Inside Minds: How Synthetic Research Panels Are Built

Minds is a synthetic market research platform for testing concepts, messaging, positioning, pricing, and audience reactions before teams commit to full fieldwork.

This page explains the public methodology behind Minds without exposing proprietary implementation details.

The Method in Brief

Minds follows one connected six-step workflow:

1. Write down the decision, hypotheses, and evidence threshold before generating responses.
2. Define and review a target Audience from existing Minds, an audience description, and permitted source material.
3. Separate observed source values, requested quotas, and explicit assumptions during grounding.
4. Run neutral qualitative, survey, or configured method questions in a Study.
5. Compare distributions, segments, reasons, contradictions, and individual responses rather than selecting only persuasive quotes.
6. Validate the findings against held-out human or behavioral evidence when the decision requires it.

The goal is not to invent a fictional average customer. The goal is to create a research-useful simulation of a defined audience segment, with enough context to respond consistently to concepts, claims, messages, and tradeoffs.

1. Audience Creation Starts With Evidence

Minds does not start from a single generic prompt like "act as a customer." Each research Audience is built around a defined population, use case, and decision context.

Audiences can use three product-supported inputs:

• Existing Minds: reuse personas already available to the workspace.
• Audience descriptions: describe the roles, segments, contexts, and differences the Audience should cover.
• Attached files or links: add permitted research notes, source material, or background documents relevant to the study.

The available inputs and creation modes depend on the workspace configuration. The researcher remains responsible for source rights, Study design, Audience review, and deciding what requires human validation.

For the public technical overview and boundaries of the source-modelling approach, see Minds PRISM.

2. Each Mind Uses Many Small Knowledge Bases

Minds are reusable AI personas created from descriptions, profiles, links, files, or research notes. They can be used across chat and Audience research workflows.

A pricing study, concept test, and positioning study may use the same audience label but require different source material and different validation criteria.

This structure lets an Audience behave less like one averaged persona and more like a set of individual respondents with overlapping but distinct beliefs, priorities, and objections.

3. Research Runs as Interviews, Surveys, and Group Discussions

Researchers use Minds across qualitative and quantitative synthetic-research workflows, including:

• Concept testing
• Message testing
• Positioning research
• Pricing and packaging reactions
• AI focus groups
• Audience and segment exploration
• Early qualitative probing before human fieldwork
• Single-choice, multiselect, scale, and open-text questions
• Configured methods such as MaxDiff, where enabled

A Mind can answer individually, join a Study readout, compare perspectives with other Minds, or respond to moderator follow-ups. Supported Study workflows can aggregate structured responses, compare segments, and retain individual responses for review. Method and artifact availability depends on the plan and workspace configuration.

For the underlying academic category, see our guide to silicon sampling. For the broader commercial workflow, see the synthetic research guide.

Validity Must Be Tested, Not Assumed

Synthetic research should be evaluated against relevant human or behavioral evidence. A single accuracy percentage would hide important differences between audiences, questions, stimuli, models, and scoring rules, so this page does not publish a universal Minds accuracy claim.

For a defensible validation study, define the task and success metric before running the synthetic Audience, keep a human comparison set separate, report agreement and disagreement by question, and disclose the Audience, inputs, model configuration, sample sizes, and limitations. Do not tune the workflow on the same human responses later presented as independent validation.

Minds should not be treated as a universal replacement for human research. Use human fieldwork when:

• The decision is high-stakes, regulated, legal, medical, political, or safety-critical.
• You need statistical population estimates with confidence intervals.
• You need to observe real behavior rather than stated preference.
• The audience is poorly represented in available data.
• The stimulus depends on physical, sensory, or in-store experience.
• Final validation must come from recruited human respondents.

The strongest workflow is usually hybrid: use Minds to explore the space, test more variations, refine the instrument, and narrow the options; then use human research for the final questions that need external validation.

External Registry Relationship Disclosure

For an inspectable view of the evidence—not a Minds-authored award—see the versioned Minds evidence dossier and the candidate benchmark preregistration.

Minds founded and funds the Synthetic Research Index. The Index discloses that conflict, records unresolved evidence checks, and publishes a Minds-specific recusal that prevents Minds personnel from approving Minds evidence, scores, disputes, or conclusions. The external reviewer seat is currently vacant, so these records should not be described as an independent certification, award, or completed benchmark.

Key Terms

Synthetic research uses AI-generated respondents or panels to simulate how a defined audience may react to questions, concepts, messages, and tradeoffs.

Silicon sampling is the academic method of conditioning large language models on respondent profiles, asking survey-style questions, and comparing the resulting distributions against human survey data.

Synthetic panel means a structured group of AI respondents built to represent a segment, audience, buying committee, user group, or market category.

AI persona usually means a single simulated respondent profile. In Minds, a Mind is more than a static persona: it is an interactive respondent grounded in many scoped knowledge bases.

AI focus group is a moderated session where multiple synthetic respondents react to the same prompt, concept, message, image, or product idea and surface agreement and disagreement.

What This Makes Possible

Traditional research is often rationed because recruitment is slow and budgets are finite. Minds changes the workflow by letting teams ask more questions earlier:

• Screen early concepts before paying for full fieldwork.
• Compare message options before media spend.
• Find objections before sales enablement or launch.
• Test positioning before rebuilding a deck or website.
• Rehearse qualitative interviews before recruiting humans.
• Turn prior research into an interactive panel that teams can query repeatedly.

This is why Minds should be used as a decision-support layer, not a replacement for rigor. The workflow is most valuable when it helps teams explore more options and focus human research on the questions that matter.

Suggested Citation

Minds. “How Minds Builds Synthetic Research Panels.” Updated July 31, 2026. https://getminds.ai/research/methodology

Further Reading

• Silicon Sampling: How LLMs Simulate Survey Responses
• Synthetic Research: The Complete 2026 Guide
• AI Focus Groups: How They Work
• The Spark Effect: Creative Diversity in Multi-Agent AI