Synthetic Users

Generates realistic synthetic interview transcripts by accepting research briefs, target persona definitions, and interview question sets, then using LLM-based conversation simulation to produce multi-turn dialogue that mimics natural human interview flow. The system likely uses prompt engineering with persona context injection and conversation history management to maintain coherence across interview exchanges, enabling researchers to produce dozens of interview transcripts in hours rather than weeks of manual recruitment.

Generates synthetic survey responses at scale by accepting survey question sets and target demographic parameters, then using LLM inference to produce realistic response distributions that match specified population characteristics. The system models response patterns across multiple respondents to create statistically plausible datasets, enabling researchers to run analysis workflows on synthetic data before deploying real surveys.

Provides a centralized workspace where distributed research teams can collaboratively review synthetic interview transcripts and survey data, annotate findings, synthesize insights, and iterate on research questions without managing scattered documents or email threads. The system likely uses real-time collaboration primitives (shared document editing, comment threads, version history) combined with research-specific affordances like transcript tagging, insight extraction, and finding aggregation.

Enables researchers to refine research questions and interview prompts based on initial synthetic data by accepting feedback on generated responses and automatically adjusting persona definitions, question framing, or interview flow. The system uses iterative LLM prompting where researcher annotations and insights feed back into the prompt engineering pipeline to generate more targeted synthetic data in subsequent rounds.

Automatically extracts key insights, themes, and patterns from synthetic interview transcripts and survey responses using NLP-based thematic coding and summarization. The system likely uses LLM-based extraction to identify recurring themes, pain points, feature requests, and sentiment patterns across multiple synthetic transcripts, then aggregates findings into structured insight reports with supporting quotes and frequency counts.

Provides a free tier that allows researchers to generate a limited number of synthetic interviews and surveys per month (likely 10-50 transcripts/responses) before requiring paid subscription. The system implements quota tracking and enforcement at the API level, enabling teams to validate the synthetic research approach and workflow before committing budget, with clear upgrade paths to higher generation limits.

Generates synthetic interviews where each respondent maintains consistent persona characteristics (demographics, values, behaviors, communication style) across multiple interview turns, creating realistic dialogue that reflects how a specific person would respond to follow-up questions. The system likely uses persona context injection and conversation history management to ensure responses remain coherent and in-character throughout the interview.

Enables researchers to define initial hypotheses, generate synthetic data to test them, and track how hypotheses evolved or were validated/invalidated through research iterations. The system likely maintains a hypothesis registry with links to supporting synthetic data, researcher annotations, and findings, creating an audit trail of research reasoning and decision-making.