Everything we've written about AEO in 2026 so far

Answer-engine optimisation (AEO) is moving fast. Over the past few months, we've published a run of posts on a range of core topics that aim to help founders and marketers win in this area. If you've missed some of them, here's the full picture in one place.

How AI recommendations actually work

Before you can optimise for AI visibility, it helps to understand what's happening under the hood.

How AI assistants decide which brands to recommend breaks down the mechanics: training data, retrieval systems, and the real-time signals that push some brands to the top of AI-generated responses. The key takeaway is that AI recommendations aren't random. They follow logic you can learn and act on.

Content rules that determine AI visibility picks up from there and gets practical. Vague content gets filtered out. Specific, structured content gets surfaced. The post covers entity statements, named frameworks, FAQ-style formats, and why defining your terms is more than good writing.

Tracking your AI brand visibility

Once you understand how the system works, the next question is how to measure where you stand.

Metrics that matter for AI brand visibility and How to plan an AI brand visibility tracking project sit well together. The first explains what to measure: share of voice, position, framing, and sentiment. The second explains how to set up a tracking programme properly, from defining your objectives and building a competitor inventory to writing prompts that actually reflect how your customers search.

7 things you need to know about AI brand visibility tracking covers the technical variables many marketers haven't encountered before: how model temperature affects which brands surface, why raw mention counts mislead, and what makes AI tracking meaningfully different from SEO monitoring or social listening.

What citations really mean in AI brand monitoring goes deeper on one specific signal. In AI responses, a citation isn't a directory listing. It's evidence of what the model read before it decided to mention your brand. Understanding citations lets you move beyond whether you're mentioned to why you're mentioned, which is where the real improvement opportunities are.

The content quality problem

One post stands apart from the rest but it's arguably the most important one if you're producing content at any volume.

Customers can tell your content is AI generated and it's hurting your brand covers research from a study of 3,000 adults that found content suspected of being AI-generated was rated significantly less trustworthy and less authentic. And shockingly it didn't matter whether the content actually was AI-generated. The suspicion alone was enough to damage credibility. That has direct implications for both your human audience and the AI models learning from your content.

Where to start

If you're new to AEO we'd suggest reading in roughly this order:

1. How AI assistants decide which brands to recommend - the foundation

2. Content rules that determine AI visibility - how to optimise your content for AI visibility

3. Metrics that matter for AI brand visibility - how to measure progress

The tracking and citations posts are worth reading once you've got something running. And the AI-generated content piece is worth reading regardless of where you are.

We'll keep adding to this category as the space develops. If there's something you'd like us to cover, get in touch.