Document intelligence
Meibel turns complex documents into AI-ready context: structure preserved, confidence scored, every answer traceable to its source.










Why you need meibel
OCR gives you characters. Chunking gives you fragments.
Neither gives you the structure that decides whether the answer is right.
Table structure is flattened into text. Rows, columns, and relationships between values disappear. A table rendered as a paragraph is useless for any downstream query.
A 600-page legal document contains covenants that reference other covenants. None of that resolves when files are processed in isolation.
COAs come in thousands of formats from various suppliers. The fields that matter (batch number, test method, result, specification) vary by document. Generic extraction misses domain-specific fields entirely.

Most platforms stop at extraction. Document Intelligence starts there.
Document Intelligence does not treat documents as bags of text. It understands structure: headers scope sections, captions describe tables, footnotes relate to claims, charts contain data that is not in the text. Every element gets the processing path it needs.

Document Intelligence does not stop at the boundary of one file. When your documents reference each other, cite each other, or share structure, those connections are extracted and made traversable. Your document estate becomes a connected knowledge base, not a filing cabinet.

A single PDF can produce text chunks for semantic search, tables for SQL queries, metadata for filtering, and citations for graph traversal. All three retrieval modes are available simultaneously. An agent can find relevant content by meaning, query precise values from extracted tables, and follow citation chains to referenced documents in a single reasoning step.

Use Cases
One data corpus. Multiple experiences. Meibel lets you process your data once and build as many solutions as you need on top, without reprocessing or rebuilding your pipeline.
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We tore down a 168-page fabrication spec. See exactly where extraction failed and how to build document AI that understands tables and graphs across the whole corpus.



Drop in any file. The system detects the format, analyzes layout and reading order, and processes every element according to its type - automatically, with no configuration needed.
25+ formats supported: PDF, DOCX, PPTX, HTML, images, emails, spreadsheets, JSON, CSV, archives, and more
Layout understood: headers, paragraphs, tables, images, lists, captions, and their spatial relationships
Every element processed correctly: tables stay structured, handwriting gets read, mixed content handled automatically

Every extracted element links back to its exact location in the source document, and every field is pulled according to what the document actually is - not a generic template.
Bounding box coordinates identify the precise region on the precise page
Seven built-in metadata models: insurance, legal, medical, manufacturing, construction, bibliography, and custom
Custom extraction schemas can be defined per data source, industry, or workflow

Every extraction is scored across multiple independent dimensions: Coherence, Completeness, Correctness, Faithfulness, Relevance, and OCR Confidence.
High-confidence outputs move forward.
Low-confidence or critical fields trigger retry, review, or escalation.




What Our Clients Say







Try it live
Document Intelligence is free to start. Upload a document and see what production-grade document understanding looks like.



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