# Generative Engine Optimization in 2026: how to get cited by ChatGPT, Perplexity, Claude and AI Overviews
> Canonical URL: https://argosor.com/guides/geo-llm-visibility-optimization-2026
> Markdown Twin: https://argosor.com/guides/geo-llm-visibility-optimization-2026.md

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.