1) Ingestion
Defluff supports batch and incremental processing of media and documents, enabling both back-catalog conversion and ongoing updates.
Designed for clarity, governance, and scale.
Defluff ingests content, segments it, extracts meaning into structured Knowledge Cards, assembles curated Journeys, delivers experiences across web/kiosk/embed, and measures engagement to continuously improve both curation and content quality.
2) Understanding Layer
Defluff performs multi-step extraction: segmentation, topic modeling, entity resolution, claim detection, definition extraction, and takeaway generation—paired with confidence signals.
3) Knowledge Layer
Structured Knowledge Cards are stored with relationships, enabling cross-collection intelligence and reuse across exhibits or curricula.
4) Experience Layer
Journeys are delivered through a branded UI layer (web/kiosk/embed) with personalization rules.
5) Measurement Layer
Defluff captures segment-level engagement, journey completion, replays, drop-offs, and cohort comparisons to inform continuous improvement.
Trust, Safety, and Governance
Explainability
Defluff is designed to show "what was extracted" and "where it came from," so your team can audit, edit, and approve.
Human-in-the-loop
Defluff supports review workflows so editorial teams can approve outputs, lock canonical definitions, and create institution-grade narratives.
Provenance and traceability
Defluff can retain linkable provenance references to support trust and institutional accountability.
Audience modes
Defluff can support strict modes for education and youth contexts, enabling stronger filtering and curated experiences.
Brand and tone controls
Defluff can align output style with institutional language, reading level, and terminology.