We do not just summarize research, we preserve the full evidence chain from transcript to quote to theme to actionable insight. Teams can inspect, edit, and trust the output instead of treating AI as a black box. With workspace-wide search, project snapshots, and hypothesis-driven analysis, research becomes reusable institutional memory that compounds over time.
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Maker
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We kept seeing the same gap in research workflows: teams were running interviews, but the value got lost between raw transcripts and actual decisions. We wanted to build a system that could turn messy qualitative data into something structured, usable, and easy to trust.
The problem was not collecting research, it was making sense of it fast enough for teams to act on. Most tools stop at transcription or summarization. We wanted to solve the harder problem of helping teams move from interviews to evidence-backed insights and decisions.
We started by focusing on AI-generated synthesis, but quickly realized speed alone was not enough. We shifted toward an evidence-first approach: keeping the source material attached, making outputs editable, and building evaluation loops so quality could improve over time.