Answer Engine Optimisation (AEO) is the practice of making sure a business is represented well when customers ask AI assistants for recommendations, comparisons, or answers — instead of (or alongside) typing queries into a search engine. If you’ve come across AEO elsewhere, you may also have seen it called Generative Engine Optimisation (GEO) or LLM Optimisation (LLMO). The terms all refer to the same underlying discipline. Onsomble uses AEO because it emphasises what actually matters: the customer isn’t running a search, they’re asking a question and getting an answer.Documentation Index
Fetch the complete documentation index at: https://docs.onsomble.ai/llms.txt
Use this file to discover all available pages before exploring further.
Why AEO exists now
Customer discovery journeys have shifted. When someone wants to find a plumber, compare insurance providers, or choose a product, they increasingly ask an AI assistant instead of running a search. ChatGPT, Claude, Gemini, and Perplexity are now real entry points to a buying decision — not just curiosities. Two consequences for businesses:- Your presence in AI assistant answers is now part of how customers decide. If the assistant doesn’t mention you, the decision happens without you.
- Whoever gets mentioned — and how they get mentioned — has material business impact. Prominence, accuracy, and framing in an AI answer matter in the same way that ranking in a Google result mattered before.
How AEO differs from SEO
AEO and SEO share some DNA — both are about being visible to the systems customers use to find you. But the practical mechanics diverge quickly.| SEO | AEO | |
|---|---|---|
| What you’re optimising for | Ranking in a list of blue links | Inclusion, prominence, and accuracy inside a generated answer |
| How you measure success | Position, click-through rate, organic traffic | Citation frequency, share of voice, sentiment, accuracy |
| What the system is doing | Indexing pages and ranking them by relevance and authority signals | Synthesising an answer from multiple sources, often without showing the sources |
| How fast things change | Relatively stable — ranking shifts over weeks or months | More volatile — model updates can shift what’s said about a business overnight |
| What the customer sees | A list of links they can click to explore | A direct answer, often with no exploration needed |
| Lever: keywords | Central | Mostly irrelevant — what matters is whether the content explains the business well |
| Lever: backlinks | Still a factor | Less direct, but authoritative source signal still matters |
| Lever: content clarity | Important | Critical — LLMs draw from content that’s structured and unambiguous |
| Lever: third-party signal | Important (reviews, mentions) | Very important — reviews, directories, and press shape how AI describes a business |
What AEO actually involves
In practice, AEO is a loop:Measure
Run realistic customer questions through the major AI models. Capture what they said. Turn that into measurable numbers — citation frequency, share of voice, sentiment — that compare you against your competitors.
Interpret
The numbers are only useful if you can explain what they mean. Where are you
strong? Where are you weak? What specifically is being said, and why?
Act
Translate insights into changes — content you publish, accuracy you correct,
positioning you sharpen, third-party signal you build.
What AEO-friendly content looks like
A handful of patterns consistently produce better AI representation:- Direct answers to specific questions. Content structured as “What does X do?” / “Answer: X does…” is more extractable than the same information buried in marketing prose.
- Clear positioning. Specificity about who the business is for and what it’s best at shows up in how the AI describes it.
- Factual accuracy on key details. Pricing, service area, hours, product lineup. AI models can and do reproduce these verbatim when they’re easy to extract.
- Consistency across the web. If your website, your directory listings, and your press all describe you the same way, that consistency reinforces the signal models pick up on.
- Structured formats where they make sense. FAQs, comparison pages, and explicit Q&A formats work disproportionately well.
Where AEO is going
A few tentative observations about the direction of the field:- The tooling is still young. Most established SEO tools are still figuring out what AEO looks like in their products. Purpose-built platforms like Onsomble will lead until the incumbents catch up.
- Interactivity is the next layer. Discoverability is the first wave. The next wave is businesses being able to do things through AI assistants — answering questions, taking bookings, providing quotes — not just being mentioned. This is what Onsomble’s Workflows pillar is aimed at.
- The metrics will mature. Right now, AEO metrics borrow a lot from SEO metaphors. As the discipline settles, more AEO-native measures will emerge.
Onsomble’s take on AEO
Onsomble is built specifically for AEO. The Discoverability pillar — scanning, measuring, recommending — is the measure-interpret-act loop made explicit. See:AEO overview
How Onsomble implements the AEO loop in practice.
Setting up a scan
Configure a scan to measure your business’s current AEO state.