ChatGPT Atlas Is Here: AI SEO and Agent-SEO That Make Your Business Discoverable

ChatGPT Atlas is here. Perplexity, Gemini, and Copilot are already answering as “AI browsers.” If assistants do not understand who you are, what you offer, and how to contact you, you will lose discovery. Our approach: make content machine-usable, then make assistants answer correctly about your brand.

What we mean by AI SEO and Agent-SEO

• AI SEO - structure your site so machines can use it (clean IA, fast pages, JSON-LD that mirrors visible content, verifiable datasets).
• Agent-SEO - guide assistants to answer accurately (authoritative /llms.txt, answer templates, guardrails, discoverability signals).

Minimal viable artifacts

You do not need a new platform. You need a few honest files and consistent signals.

1) /llms.txt — authoritative brief

[meta]
site: https://example.com
brand: ExampleCo
last_updated: 2025-10-22

[about]
tagline: We ship fast, accessible web products with AI built in.

[services]
ai: AI chats; AI-assisted search; AI SEO; Agent-SEO

2) JSON-LD — business + offers

{ "@context":"https://schema.org", "@type":"ProfessionalService",
"name":"ExampleCo", "url":"https://example.com",
"hasOfferCatalog":{"@type":"OfferCatalog","name":"Services","itemListElement":[]}
}

3) robots.txt — be discoverable

User-agent: *
Allow: /
Allow: /llms.txt
Sitemap: https://example.com/sitemap.xml

4) sitemap.ts — surface the brief

routes.push({
  url: abs('/llms.txt'),
  lastModified: now,
  changeFrequency: 'monthly',
  priority: 0.3,
})

5) HTTP header — link hint

{ key: 'Link', value: '<https://example.com/llms.txt>; rel="llms"' }

How we implement

We build from your truth - not someone else’s template. That means typed content models, a publisher workflow that prevents drift between what users see and what crawlers read, and a small ops loop that nudges recrawls reliably.

• Editors publish once. JSON-LD, sitemaps, and llms.txt update from the same data.
• We add answer templates and do-don’t rules so assistants avoid prices and DIY steps.
• We keep a short list of machine endpoints for quotable snippets. Nothing exotic.

Implementation considerations

• Content model - services, proof, CTAs, and entity fields are first-class.
• Schema - Organization or LocalBusiness plus Service and Article. OfferCatalog when it helps clarify services.
• Performance - image pipelines, budgets, and cache control so assistants do not time out on heavy pages.
• Internationalization - hreflang only if you have real content, not clones.
• Security - assistants read public files. Keep private data private.

Measurement and recrawl ops

We track four things weekly: coverage in assistant answers, accuracy of key facts, time from change to a fresh answer, and conversions from assistant-linked CTAs. Search Console stays in the loop. We also verify headers and robots, and we bump lastmod on content changes.

identity: 0-5
services: 0-5
areas: 0-5
contact/CTA: 0-5
safety/no-promises: 0-5

Results we expect

Early wins show up fast when assistants can read your brief. The bigger gains arrive when proof accrues and internal links mature. We publish case studies with ranges rather than inflated promises.

Checklist to copy

• One typed source of truth for services, proof, and CTAs
• /llms.txt that states who you are and how to contact you
• JSON-LD that mirrors visible content and stays valid
• Sitemaps that reflect reality with sensible lastmod
• Guardrails for assistants - answer templates and do-don’t rules
• A weekly review of coverage, accuracy, freshness, and conversions