DocLang Aims to Become the Universal Language for AI-Ready Documents

For decades, enterprises have relied on document formats designed for human readers rather than AI systems. Contracts, invoices, reports, presentations, forms, and countless other business documents contain valuable information, yet extracting that knowledge for AI applications often requires complex processing pipelines that add cost, latency, and opportunities for error.

As organizations increasingly deploy generative AI and autonomous agents, that disconnect has become a growing challenge. To address it, ABBYY has joined IBM, NVIDIA (NVDA ), Red Hat, HumanSignal, and the Linux Foundation’s LF AI & Data Foundation in launching DocLang, a new open standard designed to create an AI-native representation of documents. Supporters of the initiative believe it could play a role similar to HTML’s standardization of web content, creating a common language that allows AI systems to understand documents more consistently and efficiently.

Why Documents Have Become an AI Problem

Most of the world’s business knowledge exists in formats such as PDFs, scanned images, spreadsheets, and presentations. While these formats work well for human consumption, they were never designed for machine understanding.

Humans can instantly recognize headings, tables, relationships between sections, and the significance of information based on its placement within a document. AI systems, however, often require multiple layers of OCR, layout analysis, document parsing, and post-processing before they can reliably interpret the same content.

This challenge becomes even more significant as organizations adopt AI agents capable of reasoning across large collections of enterprise data. Every document must first be transformed into a structured representation before it can be effectively used by language models, retrieval systems, or automated workflows.

The result is a fragmented ecosystem in which different tools often create their own document representations, making interoperability difficult and increasing the likelihood of inconsistencies.

How ABBYY Helped Shape the Vision

ABBYY has emerged as one of the key contributors behind the DocLang initiative. The company has spent decades developing document intelligence, OCR, and automation technologies, giving it a unique perspective on the challenges enterprises face when attempting to bridge the gap between traditional documents and modern AI systems.

According to Maxime Vermeir, Vice President of AI Strategy at ABBYY, the idea for DocLang grew out of conversations within the document AI community about the need for a common representation layer that could sit between raw documents and AI applications.

“DocLang is designed to solve one of the foundational problems in enterprise AI: documents were built for humans, not machines,” Vermeir explained.

Rather than forcing every AI system to independently interpret document layouts, tables, relationships, metadata, and structure, DocLang seeks to establish a standardized framework that can be shared across platforms and applications.

The goal is to make document understanding more reliable, reduce hallucinations caused by missing context, and lower the computational costs associated with repeatedly processing the same information.

What Exactly Is DocLang?

DocLang is an open specification for representing documents in a format specifically optimized for AI systems.

Unlike traditional formats that focus primarily on visual presentation, DocLang is designed to preserve multiple layers of information simultaneously, including:

• Semantic meaning
• Document structure and hierarchy
• Geometric layout and positioning
• Tables and complex document elements
• Metadata
• Governance and usage controls

This approach allows AI systems to understand not only what information exists within a document, but also how that information is organized and related.

For example, a value contained within a financial table carries meaning not only because of the number itself but because of its relationship to surrounding rows, columns, headings, and contextual information. Preserving those relationships in a standardized format can help AI systems reason more accurately about document content.

DocLang also incorporates governance controls that allow organizations to specify how document content may be used, including policies related to privacy, extraction, and AI model training.

The HTML Comparison

Supporters of the initiative frequently compare DocLang to HTML’s role in the evolution of the web.

Before HTML became widely adopted, there was no universal way for browsers to consistently interpret and display content. HTML introduced a common structure that allowed websites to be understood across different systems and platforms.

DocLang aims to bring a similar level of standardization to enterprise documents. Instead of every AI platform developing its own interpretation of document structure, a shared format could provide a common foundation for document understanding across the broader AI ecosystem.

As AI adoption accelerates, proponents argue that standardized document representations may become increasingly important for ensuring interoperability between models, applications, and autonomous agents.

How DocLang and Docling Work Together

The initiative also builds upon Docling, the open-source document processing toolkit originally developed by IBM Research Zurich and released as open source in 2024.

Docling focuses on document ingestion and conversion. It can process PDFs, Word documents, spreadsheets, presentations, HTML files, and images, transforming them into structured representations using advanced layout analysis and document understanding models.

DocLang complements that capability by providing a standardized format for representing and exchanging the structured output generated by tools such as Docling.

Together, the projects create a more complete document AI stack:

• Docling handles ingestion and document understanding
• DocLang provides a universal representation layer
• AI models and agents consume the resulting structured information

This separation helps reduce fragmentation while creating a common framework that different vendors and developers can adopt.

Why Open Standards Matter for Enterprise AI

As enterprise AI deployments move from experimentation to production, interoperability is becoming increasingly important.

Organizations rarely rely on a single AI model, document platform, or software vendor. Instead, they operate complex ecosystems that require information to move seamlessly between systems.

Open standards have historically played a critical role in enabling technology adoption by creating common frameworks that reduce integration complexity and vendor lock-in. Kubernetes helped standardize cloud-native infrastructure, while HTML became the foundation of the modern web.

DocLang’s backers believe AI-native document standards could serve a similar function for document intelligence and agentic AI workflows.

Looking Ahead

The AI industry has invested enormous effort into teaching machines how to interpret documents that were never designed for machine consumption. DocLang represents an attempt to address that challenge at its source by creating a document language built specifically for AI.

If successful, the initiative could help improve document interpretation, reduce hallucinations caused by missing structural context, lower processing costs, and make it easier for AI systems to exchange information across platforms.

At a time when organizations are increasingly relying on AI agents to navigate vast collections of business knowledge, standardizing how documents are represented may prove just as important as advancing the models themselves. For ABBYY and its collaborators, DocLang is an effort to build the foundation that could make that future possible.