The Challenge: Your Schema Is Built for 2020, Not Going Into 2026
Most structured data implementations follow the same pattern: add basic Organization schema, maybe an FAQ block, validate with Google’s testing tool, and move on. It was good enough when search engines just needed entity confirmation.
But the schema that satisfies Google’s validator doesn’t satisfy AI systems. When ChatGPT, Perplexity, or Google’s AI Overview processes your page, they’re looking for explicit entity relationships, extractable answer patterns, and knowledge graph connections that most schema completely ignores. Your markup might be “valid” while being functionally invisible to the systems that increasingly determine visibility.
Why This Matters
AI retrieval systems don’t read your content the same way traditional crawlers do. They parse structured data looking for semantic relationships, entity disambiguation via knowledge graph links (Wikidata, Wikipedia, DBpedia), and citation-ready statements formatted in predictable patterns. Schema that lacks these signals gets passed over for competitors who’ve optimized for extraction.
The gap is widening. Brands investing in LLM-optimized schema are building compound advantages—each properly linked entity reinforces the next, creating knowledge graph density that AI systems reward with citations. Meanwhile, brands running basic schema fall further behind with every query.
The QNTM Approach
QNTM’s Schema Module generates structured data specifically architected for AI and LLM retrieval. Every schema includes entity linking to Wikidata and Wikipedia, speakable specifications for voice assistants, knowsAbout arrays with 40+ expertise terms, and descriptions formatted in the “X is Y that Z” pattern AI systems extract cleanly.
Choose your schema type. Fill in the details. Generate markup that’s optimized for where search is going, not where it’s been.
Key Features…
Five Schema Generation Modes
Single Page, Multi-Page, Local Business, E-Commerce, and Product schemas—each optimized for its specific use case.Five Schema Generation Modes
Not all pages need the same schema structure. Single Page mode generates Organization, WebPage, and FAQ schemas for standard content. Multi-Page mode creates a shared Organization schema with page-specific markup. Local Business adds geo coordinates, opening hours, and service areas. E-Commerce includes product collections and offers. Product mode generates rich product schema with specifications, reviews, and availability. Each mode asks only for relevant inputs and generates precisely what that page type requires.
Entity Linking Integration
Automatic Wikidata, Wikipedia, and DBpedia connections that anchor your entities in the knowledge graph.Entity Linking Integration
AI systems disambiguate entities by checking knowledge graph connections. The Schema Module prompts for Wikidata IDs, Wikipedia URLs, and generates proper sameAs arrays that link your organization to authoritative sources. This explicit entity linking tells AI systems exactly who you are—not a guess based on name matching, but a verified connection to the global knowledge graph. The difference between “probably this company” and “definitely this company.”
LLM-Optimized Descriptions
LLM-Optimized Descriptions
When AI systems scan your schema for citable statements, they look for predictable patterns. The Schema Module ensures every description—Organization, Product, Service, Page—starts with a directly extractable statement: “[Entity] is a [category] that [primary function/benefit].” This isn’t about keyword stuffing; it’s about formatting for extraction. Your first sentence becomes a citation-ready statement that AI systems can confidently attribute to your brand.
Speakable Specifications
Voice assistant optimization built into every schema for Alexa, Siri, and Google Assistant queries.Speakable Specifications
Voice search doesn’t display ten blue links—it reads one answer. The speakable specification tells voice assistants exactly which content to read aloud. The Schema Module generates speakable markup pointing to your key content sections: introductions, summaries, FAQ answers, and product descriptions. When someone asks their smart speaker about your category, your content is formatted for spoken delivery.
KnowsAbout Expertise Arrays
40-60 expertise terms per organization that signal topical authority to AI systems.KnowsAbout Expertise Arrays
AI systems assess expertise by scanning knowsAbout arrays—the explicit list of topics an organization claims authority on. Most schema includes 5-10 generic terms. The Schema Module generates comprehensive arrays with 40-60 terms: technical terminology, process names, industry standards, compliance frameworks, and semantic variations. This density signals deep expertise rather than surface knowledge, improving your authority signals for AI citation decisions.
Rich FAQ Schema
Citation-optimized FAQ markup where each answer is structured for direct AI extraction.Rich FAQ Schema
FAQ schema is high-value real estate for AI citations. The Schema Module generates FAQPage markup where each answer follows extraction best practices: direct answer in the first sentence, supporting detail following, no ambiguous pronouns, self-contained responses that don’t require question context. Enter your Q&A pairs and get schema formatted for both featured snippets and AI citations.
Real-Time Schema Scoring
100-point scoring system that evaluates structure, entity linking, LLM optimization, and completeness.Real-Time Schema Scoring
Know exactly how your schema stacks up before deployment. The Schema Scoring system evaluates four dimensions: Structure (25 points) checks @context, @graph, @id patterns, and BreadcrumbList presence. Entity Linking (25 points) scores Wikidata connections, sameAs arrays, and additionalType usage. LLM Optimization (25 points) evaluates speakable specs, knowsAbout depth, and temporal signals. Completeness (25 points) confirms all expected properties are populated. See your score immediately and know what to improve.
Validation Tool Integration
Direct links to Google Rich Results Test, Schema.org Validator, and JSON-LD Playground for verification.Validation Tool Integration
Generated schema should be validated before deployment. The Validate tab provides direct links to Google’s Rich Results Test (confirms rich result eligibility), Schema.org’s official validator (checks specification compliance), JSON-LD Playground (tests parsing), and Google Search Console (monitors production performance). One-click access to every tool you need to verify your markup works as intended.
What’s shipping next
- v1.1 – Content Engine Integration (Q1 2026) — Generate schema directly from Content Engine output, with entity extraction and automatic knowsAbout population.
- v1.2 – Multi-Page Batch Generation (Q1 2026) — Upload CSV with page data and generate schema for entire site sections in one operation.
- v1.3 – Schema Diff & Comparison (Q2 2026) — Compare generated schema against existing markup to identify gaps and improvements.
- v1.4 – Industry Templates (Q2 2026) — Pre-configured templates for healthcare, legal, financial services, SaaS, and manufacturing with industry-specific schema types.
- v1.5 – Competitor Schema Analysis (Q3 2026) — Enter competitor URLs and see what schema they’re running, with gap analysis against your markup.
- v2.0 – Schema Monitoring (Q4 2026) — Track deployed schema for validation errors, deprecation warnings, and optimization opportunities over time.
**Tool is best viewed on desktop… a mobile solution will become available once we have a full dashboard ready for deployment.**