AI Readability: The “Table Stakes” Check Every Website Needs in 2026

If machines can’t reliably crawl, understand, and trust your page, you won’t be visible, no matter how good your product is. That’s why we built an AI Readability Analysis tool: a technical “readability” audit for machines. It’s like technical SEO, expanded for the AI era, focused on how well systems can parse your structured data (JSON-LD), validate Schema.org markup, and connect entities across your site. This is foundational. Not a growth hack. Not a nice-to-have. Table stakes. What “AI readability” actually means When humans say a page is “readable,” they mean the writing is clear. When machines say a page is “readable,” they mean: **Crawlability & indexability** Can a bot fetch the page, see the content, and discover the rest of the site? **Structured understanding (Schema + Canonicals)** Can the bot identify what the page is about (product, brand, organisation, FAQ, etc.) and trust the canonical version? **Knowledge graph strength (JSON-LD graph tightness)** Do your entities connect cleanly—product → brand → organisation → offers → inventory → category—using consistent IDs so the machine can “remember” and reuse the info? Your tool output reflects exactly this: a single page can look “fine” to a human, yet still have gaps that make AI and search engines hesitant to surface it. Why this is now the foundation for both AI visibility and regular SEO **1) Search engines and AIs both run on extraction + trust** Traditional SEO still relies on crawling, indexing, and ranking. AI-based discovery relies on retrieval + synthesis. Different surface area, same prerequisite: the system has to extract clean facts and trust them. If your product page doesn’t explicitly provide a price, currency, availability, canonical, and brand/organisation context in machine-readable form, you’re forcing models to guess, or ignore. And machines don’t guess in your favour. **2) Rich results, Shopping, and “AI answers” need structured certainty** For eCommerce, structured data isn’t “extra credit.” It’s how you qualify for key placements: Product rich results (price, stock, ratings where applicable) Merchant / Shopping eligibility signals AI systems pulling “fact cards” for price, pack size, availability, and variants Retail copilots that compare items across brands When your schema is incomplete, you’re effectively telling the system: “I’m not sure what this is.” …and the system responds: “Cool, I won’t recommend it.” **3) AI amplifies the cost of ambiguity** In classic SEO, ambiguity might mean you rank #9 instead of #4. In AI discovery, ambiguity can mean you never get cited at all, because the model can’t confidently anchor a statement to your page. AI is less forgiving because it needs to produce an answer, not a list of 10 links. The common “AI readability” gaps (and why they matter) Here are the kinds of issues that show up constantly in audits—exactly the sort your tool highlights. Missing Offer schema (price, currency, availability) For product pages, Offer is the difference between “this is a product” and “this is a purchasable product right now.” At minimum, a product should provide: - offers.price - offers.priceCurrency - offers.availability (e.g., https://schema.org/InStock) - ideally offers.url and offers.itemCondition Without this, systems struggle to show pricing confidently, which reduces eligibility for Shopping-style placements and AI comparisons. Missing Organisation schema A product doesn’t exist in a vacuum. AI systems care about who is selling it and who the brand is, because identity helps trust. Organisation schema helps connect your site into a single entity: - Name, URL, logo - social profiles - customer service/contact points (where relevant) - sameAs links This is especially important for AI: entity trust matters more when content is being summarised, not just indexed. Canonical mismatches If the page URL and canonical URL don’t match cleanly, you’re telling machines: “This page might not be the real one.” That can cause: - diluted indexing signals - product pages not consolidating properly - AI retrieval pulling the “wrong” version (or skipping it) __Sitemap discoverability gaps__ Sitemaps aren’t glamorous, but they’re still one of the best ways to help machines discover and prioritise your URLs, especially for large catalogs. If pages aren’t easily discoverable, they’re less likely to be crawled frequently, which is a real issue for inventory and price freshness. __Loose entity IDs in JSON-LD (“graph tightness”)__ This is the quiet killer. If your JSON-LD uses inconsistent @id patterns, or doesn’t link entities together, you end up with “floating” facts, hard for machines to stitch into a coherent understanding of your catalog. A tight graph uses stable IDs like: https://example.com/product/slug#product https://example.com/#organisation https://example.com/brand/acme#brand This improves: - deduplication - cross-page entity resolution - AI’s ability to carry context from one page to another **Regular SEO vs AI SEO: what’s actually different?** They overlap heavily, but AI adds pressure in a few specific ways. **Regular SEO is mostly “ranking” problems** Is the page indexable? Is content relevant to a query? Do you have authority/backlinks? Is the page experience solid? **AI visibility is mostly “retrieval + confidence” problems** Can the system extract facts without guessing? Are entities unambiguous (product, brand, org, variant)? Is the information consistent site-wide? Can the model cite or ground the answer to your page? __Think of it like this:__ SEO = competing in a list of results AI = being selected as a source of truth To be a source of truth, you need structured clarity. Why eCommerce brands selling consumables are hit hardest (and why ontology matters) If you sell consumable branded products, paper goods, supplements, beauty, food, pet, cleaning products, AI shoppers care about specifics: - size, count, pack configuration - variants (scent/flavour/strength) - compatibility (dispensers, refills, formats) - recurring purchase patterns - substitutions and equivalents (“compare to”, “similar to”) This is where product ontology becomes a competitive advantage. Ontology is just a clean way to say: your product attributes are consistent, structured, and meaningful across the whole catalog. If one product says “12 rolls” in a title, another says “12ct,” another says “12 pack,” and none of it is structured, AI struggles to compare and recommend accurately. A strong ontology means: - consistent attribute naming (size, count, material, usage) - normalised units (ml, oz, sheets, rolls, grams) - clear variant relationships - clean structured data reflecting those attributes That’s the path to: - better Shopping coverage - fewer mismatches in AI summaries - improved on-site search and filtering - cleaner feeds (Google Merchant Center, retail channels) What a “minimum viable AI-readable” product page should include If you want a practical checklist, start here: Technical basics HTTP 200 robots.txt allows crawling canonical is correct sitemap is present and accurate Structured data Product schema with stable @id Offer schema with price/currency/availability Brand + Organization entities BreadcrumbList (Optional but strong) FAQPage for common questions, where relevant Content clarity a clear product title (human) clean description (human) consistent attributes (machine + human) one source of truth for price/availability (avoid conflicting scripts) Do this and you’re already ahead of most catalog sites. **Where the [Lmo7 AI Readability Analysis](https://www.lmo7.com/ai-readability-tool) tool fits in** The value of an audit like this is speed and focus. Instead of “maybe we should add schema,” you get: a score across crawlability, schema coverage, and graph tightness a short, prioritised issues list (the stuff blocking visibility) practical fix examples (e.g., Offer schema snippets, availability URLs, ID conventions) It turns “technical SEO” into a repeatable QA loop you can run across templates, categories, and high-value SKUs. How Lmo7 can help. At Lmo7, we’re focused on agentic eCommerce, helping brands and retailers become discoverable and conversion-ready in a world where AI systems increasingly sit between customers and catalogs. If you’re serious about AI visibility, we typically help teams with: - AI Readability + schema audits (site-wide, not just one-off pages) - Structured data implementation (Product/Offer/Org, variants, breadcrumbs, FAQs) - Product ontology design (attribute strategy for catalogs and consumables) - Template-level fixes so every SKU benefits (not just your top 20) - Ongoing monitoring so changes don’t silently break eligibility If you want, run a few of your most important product URLs through the tool first. The fastest wins usually show up immediately: Offer completeness, canonical alignment, and graph consistency. Once those are fixed, everything else, SEO, Shopping, and AI discovery gets easier to scale. Quick FAQ Is AI readability just schema? No. Schema is a big part, but crawlability, canonicals, sitemaps, and entity linking matter too. Will this help regular SEO rankings? Yes. These fixes improve indexing confidence, reduce duplication, and increase eligibility for rich results. What’s the biggest mistake eCommerce sites make? Treating product data as “copy” instead of “structured truth.” AI needs attributes and offers to be explicit and consistent.