last deploy · 2026.05.29 · cd57f71
Extract Structured User Personas from Writing Samples
Paste any writing. Long text is automatically split into chunks and analyzed incrementally.
Every system that personalizes needs a model of the person it serves. Today, most persona work is manual — PMs read transcripts, researchers tag surveys, marketers build segments from demographics. The actual voice of the user — how they think, decide, communicate — gets lost in aggregation. This tool extracts behavioral signals directly from writing: communication style, decision-making patterns, values, expertise markers, and interaction preferences.
Demographics tell you who someone is. Writing tells you how they think. The delta between these two is where personalization actually lives. This extractor captures the cognitive fingerprint that traditional persona methods miss — across 15 behavioral dimensions that evolve over time.
Customer intelligence from support tickets and feedback. User research automation from communication archives. Personalized AI interactions grounded in real behavioral data. Coaching and development insights from how leaders communicate across different contexts and time periods.
The incremental update pattern — analyze new writing, merge with existing persona, track what shifted — is the same architecture production recommendation engines use. Local-first storage solves the compliance problem that kills most personalization initiatives before they launch.