What problem does it solve? Qualitative research produced by AI analysis is hard to trust: tags, dictionary terms, and findings may not trace back to real transcript lines, and there is no easy way for a person to check the work or record corrections. Stu solves this by launching a local web app that lets a human verify every claim against its source line and records each human edit against a named identity. ## Core Features & Use Cases - Traceability Explorer: Launches a local web app where every tag, dictionary term, and finding links back to the exact transcript line it came from. - Human Decision Queue: Reports what is waiting on a person — terms and findings sitting at proposed, untagged lines, count mismatches, and thin evidence — pulled from BigQuery before handoff. - Attributed Edits: Records every human edit in edit_log against a verified identity (Buzz pubkey or email), while the agent itself never edits or approves anything. - Use Case: After Claire ingests and analyzes interview transcripts, Stu launches the explorer, hands the reviewer a localhost URL, and summarizes which proposed terms and findings need a human decision. ## Quick Start Ask Stu to launch the traceability explorer for your research project and report which proposed terms and findings are waiting for review.