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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Secure data sharing & collaboration · PUBLIC RESEARCH BRIEF

SnowflakeWhat makes a data collaboration safe enough for both sides to start?

Snowflake documentation says Secure Data Sharing lets providers expose selected database objects to other accounts without copying or transferring the underlying data. It describes read-only imported databases, provider-controlled access and several sharing options, including listings, direct shares, data exchanges and clean rooms. This brief studies how a provider and consumer reach a correct setup decision; it does not certify a collaboration as compliant.

Sources checked 2026-09-23 · 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 proposed Snowflake collaboration, would a small authorized sample with an explicit object-and-role policy or an architecture overview alone help provider and consumer teams reach a more accurate go, revise or stop decision? Score understanding of data scope, access and accountability.

Set up this study →

Would choosing among a Direct Share, Listing, Data Exchange and Data Clean Room from a use-case decision guide produce a more appropriate design than starting from the most familiar feature? Use current documentation and allow the answer to be that the collaboration should not proceed.

Set up this study →

Before a consumer creates a database from a Snowflake share, would a provider-authored data contract or an interactive preview of schemas, freshness and permitted uses better expose missing assumptions? Use non-sensitive test objects and capture unresolved governance questions.

Set up this study →

PROPOSED AUDIENCE

Who should weigh in?

North American data platform, analytics, governance and business teams planning an internal or partner data collaboration on Snowflake. Include provider and consumer roles, security or privacy reviewers and teams with different levels of Snowflake experience. Use authorized professionals and non-sensitive test data. Proposed audience; no compliance, revenue or adoption outcome is implied.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, run paired provider-consumer design sessions using synthetic or approved non-sensitive datasets. Measure accurate understanding of shared objects, access roles, permitted uses, freshness, revocation and expected compute responsibility, then review the proposed configuration independently. Do not share production data during the study.

Longer term · 3–12 months

Over 3–12 months, follow approved collaborations through onboarding and governed use. Examine access changes, stale or misunderstood data, policy exceptions, query adoption, support burden, revocation tests and whether the chosen sharing pattern still fits. Business value and compliance require observed usage, controls and legal review; they cannot be inferred from workshop preference.

What would make the result actionable?

Use current Snowflake documentation, actual account and regional constraints, approved test objects, data-owner permission and security or privacy review. Verify grants and revocation in an isolated environment before any production step. Never expose private customer data in a public brief or simulation, and do not label a design compliant without the relevant accountable reviewers.

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

Public sources

Snowflake documentation: About Secure Data Sharing ↗Current product page; checked 2026-09-23