Extend - Turn documents into high quality data

What

Extend is a document processing platform for turning unstructured files into usable data. It provides APIs to parse documents into LLM-ready markdown, extract structured fields into defined schemas, split multi-document files, classify documents, and detect or fill form fields programmatically.

The product appears aimed at AI teams, engineering teams, and organizations building production document workflows, especially where accuracy, scale, and layout complexity matter. Its positioning is a production-ready document intelligence layer that combines layout detection, specialized vision models, workflow tooling, and evaluation features so teams can move from raw PDFs to operational pipelines more quickly.

Features

• Document parsing to markdown: Converts unstructured documents into LLM-ready markdown, which helps downstream AI and search workflows consume content more reliably.
• Schema-based data extraction: Extracts structured data into user-defined schemas, making it easier to standardize information from varied document formats.
• Document splitting and classification: Segments multi-document files and assigns documents to predefined categories, which supports intake automation and routing.
• Advanced layout detection: Detects tables, checkboxes, images, handwriting, and signatures on each page, improving handling of complex real-world documents.
• Performance mode controls: Offers modes optimized for speed, cost, or accuracy, allowing teams to tune processing behavior to specific operational needs.
• Workflow, eval, and review tooling: Includes confidence scoring, a multi-pass review agent, orchestration workflows, and a Studio interface for schema iteration and regression testing.

Helpful Tips

• Prioritize a representative document set during evaluation, since products like this are most useful when tested against the hardest layouts, edge cases, and language variations in your workflow.
• Use schema design carefully; extraction quality often depends as much on well-scoped field definitions and validation logic as on the underlying model quality.
• Match processing mode to workload: low-latency settings may fit real-time intake, while accuracy-focused modes are better for sensitive or exception-heavy documents.
• Build human review around confidence scoring for high-risk use cases, especially early in deployment when failure patterns are still being discovered.
• If deployment environment matters, verify whether cloud or self-hosted operation best fits your data handling requirements and internal infrastructure model.