Ranking in AI
Schema.org turnkey
FAQPage, LocalBusiness, Article, BreadcrumbList — markup for all page types.
JSON-LD and structure
Clean JSON-LD with no validation errors, verified in Rich Results Test and Schema Validator.
llms.txt and semantics
We set up llms.txt, clean HTML semantics and heading hierarchy for LLM crawlers.
Audit in 24 hours
We check the current state of your markup and deliver a list of critical errors.
Schema.org, JSON-LD, llms.txt and clean semantics — the foundation without which AI cannot read your business correctly.
ChatGPT, Gemini and Perplexity read a site differently from Google: they rely on structured data, semantic hierarchy and the llms.txt file. Without the right technical foundation, AI systems ignore or distort your company information in their answers. We set up the markup turnkey — from FAQPage to LocalBusiness — and make your site readable for all modern AI search engines.
Schema.org turnkey
FAQPage, LocalBusiness, Article, BreadcrumbList — markup for all page types.
JSON-LD and structure
Clean JSON-LD with no validation errors, verified in Rich Results Test and Schema Validator.
llms.txt and semantics
We set up llms.txt, clean HTML semantics and heading hierarchy for LLM crawlers.
Audit in 24 hours
We check the current state of your markup and deliver a list of critical errors.
Quick answer
Technical AI search setup means implementing Schema.org (JSON-LD), structured data, an llms.txt file and clean HTML semantics so that ChatGPT, Gemini and Perplexity understand and cite your site correctly. Without this foundation, AI systems ignore or distort your business information. Request an audit — we reply within 24 hours.
This page covers technical AI search setup for businesses across Spain. We explain what Schema.org, JSON-LD and structured data are, why you need an llms.txt file and how clean semantics affect the way ChatGPT, Gemini and Perplexity read and cite your site. By technical setup we mean: implementing Schema.org markup of the right types (FAQPage, LocalBusiness, Article, BreadcrumbList, Product, Service), writing valid JSON-LD, creating and optimising llms.txt, cleaning up HTML structure and heading hierarchy, and configuring sitemap and robots.txt for LLM crawlers. Below you will find a step-by-step audit and implementation plan, a table of key setup elements, Google Search Central guidance on structured data and answers to common questions. At the end — our "Ranking in AI" packages with real prices and related services.
Most sites either have no Schema.org at all, use the outdated Microdata format instead of JSON-LD, or contain validation errors. Before adding anything, you need to understand what already exists and where the critical gaps are. The right sequence is: audit → prioritise markup types → write JSON-LD → validate → llms.txt → check semantics. After implementation, AI systems start correctly interpreting pages on the very next crawl.
Not all technical elements affect AI equally. Schema.org in JSON-LD format and the llms.txt file are direct signals to language models. Clean HTML semantics and heading hierarchy help LLM parsers split the page into meaningful blocks correctly. Sitemap and robots.txt determine what actually enters AI crawler indexes. The table below shows each element with its role and the expected effect on AI search.
| Element | Why it matters | Effect on AI |
|---|---|---|
| Schema.org JSON-LD | Passes structured data about the business, services and FAQ directly to the parser | AI knows the exact type of object, its attributes and relationships |
| FAQPage schema | Marks questions and answers as separate semantic blocks | Direct answer output in ChatGPT, Perplexity and voice assistant chats |
| llms.txt | Controls LLM crawler access to content and sets indexing priorities | AI systems read the right sections and do not index service content |
| HTML semantics and H1–H6 | Forms the logical page structure for parsers | LLM correctly chunks content and cites the right block |
| Sitemap.xml + robots.txt | Tells crawlers which pages exist and are accessible | AI crawlers traverse the full site and do not waste budget on blocked URLs |
AI systems, including ChatGPT, Gemini and Perplexity, use structured data in two scenarios: during indexing — to classify the page precisely — and during answer generation — to extract a specific attribute (address, price, FAQ answer) without the risk of hallucination. Without Schema.org, the model is forced to "guess" from the text, which sharply reduces citation accuracy. Google Search Central confirms that structured data is a direct signal for search and AI systems, enabling content to be displayed in rich result formats.
Expert opinion
«Structured data is code that you add to your website pages to describe your content so that search engines can better understand it. Search engines use that knowledge to display your content in useful (and visually engaging) formats in search results.»
In short
Technical AI search setup means Schema.org (JSON-LD), an llms.txt file, clean HTML semantics and a correct sitemap. Without this foundation, ChatGPT, Gemini and Perplexity do not read your site correctly: they ignore services, mix up the address and do not cite your FAQ. With valid markup, AI systems receive precise data about your business and use it in their answers — this is the direct path to being cited in AI search. Start with an audit of the current markup, then implement in priority order: LocalBusiness → FAQPage → Article → llms.txt. The work takes 1–2 weeks and delivers a long-term effect without ongoing investment. Request a quote — we will prepare your audit and "Ranking in AI" plan within 24 hours.
AI Promotion
So ChatGPT, Gemini, Perplexity and Google AI Overview recommend your company when a customer asks “who’s the best in Valencia”.
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