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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Workplace communication & enterprise search · PUBLIC RESEARCH BRIEF

SlackWhich Slack catch-up turns context into the right next decision?

Slack's current AI page describes channel and thread summaries, daily recaps, enterprise search across conversations and connected apps, and automations inside Slack. This brief studies whether a catch-up artifact helps a team identify an accountable next decision without flattening uncertainty or moving work into an ungoverned channel.

Sources checked 2026-09-24 · 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.

After a participant misses a day in a busy Slack project channel, would a chronological recap or a decision-and-owner recap help them identify the correct next action with fewer false assumptions? Use authorized synthetic or sanitized channel content and score against a facilitator-reviewed record.

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When Slack enterprise search returns an answer from messages and connected apps, would source snippets beside each claim or a concise answer followed by expandable sources produce more accurate follow-through? Preserve source permissions and include an appropriate 'not enough evidence' outcome.

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For a recurring Slack handoff, would an automated workflow that requests structured fields or a human-written thread summary create a more complete transfer into the system of record? Test a reversible process and measure missing context, duplicate work and corrections.

Set up this study →

PROPOSED AUDIENCE

Who should weigh in?

North American knowledge-work teams using paid Slack plans across product, sales, customer success or operations, including people returning after time away, managers reviewing multiple channels and employees with different access permissions. Recruit authorized adult employees only. Proposed audience; no productivity or adoption result is implied.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, run controlled catch-up, search and handoff tasks with synthetic or approved sanitized workspace content. Measure correct next-action identification, source checking, permission-respecting retrieval, handoff completeness, correction rate and notification burden. Faster reading is not success if participants act on a wrong summary.

Longer term · 3–12 months

Over 3–12 months, follow approved teams using a stable set of channels and workflows. Examine search reliance, stale summaries, duplicated systems of record, unresolved decisions, workflow maintenance, access changes and whether teams still verify consequential claims. Productivity needs observed work outcomes and cannot be inferred from self-reported time saved.

What would make the result actionable?

Use current workspace configuration, documented permissions, authorized messages or synthetic equivalents and an independently reviewed decision log. Exclude secrets and unnecessary personal content, test automations in a safe channel and require human approval before external or consequential actions. Any productivity or quality claim needs observed baseline and outcome data.

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

Public sources

Slack AI features, enterprise search, summaries and security overview ↗Current product page; checked 2026-09-24