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 planning & AI workflows · PUBLIC RESEARCH BRIEF

MiroDoes the team trust the workflow—or just the first output?

Miro describes Sidekicks as context-aware AI agents and Flows as visual, multi-step workflows that teams can build, run and share. It also describes connectors that can bring data from tools such as Slack and Jira onto the canvas. This creates a research question about whether teams can inspect, adapt and repeat an AI-assisted process rather than merely accepting one polished output.

Sources checked 2026-09-22 · 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 the same approved planning task in Miro, would a visible multi-step Flow or a direct Sidekick request give cross-functional reviewers more confidence in how the output was produced? Hold the source material constant and test whether participants can identify inputs, edits and unresolved assumptions.

Set up this study →

When a team starts an AI-assisted process in Miro, would a prebuilt Playbook or a lightly customized Flow help more people complete the process correctly on a second project? Separate first-use appeal from repeatability and measure where the team departs from the prescribed steps.

Set up this study →

For a Miro workflow that uses connected Slack or Jira context, would showing the source beside the generated canvas output or presenting a cleaner result without source detail better support an approval decision? Use only authorized test data and score evidence recognition, not stated trust alone.

Set up this study →

PROPOSED AUDIENCE

Who should weigh in?

North American product operations, product management, design, research and marketing teams at companies with 200 or more employees that already use a collaborative canvas or are evaluating Miro AI. Include facilitators, contributors, reviewers, workspace administrators and security stakeholders. Proposed roles only; no adoption rate is implied.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, run matched tasks with company-approved, non-sensitive materials. Record correct completion, unsupported claims, reviewer edits, time to an accepted artifact and whether a different teammate can rerun the workflow. A faster draft is not success if the final decision record is incomplete or inaccurate.

Longer term · 3–12 months

Over 3–12 months, follow whether teams reuse, fork, abandon or maintain Flows across projects. Track stale connectors, permission failures, exceptions, ownership and rework when source systems change. Continued Miro activity alone would not prove that the workflow improved decisions.

What would make the result actionable?

Use consented workspace telemetry, version history and independent rubric-based review of the final artifacts. Compare roles, task complexity and prior Miro experience. Quantifying savings requires observed labor, licensing, governance and correction costs; simulated preferences cannot establish productivity or financial impact.

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

Public sources

Miro AI Sidekicks, Flows, connectors and collaborative workflows ↗Current product page; checked 2026-09-22