SEO Geo

AI Search / GEO Optimization

GEO is the practice of making content extractable and citable by AI search surfaces. This skill is the technical/audit + implementation half: crawler access, render strategy, passage citability, and a falsifiable scoring rubric.

For the broader content-strategy and AI-visibility-monitoring side (competitive citation tracking, content briefs, getting cited as a source over time), see the ai-seo skill — cross-reference it, don't duplicate it here.

Primary Source: Google's AI Optimization Guidance

Google's own position (Search Central): optimizing for generative AI search is still SEO. "AEO" and "GEO" are mostly rebranded labels for the same fundamentals — quality content, crawlable/indexable pages, good structure, unique value. Frame GEO findings as SEO fundamentals applied to AI-search surfaces, not as a separate discipline.

Things Google has explicitly said do not move the needle (treat community advice that contradicts this with skepticism, and note the contradiction in any report):

• llms.txt is not a citation/ranking signal for Google's AI search.
• Artificially chunking content for "AI parsing" is unnecessary.
• AI-rephrasing existing copy to sound "LLM-friendly" does not help.
• Mention-farming / coordinated brand-mention campaigns are not a ranking lever.

The durable test for any page is the classic quality framing — Who made it (real expertise/authorship), How it was made (original effort, not spun), and Why it exists (to help users vs. to game search). If a tactic fails the Who/How/Why test, drop it.

Relationship between the two Google AI surfaces

Google runs two distinct citation engines, and they cite different URLs:

AI Mode and AI Overviews reach the same conclusion most of the time but cite the same URLs a small minority of the time (Ahrefs, large query-pair study). Treat them as separate surfaces and score both: ranking well in classic Search feeds AI Overviews, but AI Mode draws from a broader pool where freshness and entity authority outweigh raw position.

Key Statistics (directional, verify before quoting to a client)

Brand mentions correlate ~3x more strongly with AI visibility than backlinks (Ahrefs, Dec 2025, 75k brands). Of the mention signals, YouTube mentions show the strongest correlation (0.737); Domain Rating from backlinks is weak (0.266). Only ~11% of domains are cited by both ChatGPT and Google AIO for the same query — platform-specific optimization is real.

GEO Scoring Rubric (0–100)

Score each dimension 0–100, then weight. Every dimension has a falsifiability check: how you would know it failed, plus the leading indicator to watch.

final = round(0.25·cite + 0.20·struct + 0.20·auth + 0.20·tech + 0.15·multi)

Bands: 80–100 AI-ready · 60–79 competitive, gaps remain · 40–59 significant work · <40 largely invisible to AI surfaces.

1. Citability (25%)

Run passage scoring against boilerplate-stripped main text (strip nav, header, footer, sidebars — use your own extractor/crawler), not raw HTML, so chrome doesn't dilute the signal.

• Optimal self-contained answer block: ~134–167 words.
• Front-load it: a large share of AI citations come from the first ~30% of a page (SE Ranking) — put the most citable answer near the top, not below the fold.
• Direct answer in the first 40–60 words of each section.
• "X is…" / "X refers to…" definition patterns score high.
• Specific facts/statistics with source attribution; unique data points.
• Penalize: vague generalities, buried conclusions, opinion without evidence.

Falsifiability: Failed if an AI surface, asked the page's target question, answers without citing the page despite the page ranking on p1. Leading indicator: of the top question-headings, what % have a clean, extractable 40–60-word answer block immediately under them? Below ~50% predicts low citation.

2. Structural readability (20%)

• Clean H1→H2→H3 hierarchy; no skipped levels.
• Question-based headings that mirror real query phrasing (see citability scoring below).
• Short paragraphs (2–4 sentences); lists for steps/multi-item; tables for comparative data; FAQ Q&A blocks.
• Penalize: wall-of-text, inconsistent hierarchy, no lists/tables.

Falsifiability: Failed if a heading outline extracted from the DOM reads as topical labels ("Overview", "Features") rather than questions a user types. Leading indicator: ratio of question-form headings to total headings.

3. Authority & freshness (20%)

• Author byline with real credentials; Organization + Person entity signals.
• Citations to primary sources (studies, official docs, data).
• Entity presence: Wikipedia/Wikidata, Reddit, YouTube, LinkedIn (sameAs).
• Freshness is high-leverage. Recent content (under ~3 months) is markedly more likely to be cited; pages left stale 6+ months lose citation eligibility (SE Ranking, 1.3M-citation study). A scheduled refresh program — with a real dateModified change, not a touched timestamp — is one of the best GEO plays.
• Penalize: anonymous authorship, missing dates, no sources.

Falsifiability: Failed if the most valuable pages have dateModified older than 6 months with no substantive update. Leading indicator: median age of top-traffic pages.

4. Technical accessibility (20%)

AI crawlers generally do NOT execute JavaScript — content that only appears after client hydration is invisible to most of them. Server render it.

• SSR/RSC vs client-only: is the answer in the initial HTML?
• AI crawler access in robots.txt (see table below).
• "Agent-friendly" basics: stable URLs, real <a href> links (not JS-only click handlers), text content in HTML rather than canvas/image-only.
• llms.txt presence (reported, zero citation weight — see caveat).

Falsifiability: Failed if curl-ing the URL (no JS) returns a shell without the primary answer text. Leading indicator: ratio of extractable-text bytes in raw HTML vs. post-hydration DOM.

5. Multi-modal content (15%)

Pages with relevant images/video/charts/interactive elements show materially higher AI selection rates. Check for text + relevant media, supporting structured data, and that media has real alt/captions (text the crawler reads).

Falsifiability: Failed if a how-to/comparison page is text-only where the query clearly wants a visual. Leading indicator: presence of at least one captioned, relevant media element per major section.

Question-based citability scoring (per heading)

AI answers are question-shaped. Score each section heading and its lead passage:

1. Is the heading a question (or trivially rephrasable as one matching a real query)? Headings like "How do I…", "What is…", "X vs Y" are prime.
2. Does the first sentence answer it directly in 40–60 words, self-contained?
3. Is there a quotable fact/number with attribution in the block?
4. Is the block extractable without surrounding context?

Score each 0/1, average over headings → section citability sub-score. This is the fastest lever: rewriting H2/H3s as questions and adding a direct lead answer raises citability without new content.

AI Crawler Access (robots.txt)

Recommendation: allow GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, PerplexityBot for AI-search visibility. Note that Google-Extended is a training opt-out token — blocking it does not remove you from AI Overviews/AI Mode (those follow normal Googlebot crawling). Block CCBot / training-only crawlers only if licensing policy requires it.

Next.js robots config (App Router)

// app/robots.ts
import type { MetadataRoute } from 'next'

const SITE = process.env.NEXT_PUBLIC_SITE_URL ?? 'https://example.com'
const AI_SEARCH_BOTS = ['GPTBot', 'OAI-SearchBot', 'ChatGPT-User', 'ClaudeBot', 'PerplexityBot']

export default function robots(): MetadataRoute.Robots {
  return {
    rules: [
      { userAgent: '*', allow: '/' },
      // Explicitly allow AI search crawlers (overrides any future global tightening)
      ...AI_SEARCH_BOTS.map((userAgent) => ({ userAgent, allow: '/' })),
      // Block training-only crawler if licensing requires it:
      { userAgent: 'CCBot', disallow: '/' },
    ],
    sitemap: `${SITE}/sitemap.xml`,
  }
}

Server-side rendering: the make-or-break check

Because AI crawlers don't run JS, the citable answer must be in the initial HTML payload. On our stack this means rendering it in a Server Component (default), not gating it behind a 'use client' boundary that fetches on mount.

// app/[locale]/guides/[slug]/page.tsx — RSC: answer is in the HTML, no JS needed
import { getTranslations } from 'next-intl/server'
import { getGuide } from '@/lib/payload'

export default async function GuidePage({ params }: { params: Promise<{ slug: string; locale: string }> }) {
  const { slug } = await params
  const guide = await getGuide(slug) // server fetch — rendered before send

  return (
    <article>
      <h1>{guide.title}</h1>
      {/* Lead answer block: 40–60 words, self-contained, first thing in the DOM */}
      <p className="lead">{guide.summary}</p>
      {guide.sections.map((s) => (
        <section key={s.id}>
          {/* Question-form heading mirrors the target query */}
          <h2>{s.question}</h2>
          <p>{s.directAnswer}</p>
          {s.body}
        </section>
      ))}
    </article>
  )
}

Verify with a JS-disabled fetch (use your own crawler/tooling, or curl -A "GPTBot" <url>): the answer text must be present in the raw HTML.

llms.txt — implement, but no false hope

The /llms.txt convention lets a site advertise its structured content to agents. Primary-source caveat: Google has stated it is not a citation or ranking signal for AI search, and server-log audits show major AI search systems rarely fetch it. Report its presence in an audit but assign it zero citation-ranking weight. It is cheap and harmless to ship (some agent tooling reads it), so treat it as hygiene, not a lever — never promise visibility gains from it.

# Example /llms.txt
# Acme Docs
> Developer documentation for the Acme platform.

## Core
- [Quickstart](https://example.com/docs/quickstart): 5-minute setup
- [API reference](https://example.com/docs/api): Full endpoint reference

## Key facts
- Founded 2019, EU-hosted, GDPR-compliant

You can serve it statically (public/llms.txt) or generate it from the CMS:

// app/llms.txt/route.ts — generated from Payload content
import { getDocsIndex } from '@/lib/payload'

const SITE = process.env.NEXT_PUBLIC_SITE_URL ?? 'https://example.com'

export const revalidate = 3600

export async function GET() {
  const pages = await getDocsIndex()
  const body = [
    '# Acme Docs',
    '> Developer documentation for the Acme platform.',
    '',
    '## Core',
    ...pages.map((p) => `- [${p.title}](${SITE}${p.path}): ${p.summary}`),
  ].join('\n')
  return new Response(body, { headers: { 'Content-Type': 'text/plain; charset=utf-8' } })
}

Structured data for AI discoverability (RSC, JSON-LD)

Emit entity signals server-side. Keep dateModified honest — bump it only on a real content change so the freshness signal stays trustworthy.

// Inside the RSC page — Article + Person + Organization signals
function JsonLd({ guide, site }: { guide: Guide; site: string }) {
  const data = {
    '@context': 'https://schema.org',
    '@type': 'Article',
    headline: guide.title,
    datePublished: guide.publishedAt,
    dateModified: guide.updatedAt, // real modification date only
    author: {
      '@type': 'Person',
      name: guide.author.name,
      jobTitle: guide.author.role,
      sameAs: guide.author.profiles, // LinkedIn, Wikipedia, etc.
    },
    publisher: {
      '@type': 'Organization',
      name: 'Acme',
      sameAs: [`${site}`, 'https://www.linkedin.com/company/acme'],
    },
  }
  return <script type="application/ld+json" dangerouslySetInnerHTML={{ __html: JSON.stringify(data) }} />
}

For FAQ/HowTo markup and full schema patterns, defer to the seo-schema skill rather than re-deriving it here.

Platform-Specific Optimization

Audit Output

Produce GEO-ANALYSIS.md:

1. GEO Readiness Score: XX/100 + per-dimension breakdown
2. Per-surface scores (AI Overviews, AI Mode, ChatGPT, Perplexity, Copilot)
3. AI crawler access status — exactly which bots allowed/blocked + the robots.txt directives to fix it
4. SSR/render check — does the raw HTML contain the answer text? (per key page)
5. Passage citability — question-heading score, identified weak blocks, and specific 134–167-word rewrites
6. Authority & freshness — author/entity signals, median page age
7. llms.txt status — present/absent (zero ranking weight; template if absent)
8. Top 5 highest-impact changes with effort estimates
9. Falsifiability notes — for each top change, how we'd know it worked (leading indicator) and how we'd know it failed

When a community recommendation contradicts Google's primary-source guidance, defer to Google and flag the contradiction.

Error handling

Priority Playbook

Quick wins — rewrite H2/H3s as questions; add a 40–60-word direct answer under each; front-load the most citable block; add specific stats with sources; publish/updated dates; allow key AI crawlers in robots.txt; Person schema for authors.

Medium — move client-only answer content into RSC/SSR; author bios with credentials + sameAs; comparison tables; ship /llms.txt (hygiene only); set up a content-freshness refresh cadence.

High impact — original research/surveys (unique citability); entity presence (Wikipedia/Wikidata, YouTube, Reddit); comprehensive sameAs entity linking; build/own a useful tool or calculator that earns mentions.

Parts adapted from claude-seo (MIT, © 2026 agricidaniel).