Being found on AI means moving from SEO to AEO
As search evolves from a list of links into a direct conversation, our Discovery is finding that Answer Engine Optimisation, or AEO, is an ever-increasingly-important strategy for brand visibility. Unlike traditional SEO, which prioritises click-through rates, AEO focuses on making your content the primary cited source for models like Gemini and ChatGPT by using structured schema markup, answer-first formatting and a high-authority digital footprint across the web. By optimising for how AI synthesises information, businesses can transition from simply ranking on a results page to becoming the ‘Single Source of Truth’ that AI assistants deliver directly to the user.
It’s 2026, and your potential customer is asking their AI assistant ChatGPT, Gemini or DeepSeek (and not Google), “Who is the best blank company in the UK?” Then, they’re getting a single, direct answer; maybe a shortlist of 3, and not a list of ten links to sort through. This year, the ‘search engine’ is becoming an ‘answer engine.’ So, if your website isn’t trying to provide answers that AI assistants understand, then you could be left behind.
What’s the shift from SEO to AEO all about?
SEO was search engine optimisation, or how sites used to make themselves attractive and relevant to Google and Bing. With AEO or Answer Engine Optimisation, this becomes the process of making your brand the definitive answer for AI models. The Wirebox team is paying attention to this shift for our clients as we want to help them become the cited source in synthesised AI responses.
The pillars of being AI-discoverable
To be found by AI, your content needs to be ‘machine-readable’ and ‘highly authoritative’ – we can do this in a few ways:
Structured data
We recommend using Schema Markup (JSON-LD) to tell AI exactly what your content is (FAQs, Products, Reviews), so that way, it understands your test. Also, don’t forget that being found through AI-interpreted images and video transcripts is also possible.
Answer-First content model
When writing, don’t be coy. AI loves front-loaded answers. This means moving away from long intros to TL;DR summaries at the top of your posts. Make content more bite-sized and scannable. Your readers AND the robots will thank you.
Entity authority & E-E-A-T
AI also trusts consensus. Being mentioned on Reddit, industry forums and news sites is now just as important as your own blog. It’s one of the many ways that AI looks for Experience, Expertise, Authoritativeness and Trust. So, you need to find a way to build consistent brand mentions across the web, too.
Things you can do today to optimise for AEO
You don’t need to wait for us to help you with our website (although we’d love to). Here are a few things you can do today to improve your standing with our future AI overlords:
Audit your FAQ
Are you answering the ‘Who, What, Why and How’ of your industry? Look at Answer the Public and other tools to see what your targets are interested in.
Optimise local pages
AI assistants now personalise answers based on geographic relevance… is your NAP (Name, Address, Phone) and location-specific data correct?
Simplify your speech
AI prefers clear, school-aged-level English. Keep your sentences short and use stats when you can. Then, link back to other reputable websites for an extra boost.
Build your footprint
Lastly, ensure your brand is mentioned on Reddit, Quora, industry-specific directories or news sites…the places AI models ‘crawl’ for consensus.
Using the right storage
Not all databases are meant for AI. Some are great for daily transactions, and others are better for analysis, history and large datasets. Our database design best practices for AI readiness mean you need to pick the right tool for the job, referring to your workloads above. Remember that AI data grows fast – often faster than expected – so your systems should stay quick and responsive even as data increases. Slow data access slows down AI development, so it should be avoided.
Access and protection matters
AI often uses sensitive customer or business data, so database designers need to control who can see what. Also, when you’re using AI, tracking where data comes from helps explain its decisions; so that’s a design consideration as well. Lastly, data shouldn’t require manual cleanup every time… teams need consistent, repeatable access, and well-designed pipelines reduce errors and delays to that access.
Overall, the success of your AI implementation depends heavily on your database design. Even non-technical teams should care about database decisions because planning ahead saves time, money and frustration. But you’re not designing for perfection. AI needs to evolve quickly, and AI databases should support that experimentation. When you talk with your database development team, make sure they believe that small improvements over time beat rigid designs that can’t react. That’s the right mindset for the AI era.
