This research hub collects Anup Srivastava's work on how AI search systems choose, cite and recommend sources, and what that means for brands.
Research questions
- Which sources do AI tools cite most in a category, and why?
- How consistent are AI answers across days, locations and models?
- What page structures are quoted most often?
- How do entity signals affect brand descriptions in AI answers?
- How should AI visibility be measured for business decisions?
GEO, Generative Engine Optimization
Structuring content so it gets cited and recommended directly inside AI-generated answers from ChatGPT, Gemini and Perplexity, not just ranked on a results page.
AEO, Answer Engine Optimization
Winning featured snippets and direct-answer placements across Google and other answer engines, where the goal is the answer itself rather than the click.
LLMO, LLM Optimization
Structuring and formatting content, entities, schema and clear factual claims, so large language models parse, trust and surface it accurately.
Technical and classic SEO
Crawlability, site speed, structured data, keyword and intent research, and on-page optimization for traditional Google rankings.
SXO, Search Experience Optimization
Converting the traffic that still clicks: turning search visitors into engaged users and customers.
Organic traffic can hold steady on rankings while still declining in practice, because users are getting their answer from an AI tool before ever clicking through. The fix is to treat SEO, GEO, AEO, LLMO and SXO as one connected system: audit where a brand is cited, or isn't, across AI answers, then build the content and technical foundation to close that gap.
Anup Srivastava, field note
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