Extend is an NYC-based operator of internet extension document tools intended to provide powered workflows inside existing tools. The company specializes in reducing context switching, eliminating the need for copy and pasting, increasing the capacity of existing tools, and automating ticket workflows, enabling clients to customize websites without hiring a salesforce administrator.
Extend was funded with $17 million led by Innovation Endeavors, with participation from Y Combinator, Homebrew, Character, and angel investors, including Scott Belsky (former Chief Strategy Officer of Adobe) and Guillermo Rauch (CEO of Vercel).
Revolutionizing Document Intelligence
Historically, companies relied on legacy OCR systems and fragmented integrations to extract data from PDFs, scans, handwriting, and tables—but accuracy often suffers, and pipeline development can drag on for months. Extend positions itself as a full-stack document processing cloud, combining advanced LLM-powered parsing, precise extraction, pipeline orchestration, schema generation, and human-in-the-loop tools—all under one umbrella.
The startup claims 95%+ accuracy across a variety of document types, even degraded or handwritten content, and is already cash-flow positive with multi-million-dollar annual recurring revenue—surpassing all prior seed funding before closing this latest round.
“We’ve seen remarkable momentum over the past year, and this funding allows us to double down on our mission of helping ambitious teams unlock the full potential of their unstructured documents,” said Kushal Byatnal, co-founder and CEO.
Further highlighting their strength in the market:
Pedro Franceschi, CEO of Brex, reported Extend “outperformed every solution we tested—including other vendors, open source and even foundation models” and now supports document workflows for Brex’s 30,000 users.
Eli Badgio, co-founder and CTO, noted that the platform reduces time-to-production from months to just days, thanks to its LLM foundation and robust developer and operator tooling.
Davis Treybig, Partner at Innovation Endeavors, praised Extend’s “full-stack approach,” which empowers teams to both automate existing workflows and build innovative internal features for a competitive advantage.
Two New Features: Sandbox Mode & Automated Config
With this round closed, Extend is rolling out two significant enhancements:
Sandbox Mode: a self-serve trial environment enabling developers and business users to upload documents and test workflows immediately—no setup required.
Automated Config Generation (beta): eliminates manual schema configuration and prompts engineering by auto-generating custom schemas from sample documents. Future updates will add a computerized loop that continuously refines configuration based on evaluation feedback.
In Tehrani.com’s coverage, the feature rollout further underscores Extend’s advantage, as it surfaces as a 95%+ accurate, LLM-driven platform capable of processing complex PDFs into structured, production-ready data.
A Strong Roster of Clients
Extend claims adoption by major brands across industries—including Brex, Square, Checkr, Flatiron Health, and numerous Fortune 500 companies—that rely on the platform to manage millions of documents daily with high accuracy.
These clients span sectors where data precision is mission-critical, including fintech, healthcare, logistics, insurance, and more, all seeking reliable transformation from unstructured sources to validated data.
Market Context: The Document Cloud Frontier
While cloud platforms for computing, storage, and collaboration are commonplace, Extend touts itself as a pioneer in the largely untapped document processing cloud space—a specialized layer built to handle the complexity of unstructured enterprise data at scale.
As enterprises accelerate digital transformation, scrappy startups and incumbents alike are betting heavily on AI to tame messy data assets. Extend’s seamless tie-ins—schema-free ingestion, automated pipelines, and pilot-to-production tooling—position it for a potential breakout in a rising market.
By: K. Tagura
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