I help you get more leads from search.
A large part of my work now is GEO or AI-search. I work out which questions your buyers are asking their LLM, who is being recommended and why, and what specifically needs to be done to get you into those answers.
Buyers do a lot of research before spending, but far less of that time is done on vendors' websites now than even 24 months ago. By the time someone reaches your site they've already built a shortlist around recommendations from their AI engine. To be consistently part of that recommendation set, you need your brand represented properly in the sources LLMs draw upon to answer commercially relevant queries for your market. This can include work both on your website and on third-party sites.
I build the pages that enter buying conversations: category, use case, comparison, integration and problem-led content, and do the editorial work that ensures consistency, clarity, and writing that doesn't seem like AI slop.
Technical work for GEO involves making sure AI search systems can easily find and understand your pages. Site architecture and internal linking, rendering, indexability, are the most important to get right. They also play an important role in traditional SEO, and I've spent a decade learning how to recognise and fix them.
Off the shelf software is great for a lot of jobs, but the ecosystem is built around traditional SEO. There are a lot of new tools appearing that claim to help with GEO, but many either try to be a one size fits all solution, cramming in masses of unneeded features, or they just don't provide the answer to the questions I need to have. When I need a specific set of answers, I build a tool for it: an AI visibility tracker that identifies how the LLMs are coming to their recommendations, sentiment around a set of prompts, the specific content that is getting your competitors mentioned, and a prompt builder that scrapes real customer language to show us what to track.
Share of search, LLM share of voice, assisted conversions, and a directional read on the channels that last-click no longer covers.
I have spent years reporting SEO to finance teams, so the tracking I set up answers the questions they ask.
I work inside your team
Your Slack, your project management, your reporting. I join the planning rather than running a separate consulting process alongside it, and I work directly with your writers, developers and product people.
I stay close to implementation
You won't get a strategy deck and a list of things for somebody else to figure out. I do the research, write the requirements, review the pages, and fix the technical problems.
I keep one prioritised roadmap
There is always more SEO work available than a company can ship. I keep a clear backlog, explain why things are ordered the way they are, and focus on the actions that move the needle.
Weeks 1 to 4
Technical and content audit with prioritised action plan. AI visibility baseline recorded. Competitor set and intent taxonomy agreed. Strategy set.
Months 2 to 3
Technical fixes done. Content production running. Editorial standard written and in use. First movement in AI answers.
Months 4 to 12
Content velocity holds while the programme expands into new commercial topics and page types. Technical work continues as the site grows. AI-search visibility compounds as the cited sources change and new pages start being pulled into answers. Reporting shows pipeline influence, and the roadmap is revised each quarter against what that data shows.
These came from running the channel week to week and staying accountable for the numbers.
This is the main way I work. I take on organic and AI search as part of your team: strategy, technical, content and reporting. Monthly retainer.
I have enough experience in marketing and revenue operations to understand how search fits into the wider business, and I can work with your existing attribution, CRM and reporting setup rather than around it. But SEO and AI search remain the focus.
For: B2B SaaS companies with no SEO leader in house.
A measured baseline of where you appear across ChatGPT, Claude, Perplexity and Google AI Overviews, against a competitor set and an intent taxonomy we agree first. You get the citation sources behind those answers, an explanation of why competitors are being recommended, and a roadmap for changing it. Runs on its own, or as the first six to eight weeks of a retainer.
For: SaaS teams who know AI search matters but cannot yet see where they appear, why competitors are winning, or what to change.
Topic architecture, AI drafting pipelines with a written editorial standard, programmatic page builds, and the QA pass that keeps them publishable.
For: companies that need to scale content without more headcount.
How identical tools and AI workflows have made SEO intellectually conformist, and why mental models are the real competitive differentiator.
There is less than a 1-in-100 chance that ChatGPT or Google’s AI will produce the same list of brand recommendations if you run the same query 100 times. This is the starting point for understanding why AI search attribution is a fundamentally different problem from traditional search tracking.
A guide on turning customer language from Google Reviews into structured messaging for website copy using a specific message-mining framework.
If you need someone to own organic and AI search at your B2B SaaS company, start a conversation or find me on LinkedIn.
I work with two or three retained clients at a time.