Production-Ready Document Processing

Extend provides document processing infrastructure, tooling, and APIs that enable technical teams to handle complex documents with state-of-the-art accuracy and reliability.

Companies like Chime, Brex, Flatiron Health, Opendoor, and Checkr use Extend for mission-critical document pipelines to achieve > 95% accuracy on their hardest docs, and go live in days (not months).

Processing complex documents is hard. You have to deal with:

• Difficult layout elements like tables, charts, handwriting, and signatures
• Lengthy documents needing chunking and merging strategies
• Extracting structured data with > 95% accuracy across a variety of input formats
• Optimizing outputs for LLM readability
• Ensuring accuracy and reliability in production for critical use cases

There are plenty of OCR and document parsing options on the market. However, we noticed customers still struggling to deploy complex use cases when accuracy requirements are high (> 95%). This is because OCR and parsing is only one part of the problem, and real world use cases need to bridge the gap between raw outputs and production-ready data.

Extend uniquely solves this by unifying models, infrastructure, and tooling into a single platform for end to end document processing.

This includes:

• Process any document format with state-of-the-art parsing powered by VLMs and OCR
• Capture precise data with multi-step extraction powered by semantic chunking, bounding boxes, and citations
• Tackle the most complex use cases with processing modes for document parsing, classification, extraction, and splitting
• Deploy faster with low code tooling that empowers your entire team to quickly iterate, review results, and improve accuracy
• Continuously improve results with fine-tuning pipelines that turn reviewed corrections —> custom models

For example, Extend's built-in evaluation tools to help you benchmark performance, improve accuracy, and deploy with confidence.

Our customizable document splitter identifies each distinct section and their relationships according to your instructions. This is critical when ingesting lengthy documents (or multi-document packages).

Here's an example of zero-shot table extraction on a complex healthcare doc. What enables this is vision models specialized for tabular structures, working alongside our bounding box citations & human-in-the-loop review system.