AEO - Answer Engine Optimisation - is an outsized lever for crypto and Web3 specifically because the audience is disproportionately early on LLM adoption. Roughly 15-25% of crypto-curious Indian users already start with ChatGPT, Perplexity or Brave AI for 'what is staking', 'best Indian crypto exchange', 'how does 1% TDS work' style questions - and that share is growing quarterly. If your content is the cited source, you get downstream traffic for free. If it isn't, you compete on Google alone, which is slow going under YMYL. Baclinc treats AEO as a first-class discipline alongside SEO: direct-answer paragraphs, FAQ/HowTo schema, tight H2 hierarchy, llms.txt and llms-full.txt, registration with AI indexes, and monthly citation tracking across the major LLMs.
LLMs cite content based on factors that partly overlap with Google's YMYL ranking and partly diverge. Overlap: author credibility (Person schema, visible bios, prior publications), factual tightness, schema coverage, site authority and topical depth. Divergence: LLMs reward extremely clean Q&A format, explicit direct-answer paragraphs, numbered sub-sections and FAQ schema far more than Google does. LLMs are also sensitive to recency in a way that's different from Google - ChatGPT's training cutoff and Perplexity's live-web fetch treat recent publication dates as a strong signal. For crypto specifically, the queries LLMs get asked are predictable: 'best Indian crypto exchange 2026', 'how does 30% VDA tax work', 'what is FIU-IND registration', 'is WazirX safe', 'how to buy Bitcoin in India'. Each of these is a content slot we can engineer for. The process is: identify target queries, inventory current coverage, build a direct-answer cornerstone per query, apply FAQ/HowTo schema, publish on the right sub-property, link into the topical graph, then track citations monthly. Typical timeline to show up as an LLM citation is faster than Google - we've seen pages cited within 2-4 weeks of publish when the authoring quality and schema are correct.
