Structured Data Services for LLM Visibility
Public large language models like ChatGPT, Claude, and Gemini learn from the open web—but they don’t understand it unless it’s structured. At LLM.co, we help your brand speak the language of machines through properly implemented semantic data and schema markup.
The result?
Your brand is more findable, more accurately represented, and more likely to be cited or surfaced by AI across the internet.
Semantic & Schema Data Services to Boost Your Brand’s AI Visibility Across The Most Popular Large Language Models (LLMs)
Why Structured Data Matters for LLM Visibility
Structured data (like schema.org markup) gives AI models the context they need to understand who you are, what you offer, and why it matters. It’s the difference between being mentioned vaguely and being accurately cited. Whether you’re a brand, a product, a person, or a publisher, semantic markup increases your chance of showing up in LLM responses—correctly, consistently, and with authority.
Better structured data leads to:
Accurate Brand Attribution
When LLMs reference your company or content, they rely on web data to determine who you are and what you do. Proper structured data ensures your brand is clearly defined, making it far more likely that platforms like ChatGPT, Bard, Claude, and Perplexity correctly attribute quotes, summaries, and mentions to your business—rather than confusing you with another entity.
Increased Citation Likelihood
Citations in LLMs are not random—they're pulled from content that models trust and understand. Structured data enhances clarity and authority by reinforcing your content’s context, improving the chance that your articles, product pages, or executive bios are selected as reference sources in model responses.
Reduced Hallucinations
Without semantic structure, LLMs may generate wildly inaccurate or misleading summaries about your brand, team, or offerings. By disambiguating people, places, and entities through schema markup, you lower the odds of hallucinated facts and help AI systems generate responses grounded in truth.
Better LLMO
Just like SEO optimized for Google, LLMO (Large Language Model Optimization) is the emerging frontier of digital visibility. Structured data lays the foundation for effective LLMO by signaling clean entity relationships, authoritative context, and semantic clarity—making your brand more discoverable and usable by public AI tools.
Cross-Platform Recognition
Whether a model is crawling your site directly or learning about your brand via third-party sources, structured data—especially with SameAs links and brand entities—helps establish your digital footprint across platforms. This results in better integration into the knowledge graphs that power everything from search engines to AI assistants.
Improved AI Agent & Voice Searches
Structured data not only benefits web-based LLMs—it’s essential for voice agents like Alexa, Siri, and Google Assistant. When your business, location, or product information is structured correctly, AI voice systems can surface your brand accurately in real-time conversations and local search queries.
LLM Schema Markup Services
At LLM.co, we deliver end-to-end structured data and semantic schema services built specifically for AI visibility. Our offerings include:
Schema Implementation
We set up and validate the appropriate schema.org types for your site—Organization, Person, Product, Article, Review, LocalBusiness, FAQ, and more—using clean JSON-LD that speaks directly to LLM crawlers and Google’s Knowledge Graph.
Entity Disambiguation
If your brand shares a name with other companies or your CEO is often confused with others online, we fix that. Our markup and linking strategies clarify who’s who—and help AI get it right.
SameAs + Knowledge Graph Connectivity
We link your brand, people, and products to authoritative third-party sources like Wikidata, LinkedIn, Crunchbase, and GitHub. These references help cement your identity in the semantic layer LLMs use to generate knowledge.
Schema Audit & Repair
Already using structured data but unsure if it’s helping? We’ll audit your current markup, identify errors, suggest enhancements, and implement improvements to boost performance.
Semantic Content Structuring
Beyond code, we structure your content’s hierarchy—headings, metadata, internal linking—for better machine readability. The result is cleaner parsing, stronger context signals, and smarter AI responses.
Our Structured Data Methodology
We don’t just dump JSON-LD on your page and call it done. Our process is engineered for impact:
Entity Mapping
We define your core brand entities—people, products, services, content assets, and relationships—to ensure complete, correct representation.
Schema Strategy
Based on your goals, we select the most appropriate schema types and extensions to target both LLMs and traditional search engines.
Implementation & Validation
We write, test, and validate schema using tools like Google’s Rich Results Test, Schema.org guidelines, and model-specific parsing behavior.
Knowledge Graph Integration
We connect your entities to trusted public databases to strengthen their semantic identity.
Testing & Monitoring
We track downstream effects in AI platforms, search engines, and citation visibility—adjusting as needed.
Why LLM.co?
At LLM.co, we bring together deep expertise in SEO, large language model behavior, and structured data implementation to deliver one unified goal: helping AI understand your brand with precision. Unlike one-size-fits-all schema plugins, our approach is built on real-world application and technical depth. We specialize in semantic search strategies, model-specific parsing behavior, entity linking, schema extension development, and knowledge graph integration—all tailored to enhance your visibility in AI systems.
Whether you're a fast-scaling SaaS company, a global media brand, or a founder looking to protect and project your digital identity, we make sure public LLMs like ChatGPT, Claude, and Gemini represent you correctly, consistently, and authoritatively—across platforms and across time.
Private LLM Blog
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FAQs
Frequently asked questions about our structured data & schema markup engineering services
Traditional SEO schema is aimed at search engines. Our structured data work is engineered with AI/LLM parsing in mind—adding disambiguation, authoritative linking, and deeper entity definition.
Yes—while you can’t “rank” in ChatGPT like Google, structured data increases your chances of being cited, referenced, or correctly summarized by the model.
Initial changes in crawlability and representation can occur within a few weeks. Ongoing refinement may take 30–90 days depending on model retraining and data exposure.
We’ll audit it. Most implementations are incomplete, incorrect, or don’t speak to modern LLM requirements. We’ll improve what you have and expand it as needed.