FOR AI PLATFORMS
Stop scraping HTML and guessing at structure. ARW provides clean, machine-readable content that reduces errors, lowers costs, and improves user satisfaction.
Current web scraping is inefficient, error-prone, and expensive. LLMs waste context on navigation, ads, and irrelevant content. Parsing HTML introduces errors and hallucinations.
Structured content, legal clarity, and lower operational costs.
Machine views (.llm.md) provide structured content without HTML overhead. Hierarchical discovery means loading only relevant content.
Structured markdown eliminates parsing ambiguity. Real-time data in machine views prevents stale information.
Hierarchical navigation with metadata enables progressive refinement instead of scanning everything.
Machine-readable policies specify training/inference permissions and attribution requirements. Reduces liability risk and provides clear usage terms.
85% token reduction translates directly to lower API costs. Faster responses mean better resource utilization and user experience.
Actions enable complete workflows through agents. Users can purchase, book, and transact without leaving the conversation.
ARW implements patterns proven in academic research on LLM navigation and retrieval.
Research: "LLM-Guided Hierarchical Retrieval" (arXiv:2510.13217)
Hierarchical approaches with progressive disclosure achieve superior token efficiency vs. flat files while maintaining accuracy.
Research: Nay, 2024
Structured content relationships enable agents to understand context and navigate intelligently.
Research: arXiv:2412.15235
Schema.org integration provides semantic grounding that improves retrieval quality and reduces hallucinations.
Example: User asks agent to "find and buy wireless keyboard under $150"
ARW uses standard HTTP, Markdown, OAuth, and Schema.org—technologies you already support.
/llms.txt (YAML manifest).llm.md filesJoin the movement toward a machine-readable web. ARW reduces costs, improves accuracy, and enables complete user experiences through agents.