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Start with a runnable example

The public User Intuition examples repository demonstrates three workflows using TypeScript. Your application controls the customer experience and interpretation; User Intuition provides research execution and evidence access.
Requires Node.js 22.18 or later. Default fixture mode makes no network calls and requires no API key or spending. Every fixture is fictional; it demonstrates data handling, not measured research performance.
The report and search examples were checked against the 2026-10-05 public OpenAPI and staging reads. The API search guide documents the released contract separately from the runnable example.

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Conduct a study

The example separates draft creation, Customize Plan turns, saved-plan review, a recruitment estimate, paid launch, monitoring, and report generation. A plan may require several conversational turns; relay questions rather than inventing answers. Save the study ID to resume. The user chooses panel recruitment or their own participants. Before paid recruitment, review the current saved plan and full estimate. The example requires explicit live mode and a separate launch approval flag. That flag is not a backend budget limit or an idempotency guarantee. Run the study example and fetch its source

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Retrieve study results

Study results expose Study Findings, Participant Responses, Participant Profiles, and Recommended Next Steps. Findings and response summaries are analysis; exact quotations are source evidence; profiles describe the sample; recommendations propose further work. Keep source references, interview IDs, coverage, and freshness with the report. An interview-level link does not necessarily identify the exact passage behind an answer. The example demonstrates answer-to-reference relationships and checks fictional quotations against their source messages. Retrieval does not regenerate analysis. Run the results example and fetch its source

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Search existing research

Search locates relevant study plans and structured report content across authorized studies. The API ranks candidates and returns only canonical persisted JSON. Inspect each entry in studies, including its index_status, report IDs, grouped results, dates, call IDs, and finding reference IDs before using the evidence. Recommendations are distinct from observed evidence. A search over summaries does not prove that all transcript passages were searched. Empty results are different from an API failure, and repeated matches from one interview are not independent participants. Your agent performs the final synthesis. Run the search example or read the API search guide.

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Connect through MCP or CLI

Use the MCP setup guide for authentication and current tool discovery. The examples repository includes a matching MCP and CLI walkthrough. REST and tool schemas may differ; use the contract for the surface you call.

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Verify before production use

Run fixture tests and the release-schema check, then validate the narrow live workflow your integration needs with an authorized test study. Do not publish real participant data in a reproduction. A timed-out write can leave an uncertain outcome; inspect persisted state before retrying. The release compatibility record covers response shapes, citations, pagination, coverage, errors, and source access. Each API endpoint page contains selectable request examples with the valid fields and values for that operation; start with Create Study.

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Machine discovery

The example catalog lists commands, inputs, outputs, side effects, and raw source URLs. Individual example pages contain complete code blocks generated from the executable source files.