How the AI Readiness Standard works
The AI Readiness Standard is a practical, measurable framework for the age of generative search. As ChatGPT, Perplexity, Claude and Google AI Overviews become primary discovery channels, websites must be engineered not only for human visitors but for machine comprehension and trustworthy citation. Sumly AI Readiness turns that vague requirement into a concrete 0–100 score across six weighted dimensions.
Every check is automated and reproducible. The analyzer crawls your homepage, robots.txt and sitemap, parses your structured data, measures content depth, evaluates your citation potential, probes agent-facing endpoints such as OpenAPI and MCP, and verifies trust signals such as HTTPS and contact information. The result is a transparent report you can share and act on immediately.
The six dimensions
AI Crawlability (20%). Can AI crawlers find your pages at all? We verify robots.txt reachability, sitemap declaration, and explicit Allow rules for GPTBot, ClaudeBot, Google-Extended, Bytespider and KimiBot.
AI Understanding (25%). Can AI build an accurate entity model of your organization? We detect Organization, Product, FAQPage, Article and Breadcrumb structured data, and measure how many entity fields you expose.
AI Content Readiness (20%). Is your content deep and structured enough to be useful? We score homepage depth and check for About, Product, FAQ and Blog or case-study pages.
AI Citation Potential (20%). Would an AI confidently cite you as an authoritative source? We evaluate article depth, author attribution, brand consistency and external trust signals.
Agent Readiness (10%). Can autonomous agents integrate with you? We check for an OpenAPI descriptor, a Model Context Protocol endpoint, an AI Actions manifest and developer documentation.
Trust & Security (5%). We verify HTTPS, visible contact information and a healthy HTTP status.
Why it matters in 2026
Traditional search relied on backlinks and keywords. Generative AI search instead cites sources that are machine-readable, well-structured and clearly attributed. Studies show that structured data and explicit AI-crawler policies materially increase the probability of being cited in AI-generated answers, and that sites blocked from AI crawlers simply disappear from the answers their customers rely on.
Our recommendations are built around the standards that matter most today: Schema.org JSON-LD, the llms.txt specification, OpenAPI 3.0, the Model Context Protocol, robots.txt AI-rule hygiene and semantic HTML. Each recommendation ships with copy-ready code so you can deploy the fix in minutes, not weeks.
Methodology
Each dimension is scored from detailed, machine-verifiable findings — 30 findings in total. Scores are weighted to reflect how strongly each factor influences AI discovery and citation. The total is normalized to a 0–100 scale with a grade from Poor to Excellent. The full list of checks and the scoring rubric are visible inside every public report, so the result is never a black box.
We encourage you to check your own site first, then run the same standard against your competitors. If your site earns 90 or above, you are demonstrably ahead of most of the web in AI readiness. If it scores lower, your report tells you exactly what to fix, in priority order.
From crawlability to agent integration
The journey from a generic website to a genuinely AI-ready platform usually follows a clear path. First, make sure AI crawlers can reach you: publish a robots.txt that explicitly allows GPTBot, ClaudeBot, Bytespider, PerplexityBot and Google-Extended, and declare your sitemap. Second, give AI engines structured facts about your organization using Schema.org JSON-LD, including name, description, brand, contact details, founding date, industry and the services you provide. Third, make your content deep enough to be worth citing — substantial paragraphs, clear headings and honest, well-argued claims with author attribution.
Fourth, publish the machine interfaces that agents need: an OpenAPI 3.0 descriptor at /openapi.json, a Model Context Protocol manifest at /.well-known/mcp.json and an AI Actions manifest at /.well-known/ai-plugin.json. Finally, keep the fundamentals solid: HTTPS everywhere, visible contact information and a fast, healthy site. Each of these steps maps directly onto a dimension in the standard, and each one is scored transparently in your report.
The llms.txt signal
In addition to the six scored dimensions, the standard reports on the emerging llms.txt convention. Serving a plain-text llms.txt file at your root gives language models a concise, human-readable summary of your site, its key pages and its canonical sources. When present, it dramatically lowers the cost for a model to understand what you do and why your content should be cited. Add an llms.txt and an llms-full.txt describing your homepage, products, FAQ and insights, and keep them synchronized with your real content.
Semantic HTML and readable structure
Markup is meaning. AI models that parse your HTML rely on semantic tags to separate navigation from content, headers from body copy and lists from prose. Use a single <h1> per page, a clear <h2>/<h3> hierarchy, and semantic landmarks such as <header>, <main>, <article> and <footer>. Avoid stacking everything inside generic <div> elements. This is not an aesthetic choice — it is the difference between a page a model can read and a page a model has to guess about.
Case studies and real-world signals
The most citable websites are the ones that show evidence. Publish case studies, benchmarks, technical write-ups and first-hand results; attribute them to named authors; and keep brand naming consistent everywhere. Link to authoritative external sources such as standards bodies and research that support your claims. These signals tell an AI that your content is original, accountable and part of a larger trustworthy ecosystem — the exact combination that drives citations in generative answers.
Start today, for free
There is no reason to guess about your AI readiness. Run a check on your own domain, review the six dimension scores, and follow the recommendations that the report prioritizes for you. Re-run the check after each deployment to watch your score climb. Because the standard is public and reproducible, you can also benchmark yourself against competitors, track progress over time and share a credible, third-party-verifiable score with your team, your clients or your board.