Founding Members:
| Tier | Members |
|---|---|
| Steering | Arm, Ericsson, Google, Mastercard, Microsoft, Omron, OpenAI, Schneider Electric, Siemens |
| General | Armilla AI, Mitsubishi Electric, Nemko |
| Contributor | Naaia |
The Appia launch materials identify a broad cross-industry founding coalition, and Nemko separately described itself as a founding member of the initiative.
Appia addresses fragmented AI conformity, assurance, and trust practices across the global AI value chain. Organizations today face inconsistent approaches to demonstrating that their AI systems meet regulatory and consumer expectations. Appia aims to replace ad hoc methods with standardized, modular assessment infrastructure.
The initiative is intended to establish modular open specifications and standardized conformity assessment frameworks for AI models, systems, and applications across the AI value chain. The focus is on practical assessment infrastructure that can support AI conformity, assurance, and trust across jurisdictions and organizational roles.
Founding Members:
| Role | Members |
|---|---|
| Founders | IBM, NVIDIA, Red Hat |
| Contributors | ABBYY, HumanSignal |
Multiple sources list these five organizations as founding or contributing members, with some also crediting Forgis.
DocLang addresses the fact that many enterprise document formats (PDF, HTML, Markdown, LaTeX) were primarily designed for human rendering rather than AI-native processing. The working group's stated goal is to provide an interoperable document-processing standard for AI and agentic workflows.
DocLang is described as a constrained XML format built from the ground up for LLM tokenizers, with a 1-to-1 mapping between DocLang tokens and model tokens and minimal token count. Every component carries semantic role, geometric bounding box, and reading order.
The specification complements the open-source Docling project.
Both initiatives sit within the broader Linux Foundation ecosystem but address different layers of the AI stack, making them complementary rather than overlapping.
Layer of the AI stack they address:
How they connect:
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