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Expert insights on SEO, generative search optimisation, and the future of AI-driven discovery.
Latest Articles
Google just told you GEO is SEO. What about the other LLMs?
On 15 May, Google published its first consolidated guide to optimising for its own generative AI features in Search — AI Overviews and AI Mode. Its position, stated plainly: from Google Search's perspective, optimising for generative AI search is optimising for the search experience, and is thus still SEO.
SEO Metrics Trends for 2026: A Comprehensive Guide
Navigate the future of search with confidence. Discover the metrics, predictions, and strategies that will define SEO success in the age of AI-powered discovery. The search landscape is evolving faster than ever. AI-powered discovery, generative search experiences, and shifting user behaviours are fundamentally changing how we measure SEO success. This guide breaks down the metrics, predictions, and strategies you need to stay ahead in 2026.
Google's New Search Console AI Reporting: What It Shows, What It Doesn't, and What to Do With It
On 3 June 2026, Google launched dedicated Generative AI Performance Reports in Search Console — the first purpose-built view into how your content appears inside AI Overviews, AI Mode, and Discover's AI features. Here's exactly what the data shows, what it's still missing, and how to use it.
We Didn't Wait. Here's What 12 Months of Actually Optimising for Generative Search Taught Us
While most of the industry was debating whether generative search would matter, we were already running live client programmes inside it. We've built our own tooling, developed a methodology, and seen what works — and what doesn't. This is a field report, not a forecast.
Google didn't make AI search the default. It did something quieter
At I/O 2026, the headline traveled fast and survived scrutiny badly. Here's what actually changed on 19 May — and why the real shift is both smaller than the panic and more consequential than the relief.
The Browser Is the New Battleground
For the past two years, the conversation around generative search optimisation has focused almost entirely on model quality. Which LLM is most accurate? Which one hallucinates less? Which one cites the best sources?