measure task completion, field accuracy, exception handling, automation corrections, duplicate tracking, source inspection and support burden. Do not treat logins or record volume as successful adoption.

Longer term · 3–12 months

Over 3–12 months, follow approved teams as fields, owners and connected tools change. Examine schema drift, shadow spreadsheets, broken automations, permission changes, unresolved exceptions, reporting trust and maintenance effort. Productivity or coordination claims require observed work outcomes and comparable baselines.

What would make the result actionable?

Use a versioned data dictionary, explicit owners, current permissions, sandboxed automations and independently reviewed task outcomes. Minimize personal and confidential data, preserve an audit trail and require human approval before external or consequential actions. Simulated preference does not establish durable adoption.

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

Public sources

Airtable platform overview for shared data, workflows, interfaces and governance ↗Current product page; checked 2026-09-25