Answer Engine Optimisation for Ecommerce: The Complete AEO Library

Answer Engine Optimisation (AEO) is the practice of getting your products cited inside AI answers: ChatGPT, Perplexity, Claude, and Google AI Overviews. Where SEO competed for a blue link a human clicks, AEO competes to be the source an engine quotes back when a shopper asks "what should I buy?" The work splits cleanly into making pages readable by agents, structuring your data, supplying evidence worth quoting, and measuring whether you actually get cited.

This is the hub for the whole topic. Every article below is part of one cluster; the framing under each link tells you what it answers and when to read it. You can start anywhere, but the order roughly follows the AEO journey: get crawlable, get structured, supply evidence, get cited, then measure.

Start here: AEO foundations

If you read nothing else, read these. They define the field, give you a scorecard to grade your store, and explain the agentic-commerce shift that makes AEO matter at all.

• The 2026 AEO playbook — the end-to-end strategy for getting your store cited by AI engines, start to finish.
• The 42-signal AEO scorecard — grade your store against every signal an answer engine weighs, and find your weakest layer fast.
• What is agentic commerce — a plain definition with worked examples of agents that browse, compare, and buy.
• The agentic AI shopping primer for merchants — what changes for your store when the buyer is a model, not a person.

Make your pages readable by agents

An agent cannot quote what it cannot fetch or parse. This group covers crawlability, the divergence between AI crawlers and Googlebot, and the small files and feeds (llms.txt, MCP) that hand your catalogue to agents directly instead of hoping they scrape it.

• Make product content readable by AI shopping agents — the practical checklist for content an agent can actually parse.
• Your highest-converting page may be invisible to AI — why a great PDP for humans can be a blank to a model.
• GPTBot vs Googlebot — how AI crawlers behave differently, and what your robots rules are quietly blocking.
• llms.txt: the tiny file deciding your catalogue's fate — what to put in it and why agents read it first.
• MCP tool calls and product feeds — the merchant's guide to letting agents query your catalogue as a tool.

Structured data and schema

Structured data is the language agents trust most: it states price, availability, ratings, and product facts without ambiguity. These pieces cover the JSON-LD shapes engines quote, which schemas matter for agentic commerce, and how to mark up your reviews and UGC so they count.

• Twelve JSON-LD shapes agents quote — the exact structured-data patterns that show up inside AI answers.
• Schema.org for the agentic era: 14 schemas ranked — which markup earns the most citations, ordered by payoff.
• Structured data and schema for product UGC — how to make customer photos and videos legible to engines.
• AggregateRating schema and rich snippets — getting your star ratings parsed correctly and surfaced honestly.

Evidence and citations: why you get quoted

Engines do not quote claims; they quote evidence. Reviews and UGC are the evidence layer, and verified customer content is what an answer engine leans on when it has to justify a recommendation. This is also where Idukki's reviews and UGC surfaces do their heaviest lifting, the same evidence that drives a measurable UGC conversion-rate boost for human shoppers.

• Reviews as evidence: verified content cited 14x more — the data behind why engines prefer verifiable customer proof.
• AEO for reviews — getting your ratings and review text quoted by agents, not ignored.
• Agentic SEO for UGC — structuring customer content so agents can lift and attribute it.
• Brand authority for agents with zero backlinks — how engines judge trust without the old link graph.
• The seven-second window — treating your PDP as a snippet an engine can quote at a glance.

How agents choose products and appear in AI Overviews

This is the behavioural layer: what actually happens when a model picks one store over another, how to land in Google AI Overviews, and how to audit a PDP that an engine is currently refusing to recommend.

• How AI agents actually choose products — 12 real conversation teardowns showing the decision in motion.
• How to appear in Google AI Overviews — the concrete moves that get a store surfaced in SGE answers.
• Why a PDP fails the ChatGPT audit, and how to fix it — a teardown you can run against your own product page.
• Will AI agents recommend your store? — a 2026 readiness check across every layer.
• When Perplexity bought from our store — what an actual agent purchase looked like end to end.

Research and data

If you want the evidence rather than the argument, these are the studies: large-scale prompt research on who gets cited, the citation gap across hundreds of brands, and the state of UGC in AI commerce.

• The citation gap — what 1,200 brands looked like across ChatGPT over 90 days.
• Which AI engine cites you — a 1,200-prompt study comparing engines on who they quote.
• The state of UGC in AI commerce, 2026 — where customer content sits in the new buying funnel.

Architecture: the agentic commerce stack

For builders and technical leads, these two zoom out to the whole system: the reference architecture for an agent-ready store, and what agentic commerce means at the platform level.

• The agentic commerce stack — a reference architecture for a store agents can read, query, and buy from.
• What agentic commerce means for merchants — the platform-level view of the shift, without the jargon.

Where Idukki fits is the two layers that are hardest to fake: structure and evidence. Idukki's agentfeed publishes your catalogue as public JSON plus JSON-LD, an llms.txt manifest, and an MCP feed agents can call as a tool, so the structured layer is handled for you. The reviews and UGC surfaces (Instagram, TikTok, YouTube, plus Google Reviews, Trustpilot, and Feefo) supply the verifiable customer evidence engines prefer to quote. Together that maps onto Layers 2 and 3 of the journey above.