The normalization pipeline operates in three stages. Stage 1 (extraction) uses tool-specific parsers to read each format — ComfyUI PNG text chunks, Midjourney Discord messages, Stable Diffusion plaintext parameters. Stage 2 (mapping) translates tool-specific fields into canonical equivalents — "prompt" means different things structurally in each tool but refers to the same concept. Stage 3 (enrichment) adds derived information: content hashes, embeddings, session boundaries.
The key architectural benefit is that downstream systems (search, lineage tracking, compliance export) only need to understand the canonical schema. Adding support for a new AI tool requires only a new Stage 1 extractor, not changes to every downstream consumer.
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Numonic automatically captures provenance, preserves metadata, and makes every AI-generated asset searchable and reproducible.