Factify Secures $73M Seed Round to Replace PDFs with Intelligent Documents for the AI Era

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Tel Aviv-based Factify emerged from stealth with $73 million in seed funding to reimagine digital documents beyond static PDFs. The startup is building intelligent documents with built-in identity, access rules, and version history that can update themselves and serve as verifiable sources of truth for both humans and AI systems.

Factify Secures Historic Seed Round for PDF Replacement

Tel Aviv-based startup Factify emerged from stealth with a $73 million seed funding round led by Valley Capital Partners, marking one of the largest seed investments ever completed

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. The ambitious mission: to replace antiquated PDF files and standard document formats like .docx with intelligent documents designed for the AI era. The round attracted notable backers including John Giannandrea, former head of AI at Google and senior vice president of AI at Apple, Ken Moelis, founder of investment bank Moelis & Co., and Peter Brown, CEO of hedge fund Renaissance

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Founder and CEO Matan Gavish, a tenured professor of computer science at Hebrew University of Jerusalem with a Stanford PhD, has been fixated on this problem for over a decade. "The PDF was developed when I was in elementary school," Gavish told VentureBeat. "The bedrock of the software ecosystem hasn't really evolved... someone has to redesign the digital document itself"

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Source: SiliconANGLE

Source: SiliconANGLE

Building a New Digital Document Standard for AI Systems

Factify's core innovation centers on what it calls "document-as-infrastructure," where every file carries its own identity, access rules, version history, and programmable intelligence

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. Unlike static PDFs that remain frozen in time, Factified documents function as living, evolving entities capable of updating themselves and enforcing governance directly. Each document maintains a permanent audit log that records meaningful events affecting its contents, ensuring it can serve as a single source of truth for both humans and machines.

Source: VentureBeat

Source: VentureBeat

The problem Factify addresses is substantial. Adobe estimates there are over three trillion PDFs in circulation globally, yet this technology has barely evolved since its creation over 30 years ago

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. When AI models attempt to read PDFs, they essentially guess, using optical character recognition to scrape text from what functions as a digital photo. This lack of structured data creates significant limitations as businesses increasingly rely on AI systems to review, approve, and take actions based on document contents.

Machine-Readable Documents That Enforce Their Own Governance

Factified documents address the fundamental trust problem plaguing current formats. Because PDFs have no way to verify information or help users identify the most authoritative version, they cannot be trusted in an environment where AI agents need verifiable data sources

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. Gavish's solution embeds intelligence directly into documents, allowing them to be queried through ChatGPT-style interfaces and support workflows like approvals, signatures, redactions, and compliance checks without external tools.

The new document infrastructure maintains control even after files leave an organization's systems. Unlike PDFs that drift into uncontrolled versions once sent externally, Factified documents remain managed wherever they go. Access to specific sections can be approved or blocked even after transmission, and governance is enforced directly by the document itself, granting access only to approved users

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Targeting Regulated Industries Where Ambiguity Carries High Costs

Factify initially targets document-heavy regulated industries including banking, insurance, legal services, and human resources, where the cost of ambiguity runs high

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. Early adopters in legal sectors use Factified documents to enforce nondisclosure agreements before granting access and limit visibility to specific sections based on user approval status. Human resources teams run onboarding and approval processes directly inside documents, eliminating fragmented workflows spanning email threads, shared folders, and disconnected platforms.

Gavish acknowledges the challenge of displacing a format entrenched in business infrastructure for three decades. His strategy focuses on specific use cases within medium-sized companies of hundreds or thousands of employees, examining needs across industries like law, marketing, finance, and real estate

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. The approach recognizes that PDF documents largely constitute the contractual infrastructure of the modern economy.

Building Documents as Dynamic and Verifiable Infrastructure

The shift from static files to intelligent documents represents more than a software upgrade. Gavish describes it as building a new foundation that enables software investments to compound over time. "We don't just want to solve the inadequacies of the PDF," he explained. "We want to do it in a way that creates a bedrock for post-AI business"

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The technical challenge requires building multiple layers from scratch: a new format, a data layer, and user experience interface applications. Creating connected documents that support changes and maintain version history while remaining machine-readable demands fundamental architectural innovation

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. Documents must function like websites or browsers, with dynamic content, buttons, and user interfaces, while retaining the authority and permanence businesses require from contracts and agreements.

For organizations moving toward workflow automation and AI-driven processes, the ability to trust document authenticity becomes critical. Factify's documents for the AI era address this by ensuring every file retains its identity, audit trail, and purpose throughout its lifecycle. Instead of copying files across platforms and updating them manually, organizations can link everything to a single, authoritative document that remains verifiable and current.

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