Definition
AI indexing is the set of processes by which AI systems discover web content, store it and make it retrievable for generated answers. Unlike classic crawling and indexing for a single search engine, a page can enter AI systems by several routes, each with its own timing and controls.
The main routes are:
- training data, where crawlers such as GPTBot and ClaudeBot collect pages that may shape a future model's parametric knowledge; changes reach answers only when a new model ships
- AI search indexes, such as those built by OAI-SearchBot, Claude-SearchBot and PerplexityBot, plus the Google and Bing web indexes that ground AI Overviews, AI Mode, Gemini and Copilot; these update continuously
- live fetches, where a user-triggered fetcher such as ChatGPT-User or Perplexity-User retrieves a page at answer time, which needs no prior index entry but depends on the page being reachable
- embedding indexes, where retrieved passages are split into chunks and stored as embeddings for matching by meaning
Three differences from classic indexing matter in practice. Retrieval often works at passage level, so a clear, self-contained section can be lifted out of a long page (content chunking). Each platform keeps its own index, so being in Google's index helps with AI Overviews and AI Mode but says nothing about ChatGPT or Claude. And the controls are per bot: blocking OAI-SearchBot removes a site from ChatGPT search answers, while disallowing Google-Extended stops use in Gemini training and grounding without affecting Google Search.
Monitoring has improved during 2026. Bing Webmaster Tools added an AI Performance report in February 2026 that shows how often your pages are cited in Copilot and Bing's AI summaries, along with the grounding queries behind those citations. Google Search Console added generative AI performance reports in June 2026, available to all sites by August 31, 2026, which show impressions in AI Overviews and AI Mode by page, country and device. ChatGPT, Claude and Perplexity offer no equivalent console, so teams infer their indexing from AI crawler logs and from whether the pages get cited in tracked answers (retrieval coverage). Promptwatch's search insights for Google and Bing bring the two console views next to AI answer data.
The technical basics are the same everywhere. Serve important content in server-rendered HTML. Allow the search and fetch bots you want in robots.txt, and make sure firewall rules do not answer them with 403s. Keep XML sitemaps accurate, with real lastmod dates. Use stable canonical URLs and redirect old ones after migrations. IndexNow notifies Bing and other participating engines about changed URLs, which helps content reach Copilot faster.
Examples of AI Indexing
- A SaaS company finds PerplexityBot fetching its blog but getting 403s on /docs, fixes the firewall rule, and sees docs pages start appearing in Perplexity citations for technical prompts.
- A retailer's Search Console generative AI report shows AI Mode impressions concentrated on 30 category pages, so it prioritizes those pages for structured product data and fresher copy.
- After a migration without redirects, ChatGPT-User keeps requesting old URLs that return 404; restoring the redirects brings the lost citations back.
- A publisher disallows Google-Extended to keep its content out of Gemini training, then confirms in Search Console that its AI Overviews impressions did not change.
