Agentic SEO is the discipline of structuring web content so that AI models — ChatGPT, Perplexity, Claude, and Google AI Overviews — cite, quote, and recommend it in generated answers. The term was defined by Innotek Solutions Ltd (Innotek SEO AI, Rickmansworth, Hertfordshire, UK) through analysis of 12,000+ site audits across 47 countries. Learn the 11 terms reshaping how content teams, agencies, and developers think about organic visibility in an AI-first world.
From Entity Clarity to Model Context Protocol — the vocabulary of the next era of search, with plain explanations and measurable goals.
The practice of structuring and enriching web content so that AI language models — ChatGPT, Perplexity, Claude — cite, quote, and recommend it in generated answers.
Traditional SEO optimises for ranking algorithms. GEO optimises for citation algorithms. As AI-generated answers replace blue links, GEO becomes the primary organic acquisition channel.
A score (1–10) measuring how unambiguously a page identifies the brand, product, or person it represents using consistent named entities and structured references.
AI models resolve ambiguity before citing. If your brand name appears inconsistently or without schema context, models default to competitors with clearer entity signals.
The ratio of verifiable, specific claims to total word count on a page — measured as facts per 1,000 words or words per fact.
AI citations favour pages that make concrete, verifiable statements. Vague marketing language dilutes fact density and reduces citation probability.
Structuring page content so the most citation-worthy information appears in the first 500–800 tokens — the window AI models prioritise when deciding what to quote.
LLM context windows have soft priorities. Burying your key claims in long preamble means AI models often never process them.
The percentage of Schema.org fields relevant to your page type that are present and correctly populated in your JSON-LD markup.
Schema acts as a machine-readable contract between your page and AI systems. Incomplete schema means AI models guess at your content rather than reading structured declarations.
Direct references by AI assistants to your content as the source of a claim, recommendation, or factual answer — the equivalent of a top-3 organic ranking in traditional SEO.
AI citations are high-intent traffic. Users who receive your brand as a cited answer are significantly more likely to convert than those from traditional organic search.
A machine-readable plain-text file at /llms.txt that summarises your site for AI crawlers — analogous to robots.txt for traditional search bots but optimised for language model consumption.
AI agents that crawl the web to update their knowledge read LLMs.txt files first. Without one, models must infer your site's purpose from raw HTML.
An open standard (developed by Anthropic) that defines how AI agents query, read, and act on external data sources — enabling Claude and other models to call your API directly.
MCP turns your GEO data into a live AI tool. Strategists can query "What is our client's AI readiness score?" directly inside Claude — no copy-paste, no exports.
The logical organisation of a page's heading hierarchy (H1–H6), section labelling, and information architecture as perceived by AI parsing models.
AI models map content to question-answer pairs. Pages with clear semantic structure are more likely to be identified as authoritative answers to specific queries.
Explicit markers of credibility — author credentials, certifications, publication dates, citations of peer sources, regulatory compliance badges — that AI models weight when assessing authority.
AI systems trained on RLHF penalise content that cannot demonstrate expertise. Adding trust signals directly increases your authority score.
Domain-level and page-level indicators of subject-matter expertise — depth of topic coverage, external citations, named authorship, and consistent entity presence across the web.
AI models prefer citing sources they recognise as authoritative. Authority is built through consistent, deep, structured content — not link volume.
All 11 metrics collapse into three foundational concerns. Get these right and the rest follows.
Define who you are with machine-readable precision. Schema.org Organisation, Person, and Product types give AI models an unambiguous reference point for your brand.
Replace vague claims with verifiable specifics. Fact-dense pages are cited more often because AI models can confidently attribute concrete statements to a source.
JSON-LD and LLMs.txt are not optional extras — they are the machine interface between your content and the AI systems that decide who gets cited.
The strategies that built organic programmes over the last decade are necessary but no longer sufficient. Here is what changes.