Generative Engine Optimization in 2026: how to get cited by ChatGPT, Perplexity, Claude and AI Overviews

A practical playbook for measuring and improving how often AI answer engines cite your brand, using Argosor’s visibility audit.

Blue links are only half the funnel now

A growing share of high-intent software and B2B research now ends inside an AI answer. Those answers are built from sources the model can reach, read and trust: pages with clear entities, a useful /llms.txt, clean structure and connected Schema.org JSON-LD.

Measure before you optimize

Run geo_llm_visibility_audit over MCP, or call POST /v1/geo/audit, with a prompt your buyers actually ask. You get a readiness score for nine models, your citation rate in live sources, and a list of fixes ordered by impact.

Fix access first

The most common reason a model never cites you is that it can’t read you. Check that GPTBot, ClaudeBot and PerplexityBot get HTTP 200 at your firewall, that /llms.txt is real Markdown rather than an app shell, and that robots.txt declares Content-Signal: search=yes, ai-input=yes.

Then give models something to quote

Lead key pages with a short, factual answer, add a comparison table, and publish FAQPage JSON-LD. geo_reputation_and_writer drafts all three for you, and the Action Board stages them for approval.

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