Extend-AI

AI-native document processing platform with specialized LLMs for complex document parsing, extraction, and workflow automation.

Extend

Overview

Founded in 2023 by Eli Badgio and Kushal Byatnal, Extend-AI is a Y Combinator-backed document processing platform that raised $17 million across seed and Series A rounds led by Innovation Endeavors in June 2025. The company differentiates through specialized large language models and vision models built specifically for complex document formats, claiming 95%+ accuracy on tables, handwriting, and checkboxes. Their API-first approach serves technical teams at Chime, Brex, Flatiron Health, Opendoor, and Checkr for production document workflows.

The platform's adaptive learning system creates memory from processed documents to improve accuracy on similar files over time, while autonomous AI agents run experiments to optimize extraction schemas without manual intervention.

Key Features

Specialized Vision Models: Custom AI models for complex layouts including large tables, handwritten content, and form elements
Document API Suite: APIs for parsing, classification, data extraction, and document splitting
Adaptive Learning: Memory system that learns from processed documents to improve accuracy on similar files
Autonomous Optimization: AI agents that automatically optimize extraction schemas through experimentation
Markdown Conversion: Transform documents into high-quality markdown for LLM ingestion
Production Validation: Data validation layer ensuring extracted information meets quality thresholds

Use Cases

Financial Document Processing

Fintech companies process loan applications, bank statements, and identity documents, extracting account numbers, transaction histories, and personal information from varied formats while handling poor-quality scans and handwritten annotations.

Healthcare Records Extraction

Healthcare organizations automate medical record processing by extracting patient demographics, diagnoses, prescriptions, and lab results from clinical documents with checkboxes, tables, and physician handwriting.

Real Estate Transaction Automation

Real estate platforms process property documents including contracts, disclosures, and inspection reports, with the learning system improving extraction accuracy on similar property types and jurisdictions.

Technical Specifications

Feature	Specification
Core Technology	Specialized LLMs and vision models for documents
Claimed Accuracy	95%+ on complex documents
Capabilities	Parsing, classification, extraction, splitting, markdown conversion
Learning System	Adaptive memory improving on similar documents
Document Complexity	Tables, handwriting, checkboxes, messy layouts
Deployment	API-based cloud platform
Optimization	Autonomous schema optimization through AI agents
Integration	RESTful APIs for technical teams