By any measure, the problem Extend AI is tackling is colossal. As much as 80% of enterprise data resides in unstructured formats: PDFs, scanned images, handwritten forms. These documents are the backbone of every industry, from healthcare to finance, but remain opaque to machines. Extracting value from them has long been a costly and slow affair. Extend believes it can change that, and it’s raised $17 million in seed and Series A funding to prove it.
Led by Innovation Endeavors, and joined by Y Combinator, Homebrew, and angels like Adobe’s former CSO Scott Belsky and Vercel’s CEO Guillermo Rauch, Extend is positioning itself not just as another document automation tool, but as the full-stack cloud for document processing.
“Documents are often the system of record for mission-critical business data,” says Extend co-founder and CEO Kushal Byatnal. “But reliably parsing and using that data has always been a challenge. We built Extend to change that, delivering state-of-the-art accuracy out of the box, and enabling teams to go from raw PDFs to production-ready data in days instead of months.”
Solving the Document Problem
In industries where even 1% error is unacceptable (think: healthcare reimbursement claims or KYC verification) document automation has long required expensive, in-house R&D efforts. Teams have had to hand-stitch pipelines involving OCR engines, manual prompt engineering, regex heuristics, and human-in-the-loop validation, often for marginal gains.
Extend’s value proposition is this: Instead of handing teams an OCR tool or a standalone model, it offers an entire infrastructure stack. It’s plug-and-play for any technical team facing messy documents at scale.
Pedro Franceschi, CEO of Brex, underscored this performance edge: “Extend outperformed every solution we tested, including other vendors, open source, and even foundation models. It now powers key document workflows across our 30,000 customers.”
Beyond “GPT Wrappers”: Context Is the Product
Extend is often dismissed, like many startups in the space, as just a “GPT wrapper.” But Byatnal is vocal about how that misses the point.
“Most people still think building an AI product means building a better model. But the best products today don’t own the model-they wrap it in the best context and UX,” he wrote in a recent LinkedIn post.
In practical terms, this means Extend uses large language models (LLMs) to extract data and prepare it with carefully engineered context: multimodal parsing that preserves document layout, semantic segmentation for long files, memory systems for few-shot examples, and orchestration to route documents through classification, extraction, and human review.
It’s not about being a “thin wrapper,” Byatnal argues, it’s about wrapping the models in deep context and intuitive UX that mirrors how teams actually work. Extend leans into adaptability and full-stack developer integration, with customers including Square, Checkr, Flatiron Health, and multiple Fortune 500 companies.
Extend’s recent launch included two major enhancements: automated configuration generation and sandbox mode. The former allows teams to upload a few sample documents and receive a tailored schema, dramatically reducing time spent tuning prompts and data fields. The latter offers immediate hands-on access, crucial for technical teams who want to validate accuracy before buying.
These features reinforce the platform’s mission: eliminate every bottleneck between teams and their unstructured data.
A Growing Market, With Plenty of Competition
With $17 million in fresh capital, Extend plans to double down on engineering and go-to-market efforts. It’s already cash-flow positive and hit multi-millions in annual recurring revenue before raising, making it an anomaly among early-stage AI startups.
The market opportunity is massive. Docling (open-source), ABBYY (legacy enterprise), and Mistral (high-speed enterprise AI) are among the many tackling the space, but Extend’s full-stack flexibility could set it apart. Most are focused on enterprise deployments; Extend, like Mistral, is winning favor with fast-moving AI-native teams who value speed to production, high accuracy, and customization.
From Here? Automation to Agency
Extend’s roadmap hints at AI agents that not only parse documents, but optimize workflows in real time, run self-improvement loops, and learn from user feedback, essentially becoming a virtual back-office workforce.
“The world has cloud platforms for storage, compute, and collaboration,” Byatnal says. “But until now, no one has built a true cloud for document processing… That’s what we’re building at Extend.”
