Semantic layer for files

In business intelligence, a semantic layer sits between raw data and end users, providing a shared vocabulary and agreed-upon definitions. Looker's LookML and dbt's semantic models are examples: they define "revenue" consistently across different underlying tables so every analyst sees the same meaning. A semantic layer for files applies the same principle: it sits inside the file and defines the meaning of its content for downstream consumers — AI models, RAG pipelines, or human users.

BI analogy

In BI, the semantic layer solves the "single source of truth" problem: without it, different teams define "customer" and "revenue" in conflicting ways. Looker and dbt centralize that definition. For files, the problem is the same: without embedded semantic metadata, every system that touches a file might extract different facts. By embedding descriptions, entities, structure, and transcriptions inside the file, a semantic layer ensures that all downstream consumers see consistent meaning.

What's inside

LLMind's semantic layer for files includes five layers: a human-readable description, extracted entities (people, locations, organizations), document structure (headings, sections, tables), machine-readable transcription (OCR output), and lineage (source, transformations, license). Each layer is pre-computed by specialized tools — OCR engines, vision models, language models — so downstream AI tools can skip re-parsing and jump straight to semantic reasoning.