Documents#
PDF and Office documents are parsed inside your environment. Body text, tables, headers, and embedded images are all inputs to detection — a phone number in a footnote and an ID card photographed into an appendix are treated with the same seriousness as the main text. Tokenization preserves the document's structure, so the model can still summarize, compare, and extract from it usefully, as described in Context Preservation.
Images and scans#
Standalone images and scanned pages go through OCR for embedded text and through visual detection for non-text elements — faces, signatures, identity-document regions, stamps — where those classes are configured. The full modality list and its boundaries live in Visual Detection.
Audio#
Recordings are transcribed inside your environment, and the transcript enters the same detection and tokenization pipeline as typed text. What reaches the external model is the tokenized transcript — the audio itself is not sent to answer-writing providers.
The honest boundaries#
Supported file formats, audio languages, and visual detection classes depend on the configured models and policy — they should be confirmed per deployment rather than assumed. And file handling follows the same conservative failure semantics as everything else: content in governed classes that cannot be processed is held or blocked per policy, never passed through unexamined.