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-assisted enterprise content · PUBLIC RESEARCH BRIEF

BoxWhich enterprise-content answer is safe enough to reuse?

Box's current AI page describes permission-aware queries across documents, summaries and content generation, extraction into structured metadata, and agents that can locate files and create deliverables. This brief studies when an answer, extracted field or draft is sufficiently sourced for reuse without mistaking fluent output for governed knowledge.

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 question spanning several Box documents, would an answer with inline citations or a ranked file list help a reviewer reach the more accurate conclusion? Include conflicting, stale and insufficient source material and score appropriate refusal.

Set up this study →

When Box AI Extract proposes metadata from contracts, forms or images, would field-level evidence or document-level confidence produce better reviewer corrections before data enters a downstream workflow? Use synthetic or approved de-identified files.

Set up this study →

For a Box Agent draft assembled from enterprise content, would a fact table with source links before the narrative or a polished narrative with expandable evidence better prevent unsupported reuse? Require a content owner to approve every external-facing claim.

Set up this study →

PROPOSED AUDIENCE

Who should weigh in?

North American legal-operations, finance, marketing, procurement or professional-services teams evaluating or using Box AI with document-heavy workflows. Include content owners, frequent searchers, reviewers, administrators and employees with different permission scopes. Recruit authorized adult employees. Proposed audience; no accuracy or efficiency outcome is implied.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, run controlled query, extraction and drafting tasks against an authorized, versioned document set. Measure factual accuracy, citation coverage, stale-source detection, field corrections, permission-safe retrieval and appropriate no-answer decisions. Do not publish drafts or update production metadata.

Longer term · 3–12 months

Over 3–12 months, follow approved workflows as files, permissions and models change. Examine correction rates, wrong-version use, metadata drift, access revocation, reviewer effort and downstream reuse of unsupported claims. Efficiency or risk reduction requires observed outcomes and should include administration and correction costs.

What would make the result actionable?

Use a frozen source corpus, current access controls, synthetic or de-identified content, field-level ground truth and independent review. Exclude secrets and unnecessary personal data, verify inherited permissions and require human approval before sharing or structured-data updates. Simulated confidence is not evidence of factual accuracy or governance.

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

Public sources

Box AI overview for content queries, extraction, generation and governed agents ↗Current product page; checked 2026-09-25