HeyMarvin

By automating the most time-consuming aspects of research transcription, tagging, theme identification, and report generation Marvin compresses weeks of traditional research cycles into hours.

The platform runs AI-moderated interviews in 40+ languages, conducting structured conversations with research participants, capturing live sessions with automatic transcription, and analysing responses immediately after completion.

This removes the scheduling, moderation, and post-processing overhead from qualitative research at scale allowing teams to gather insights from hundreds of participants with the same rigour and consistency as a manually moderated session.

Marvin's qualitative data analysis engine automatically quantifies qualitative inputs: themes are grouped automatically, sentiment is measured across all responses, key patterns and emerging trends are identified across the full dataset, and structured reports with cited findings are generated without researcher intervention.

Deep Research takes this further as an automated analysis mode that processes an entire project's data and produces custom structured reports a set-it-and-forget-it research synthesis capability.

The research repository functions as an institutional memory for customer knowledge: all research data interviews, survey responses, user feedback, support conversations, and sales call recordings is stored, tagged, and searchable in one place.

An AI research assistant answers questions across the entire repository with point-level citations, making existing research reusable rather than siloed in individual researchers' notes or forgotten after each project.