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 software development · PUBLIC RESEARCH BRIEF

GitHub CopilotWhich task earns enough trust for the next one?

GitHub positions Copilot across editors, the command line and GitHub, including code completion, chat, code review and agent-driven work. The useful adoption question is not whether an AI can produce code, but which bounded task gives a team enough reviewed evidence to delegate another task. No performance or quality result is assumed here.

Sources checked 2026-09-21 · 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 team piloting GitHub Copilot, would starting with explanations and test suggestions or with a small agent-assigned code change create stronger willingness to use it again? Use the same repository context and review every output against the team's normal quality and security process.

Set up this study →

What evidence would make a reviewer accept a GitHub Copilot-assisted pull request for routine work? Compare a proposed disclosure of the task, tests and changed files with the team's existing review view. Do not treat model confidence or a passing test alone as proof of correctness.

Set up this study →

When GitHub Copilot can use different models and agents, would a centrally approved task-and-model menu or developer choice within a spending limit produce a more workable pilot? Ask developers, managers and administrators separately; measure exceptions and outcomes rather than assuming one governance style is best.

Set up this study →

PROPOSED AUDIENCE

Who should weigh in?

North American software engineering teams at companies with 200 or more employees evaluating or expanding GitHub Copilot. Include individual developers, reviewers, engineering managers, platform administrators and security stakeholders within the same accounts. Recruit across familiar and unfamiliar codebases. Proposed roles only; account decisions should not be inferred from persona counts.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, select low-risk repository tasks with known acceptance tests and independent review. Compare completion, test coverage, defects found in review, rework and developer willingness to repeat. Keep suggestion acceptance, merged code and safely deployed behavior as different outcomes.

Longer term · 3–12 months

Over 3–12 months, follow use across maintenance, onboarding and feature work. Examine whether teams expand delegation only where review evidence remains strong, and whether costs or policy exceptions concentrate in particular workflows. More AI interactions alone would not establish productivity or quality.

What would make the result actionable?

Use authorized private repositories and the organization's existing code review, test and security controls. Predefine task complexity and compare with similar non-Copilot work where feasible. Capture license and metered usage, review time, escaped defects and rollback effort. Never publish proprietary code or prompts from the pilot.

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

Public sources

GitHub Copilot product, workflow and organization capabilities ↗Current product page; checked 2026-09-21