SEO & GEO Analyzer by Jean Pierre Kolb

• Title tag — 30–60 characters, primary keyword near the start. Unique per page. Avoid keyword stuffing.
• Meta description — 70–160 characters. Compelling summary that increases click-through rate. Not a direct ranking factor, but indirectly important.
• Single H1 — One H1 per page, containing the primary topic. H2–H6 should follow a logical hierarchy.
• Word count — Aim for 300+ words for indexable pages. Thin content (<100 words) is rarely ranked.
• Readability — Flesch-Kincaid score ≥ 60 (standard) is a good target for general audiences. Short paragraphs and sentences help.
• Keyword placement — Include primary keyword in title, H1, first paragraph, and naturally in body. Avoid repetitive exact-match stuffing.
• Fresh content — Update evergreen content regularly. Add a dateModified in JSON-LD to signal freshness to crawlers.
• Image alt text — Every meaningful image needs descriptive alt text. Decorative images: alt="".
• Open Graph + Twitter Card — Required for controlling how pages appear when shared on social media. Minimum: title, description, image (1200×630 px).

• Descriptive anchor text — Use meaningful text like "SEO best practices" instead of "click here". Helps both users and crawlers understand link context.
• Internal linking — Link related pages from within your content. Spreads PageRank and helps crawlers discover pages.
• External links — Link out to authoritative sources. Use rel="nofollow" or rel="sponsored" for paid/UGC links.
• Broken links — 404 links waste crawl budget and degrade user experience. Audit regularly with a crawler.
• Link depth — Important pages should be reachable within 3 clicks from the homepage. Deep pages are crawled less frequently.
• Backlinks — Links from authoritative, topically relevant domains are a major ranking signal. Focus on quality over quantity.
• Disavow sparingly — Only disavow links if you have evidence of a manual action or clearly toxic pattern. Do not disavow indiscriminately.

• llms.txt — Add an /llms.txt file (plain text, Markdown format) to help AI systems understand what your site offers and how it should be cited. See llmstxt.org.
• AI Overview optimization — Google's AI Overviews often cite pages with clear, structured answers, concise prose, and authoritative signals (author, date, organization schema).
• Entity clarity — Name entities explicitly (organizations, people, products) and mark them up with Schema.org. This helps knowledge graph inclusion and AI citation.
• Conversational queries — AI-driven search handles natural language questions. Optimize for "who, what, where, why, how" question patterns.
• Robots.txt for AI crawlers — Common AI crawlers: GPTBot, Claude-Web, PerplexityBot, CCBot. Allow or block per use case.
• Perplexity / ChatGPT citations — These tools cite readable, well-structured HTML pages. Avoid heavy JavaScript-only rendering.
• AI-generated content — Provide value beyond what AI generates. Differentiate with original data, personal expertise, and direct experience (Google's "E-E-A-T").
• E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) — Key quality signals for Google's Quality Raters. Use author schema, About/Contact pages, and editorial standards.

• Improve LCP — Preload the LCP image (<link rel="preload" as="image">). Use a CDN. Eliminate render-blocking resources. Upgrade to HTTP/2.
• Improve INP — Minimize long JavaScript tasks (>50 ms). Use requestIdleCallback for non-critical work. Avoid synchronous XHR.
• Improve CLS — Set explicit width and height on all images and iframes. Avoid inserting DOM elements above existing content without user interaction.
• TTFB — A server-side metric: < 200 ms is excellent, < 500 ms is acceptable. Use caching, CDN edge nodes, and faster server responses.
• Measure — Use PageSpeed Insights, web.dev/measure, or Chrome DevTools Performance panel.

AI systems prefer content that is clear, structured, and factual. Generic, bloated text walls are rarely cited.

• Short paragraphs — Keep paragraphs under 150 words. AI models extract information more easily from concise blocks.
• Clear headings — Use descriptive H2–H6 headings that summarize the section content. AI uses these as navigational anchors.
• Facts, tables, and lists — Explicit data points, comparison tables, and structured lists are highly quotable by AI.
• Original data — AI heavily cites original sources: own studies, surveys, benchmarks, and unique datasets. Be the primary source.
• Authenticity over mass — Generic, AI-generated content is ignored. Show real expertise, personal experience, and unique insights.
• Conversational queries — Optimize for natural language questions: "who, what, where, why, how" patterns that AI users ask.
• FAQ sections — Dedicated FAQ blocks with clear Q&A format are directly extractable by AI systems.

AI models rely on structured, machine-readable content. Unstructured text blocks are increasingly ignored.

• Schema Markup (JSON-LD) — Use FAQPage, HowTo, Article, Review, Product schemas. These feed directly into AI knowledge extraction.
• Organization & Person schema — Identify who is behind the content. AI uses this for E-E-A-T verification and citation attribution.
• Tables with <th> headers — Properly structured HTML tables are machine-readable and highly quotable.
• Definition lists — <dl>/<dt>/<dd> for glossaries and key-value explanations.
• llms.txt — Add an /llms.txt file (Markdown format) at your domain root. It helps AI systems understand your site's purpose and content structure. See llmstxt.org.
• Modular content — Design content in self-contained blocks that AI can extract independently without losing context.

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is critical for AI visibility. Without verifiable authority, even high-quality content stays invisible.

• Author pages — Create dedicated author pages with credentials, bio, social links, and published works. Use Person schema.
• Original research — Publish industry studies, surveys, and unique data. AI systems strongly prefer citing original sources.
• Expert content — Guest posts, podcast appearances, interviews, and speaking engagements build recognizable expertise signals.
• Reviews & testimonials — Customer reviews, case studies, and testimonials provide social proof that AI recognizes.
• About & Contact pages — Clear organizational identity with verifiable contact information builds trust.
• Editorial standards — Transparent sourcing, fact-checking processes, and editorial policies signal reliability.

• AI Crawler management — Common AI crawlers: GPTBot, ChatGPT-User, OAI-SearchBot, Google-Extended, Claude-Web, ClaudeBot, PerplexityBot, CCBot, Bytespider. Allow or block selectively in robots.txt.
• llms.txt file — Place at domain root (/llms.txt). Describe your site, key content areas, and how AI should cite your content. Currently experimental but gaining adoption.
• Server-side rendering — AI crawlers struggle with heavy JavaScript-only sites. Ensure critical content is in the initial HTML response.
• Fast load times — AI crawlers respect crawl-delay and skip slow sites. Optimize TTFB, enable compression, use CDN.
• Clean URL structure — Logical, descriptive URLs help AI systems categorize content topically.
• Internal linking — Strong internal link structure helps AI understand your topical authority and content hierarchy.

AI systems decompose user questions into multiple sub-queries (Query Fan-Out). Sites that comprehensively cover a topic are favored.

• Topic clusters — Build pillar pages with comprehensive subtopic coverage. Link related content together to demonstrate depth.
• Entity coverage — Explicitly name and describe all relevant entities (products, methods, people, places, studies). Use Schema.org markup.
• Multi-format content — Cover topics across text, video, infographics, and podcasts. AI aggregates from multiple formats.
• Fan-out analysis — For key topics, identify all possible sub-questions a user might ask. Create content that answers each one.
• Content depth — Aim for 500+ words on key pages. Thin content (<300 words) is rarely cited by AI.

Visibility is no longer limited to Google. AI systems aggregate information from across the web.

• Platform presence — Be present on YouTube, Reddit, TikTok, LinkedIn, and industry forums. AI crawls these for information.
• Social proof — Reviews, mentions, and discussions on third-party platforms increase your AI citation likelihood.
• Consistent identity — Use the same brand name, descriptions, and key messages across all platforms for entity recognition.
• Community engagement — Active participation in relevant communities (Reddit, Stack Overflow, industry forums) builds mention-based authority.

Search engines are evolving into AI agents that autonomously research, compare, and prepare decisions on behalf of users.

• Agent Optimization (AO) — A new discipline emerging beyond GEO. Optimizing for autonomous agents that evaluate and select sources without human intervention.
• Machine-readable data — APIs, structured data feeds, and clean data exports will become essential for agent accessibility.
• Trust signals — Verifiable credentials, consistent track records, and transparent provenance will be mandatory for agent trust.
• Decision-ready content — Content must provide clear comparisons, recommendations, and actionable conclusions that agents can directly use.
• Early mover advantage — Sites that structure data and build authority now will have a significant lead when AI agents become mainstream.