Linux Foundation Standard

Ship your AI projects on time, on budget

Introducing DocLang: an open document standard to eliminate the document bottleneck that delays every AI project - no custom engineering required.

Explore the standard

Created by:

The opportunity

Stop carrying the cost of unstructured data

Most enterprise data lives in documents, and getting it out is the expensive, brittle part of every AI project. Fewer than 10% of enterprises have scaled AI agents to real value, and 80% blame data limitations, not the model.*

DocLang relieves that burden: one open standard for representing any document — its structure, meaning, and layout — so your data flows cleanly into any downstream system, whether AI models, agents, analytics, or compliance. Build once, connect everything.

80%

of enterprises blame data limitations and not the model for stalled AI projects*

3–6 months*

of delay per project due to custom document engineering and parsing

What is DocLang

The AI-native document standard

DocLang is an open specification for how documents are represented in a machine-readable format optimized for AI consumption. Like JSON for data or HTML for the web, any vendor can implement it. Your team picks the tools that best fit your needs—knowing they all speak the same language.

Every document processed to DocLang standard carries explicit semantic meaning, maintains geometric accuracy, and includes embedded governance metadata. Your AI models get clean, structured data. Your compliance team has built-in audit trails. Your engineering team focuses on features, not parsing logic.

AI-native capabilities

Document structure optimized for model consumption
Semantic roles and reading order explicitly encoded
Tables, figures, and relationships preserved
Governance and compliance metadata embedded
Works with any document source or format

Why DocLang matters

Three strategic advantages for your AI roadmap

Faster time to production

Eliminate the document pre-processing bottleneck that delays every AI project. DocLang-compliant tools give you structured, consumption-ready data without custom engineering. Deploy AI pipelines weeks faster, not months.

Outcome: Ship 3x faster than custom-built approaches

Meet compliance by default

Governance rules and compliance metadata travel with every document. PII detection, extraction constraints, and audit trails are built-in—no external policy layers, no stripped metadata, no audit gaps. Compliance becomes automatic.

Outcome: Achieve audit-ready pipelines automatically

Open standard. No vendor lock-in

Governed by the Linux Foundation, not any vendor. Adopt the standard, not a proprietary platform. You don't have to reinvent document structure for every new tool, vendor, or AI initiative. The standard is free, open, and yours to build on.

Outcome: Stop reinventing the wheel with every AI project

In practice

Early results

Organizations piloting DocLang-compliant document processing are already reporting faster deployment cycles, fewer manual touchpoints, and reduced engineering overhead compared to custom-built approaches. You can explore the validated metrics on our benchmark page.

As adoption grows across financial services, healthcare, and logistics, we'll be sharing detailed case studies. If you're implementing DocLang today, we'd love to include your results.

Become an early adopter

Governance & Trust

Created and governed responsibly

DocLang was developed by organizations with 30+ years of document intelligence expertise. It is governed by the Linux Foundation, ensuring the standard remains open, transparent, and vendor-neutral.

Vendor-neutral governance

The Linux Foundation ensures no single vendor controls the roadmap. The working group—IBM, NVIDIA, Red Hat, ABBYY—proposes changes; the foundation approves. The community shapes the standard.

Security & compliance built-in

DocLang embeds data governance directly in the specification. PII detection, usage constraints, and compliance metadata are first-class citizens, not afterthoughts.

Open and transparent

The specification, reference implementations, and working group processes are all public. Contribute via GitHub, join working group sessions, shape the future.

* McKinsey & Company. Building the foundations for agentic AI at scale, 2026.

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