What is LLM SEO, and is it different from AEO?

The short version

LLM SEO is the work of making your pages readable and citable by AI assistants such as ChatGPT, Gemini, Copilot, and Perplexity. The goal is to be named when a customer asks a question like "best savings account near me." It is not a separate standard defined by Google or anyone else. In practice it overlaps almost completely with AEO (answer engine optimization) and GEO (generative engine optimization). One discipline, several names.

Why the labels blur together

Each label came from a different corner of the industry. AEO came first, from the featured-snippet era. GEO came from academic research on generative engines. LLM SEO is the newest and the most literal: search optimization aimed at large language models.

The day-to-day work is the same under all three. You publish clear, current, structured facts about your products. You make sure machines can read them. You build the third-party mentions that assistants lean on when they decide whom to name. Anyone selling these as three separate services is slicing one job into three invoices.

What Google actually says

There is one place where a label meets a hard requirement, and it is worth separating the two. Google's AI Overviews and AI Mode are Google features with Google rules. LLM SEO as a broader practice is not.

Per Google's guide to AI features, its existing SEO best practices remain relevant for AI Overviews and AI Mode. Pages eligible as supporting links must be indexed and eligible for a Google Search snippet, with no additional technical requirements. Google also says no new machine-readable files, AI text files, or special schema.org markup are needed to appear in these features. So if a vendor tells you that you need a special AI file or special AI schema to show up, that claim does not come from Google.

One caution from the same source: satisfying those requirements does not guarantee crawling, indexing, or serving. Eligibility is the floor, not a promise. Nothing here guarantees inclusion in any AI answer, and you should treat anyone who promises placement with suspicion.

What is genuinely different from legacy SEO

The technical foundation carries over. The output does not.

A classic results page hands back ten links to browse. An answer engine does not hand back a list. It reads across sources, forms an answer, and names a small number of institutions, often with reasons. That changes what winning looks like in three ways.

First, there is no position five. You are named or you are not, and the answer often comes with a short rationale you need to be able to influence.

Second, there is no alert when it happens. Your rankings look fine, your traffic looks normal, and the question gets decided without you. A customer asks an assistant where to open an account, gets three names, and never visits a results page.

Third, the sources widen. Assistants pull from your site, but also from review platforms, comparison articles, forums, and rate aggregators. Your own pages are necessary and not sufficient.

What a financial marketer can check this week

You do not need a tool to start. You need an hour and a browser.

Ask the question yourself. Open two or three assistants and ask the questions your customers ask: best checking account in your city, credit union vs bank for an auto loan, who offers the product you lead with. Note whether you are named, who is, and what reasons are given. The reasons tell you which facts the engine found.

Check whether your facts are readable. Open your key product pages. Are rates, fees, eligibility, and terms in plain text, or trapped in images, PDFs, and rate tables rendered as graphics? Assistants quote text. If your effective date lives in an image, the engine cannot see it, and a stale or missing date is a common reason an institution gets skipped.

Check indexation. Confirm your product pages are indexed and can appear as a Google snippet. That is the stated eligibility bar for Google's AI features, and it is free to verify in Search Console.

Check your third-party footprint. Search for your institution the way a comparison writer would. If review sites and local coverage describe you inaccurately or not at all, assistants inherit that gap.

Check consistency. If your site says one APY, an aggregator says another, and neither shows a date, the engine has to guess. Dated, consistent facts across sources are the cheapest credibility you can buy. Rates, fees, and disclosures deserve a careful eye before anything ships, but the review process is yours to set.

How to tell whether it worked

Re-run the same questions on a schedule, monthly at minimum. Track three things: whether you are named, which facts get quoted, and whether those facts are current. Improvement looks like being named more often, for the right reasons, with the right numbers.

Checking by hand works until you want coverage across many questions, several engines, and every branch market at once. That is the point where Scanley measures this across the AI engines so you can see where you are named and why. Whether you measure by hand or with a tool, the fixes are the same: readable facts, current dates, consistent third-party mentions.

The short version

LLM SEO and AEO are the same discipline under different labels. Google has confirmed no special technical requirements exist for its AI features. The real shift is the output: a named answer instead of a link list. Your job is to make your facts easy to find, easy to read, and easy to trust, then verify by asking the questions your customers ask.

Questions people ask