When the answer arrives without a search result.
A growing share of buyers now ask an AI assistant instead of scanning ten blue links. The answer they get names some brands and not others. Answer Engine Optimisation is the work of being one of the brands that gets named — and of being described accurately when you are.
How answer engines decide who to name.
They synthesise from sources they consider reliable and from data they can parse unambiguously. Both of those are things you can influence.
Entity clarity
Making sure an assistant knows what your brand is, what it sells and how it differs — consistently, across every source it might draw from.
Machine-readable product data
Complete, structured, unambiguous product information, since a model cannot cite specifications it cannot parse. This is where your catalogue does the work.
Factual consistency
Auditing how your brand is described across your site, directories, marketplaces and third-party sources, because contradictions reduce the confidence to cite you.
Question-shaped content
Content organised around the questions buyers actually ask, in a structure that can be extracted and quoted rather than only read.
Citation source strategy
Being present and accurately represented in the sources answer engines lean on most heavily for your category.
Response monitoring
Sampling how assistants currently answer questions in your category, so you can see whether you are named, ignored or described wrongly.
An honest account of a new discipline.
Answer engines favour structured, consistent, well-sourced information. That much is observable, and it is largely the same hygiene that good SEO already demands.
- Structured data materially helps parsing
- Consistency across sources builds confidence
- Question-shaped content extracts more cleanly
- Complete product data beats sparse product data
Assistants rarely pass referral data the way search engines do, so attribution is weaker here than in any other channel and we will not pretend otherwise.
- Referral data is limited or absent
- Visibility is sampled, not comprehensively measured
- No reliable rank equivalent exists yet
- Results reported as observed, not modelled
We focus on work that pays off regardless of how these systems evolve, because that hedge is the responsible way to invest in an emerging channel.
- Structured data valuable for search and answers alike
- Factual consistency useful in every channel
- Content that serves readers first
- No spend on tactics likely to be corrected
The things buyers actually ask
There is real overlap — structured data, consistency and content quality serve both. The difference is that answer engines synthesise rather than rank, so being extractable and being accurately described matter more than position.
No. These systems are opaque, change frequently and give no ranking signal to work from. What we can do is remove the reasons not to cite you and monitor whether it improves.
The measurement is early; the work is not. Almost everything AEO asks for — structured product data, factual consistency, clear content — has independent value in search and on your own site regardless of how assistants develop.
What this connects to
Every module runs standalone and every module talks to the kernel. These are the ones most often deployed alongside it.
SEO
Technical, content and category SEO for eCommerce catalogues that need to be found.
Open page → Platform · AICAI Catalogue Engine
Turn a product photo into a complete, marketplace-ready listing your team approves.
Open page → Growth · GGLGoogle Ads
Search, Shopping, Performance Max and YouTube — measured against your own order book.
Open page →Find out how AI assistants describe your brand today.
A working walkthrough with your catalogue, your order flow and your questions. No slideware.