01 / generative engine optimisation

AI Systems & Search Integration

Most businesses are using AI like a slightly smarter search engine. That's not integration. That's a different browser tab.

The shape of the problem

There is a gap between what most businesses call using AI and what actually using AI looks like.

Giving your marketing team a ChatGPT subscription is not an AI strategy. It's a starting point, and not a particularly impressive one. Real AI integration for search means building systems: automated pipelines, connected data sources, and AI that runs inside your existing processes rather than sitting awkwardly alongside them.

For search visibility specifically, it means understanding that the rules have changed. Google is no longer the only machine you are optimising for. ChatGPT, Perplexity, Google AI Overviews, and Gemini are now part of the search landscape.

I build both sides of this. The visibility strategy and the infrastructure that supports it — including the MCP servers, automation pipelines, and connected workflows that make AI genuinely useful rather than a tool you have to manually feed data into.

Three Things That Now Have to Work Together

Generative Engine Optimisation

GEO is the practice of making your content visible and citable across AI-powered search systems, not just Google. The content has to be structured clearly, attributed to a credible source, and built to be extracted and cited — not just ranked.

AI workflow architecture

AI can transform crawl data analysis, content brief generation, rank tracking interpretation, competitive research, and internal linking audits — but only when it is properly connected to your data and your workflow. Without the infrastructure, you are still doing most of the work by hand.

MCP server builds

MCP (Model Context Protocol) is the open standard that lets AI systems connect directly to external tools, data sources, and real-time information. Instead of manually feeding an AI your crawl reports or keyword data, an MCP server gives it direct access. That changes what AI can actually do inside your SEO operation.

What I Build

GEO and LLM visibility strategy

Auditing where you are being cited, where you are not, and building a content and structure strategy that improves both. Includes AI Overview readiness, citation path analysis, and content structured for extraction.

AI Overview optimisation

Understanding how Google's AI Overviews select and surface content, what formats they prefer, and how to structure information so it is both findable and extractable.

MCP servers for SEO and marketing data

Custom MCP servers that connect your AI tools directly to data sources: Semrush, Ahrefs, Google Search Console, Screaming Frog, custom crawl pipelines, reporting systems. The difference between an AI that helps and an AI you have to babysit.

AI-augmented SEO workflows

Designing workflows that integrate AI into your team's actual SEO operations. Rank drops flagged automatically. Crawl anomalies surfaced in the right place. Technical issues prioritised by actual impact. Not generic prompts in a document — proper systems connected to your data.

Bespoke AI solutions for search

Custom builds for businesses that need something specific. If your problem does not fit a standard service description, that is usually where the interesting work happens.

Who This Is For

Businesses that have invested in SEO and want to future-proof it

AI visibility is built on top of traditional SEO, not instead of it. The foundations still matter — they just have more jobs now.

Marketing teams with AI tools but not AI systems

You've got the subscriptions. What you do not have is the infrastructure that makes them useful at scale.

SEO teams drowning in data but starved of signal

Most SEO tools produce enormous output. Knowing what's actually important, in what order, and why is still mostly manual. It doesn't have to be.

Agencies that want to operate at a higher level

Automating the data grunt work means senior people spend their time on the things that actually need senior thinking.

Businesses preparing for the next three years of search

The rate of change in AI-powered search is real. Being early is genuinely valuable — and the infrastructure you build now compounds.

A Note on What This Is Not

This is not AI content generation. I'm not here to help you produce more content faster with a prompt template.

The businesses winning in AI-powered search are not winning because they published more. They're winning because their content is better structured, more credible, more specific, and built to be used by machines as well as read by humans.

That is a different problem. It needs a different solution.

It is also not demo-stage experimentation. Building something that works properly in a production workflow is different from getting an AI to do something impressive in a five-minute walkthrough. The gap between those two things is where most implementations quietly fail.

Want to know where your business actually stands in the AI search landscape?

Let's talk about what it would take to build the system properly — not just bolt something on.

Get in touch

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