Our Story
AI-Native Document Intelligence
Document Mesh was created to solve layout-aware document extraction. We started with large language models, graph compilations, and high-DPI visual tokenizers to bridge the gap between unstructured documents (Word, PDF, Spreadsheets, etc.) and structured relational databases.
Mission
Unlocking Bounded Document Semantics
Documents are spatial and hierarchical artifacts. Traditional extraction pipelines discard this structure, flattening text stream inputs and triggering hallucinations. Document Mesh restores layout context, ensuring every extracted field holds absolute layout grounding and pixel provenance.
We build the open-source pipeline modules to parse, vectorize, validate, and search unstructured portfolios, turning static document repositories into high-fidelity relational graphs.
Our Vision
"A world where no enterprise loses value or incurs compliance risks because critical obligations remained buried inside unstructured layouts."
— Document Mesh Project Team
Values
Our Core Principles
Precision Grounding
Unstructured document extraction is a high-stakes task. We believe in visual-token coordination that maps text blocks to layout coordinates, avoiding structural loss in document tables.
Defensible Provenance
Every parsed field must be auditable. Surfacing bounding boxes and token-level logprobs ensures developers and reviewers can immediately verify the extraction source.
VPC-Native By Design
Enterprise documents belong in your secure boundary. Document Mesh is built for isolated private networks, avoiding external api dependencies and enforcing regional KMS encryption.
Audience
Built for Grounded Data Workflows
Document Engineers
Software teams building structural data extraction flows from raw unstructured documents, needing layout-aware vector embeddings, custom JSON validation, and clean metadata mappings.
Compliance & Risk Teams
Enterprise risk officers requiring strict audit trails, pixel-level provenance verifications, and deterministic obligation-graph tracking to flag exposure patterns.
Legal Operations
Legal ops groups automating cross-agreement citations, binding definitions, and tracking liability/renewal schedules in an connected graph schema.
Architecture
Engineering Principles
Calibrated Confidence
No silent extraction failures. Low-confidence token sequences automatically route to human reviewers, while high-confidence values sync programmatically.
Deterministic Graph compilation
Coreference term anchoring and citation dependency mapping resolve sections and amendments into a single traversable relational network.
Tenant Isolation
Customer files remain fully isolated at the storage and compute layer, running locally or VPC-natively with zero external data retention policy.
Extensible Data Syncs
Sync embeddings to vector databases (pgvector, Qdrant) and relational networks straight to graph databases (Neo4j) to empower hybrid search.
