Answer Engine Optimisation. An AI answer names three vendors. We check whether it names you, and change the pages the model reads until it does.
This activity does not leave a trail through your site. Six numbers explain why the pipeline feels quieter than the market does.
Business buyers used an LLM during their most recent purchase. They rated it more useful than vendor websites, sales reps and product experts.
Use AI to compare vendors against each other. 54% research product information with it. 47% build the internal business case with it.
Of buyers said AI shaped their shortlist, 45% said significantly. And 95% of the time, the vendor who won was already on the day-one shortlist.
Chose a different vendor than they originally planned after using an LLM. A third of those bought from a company they had never heard of.
Notice a vendor in an AI answer because they recognise the name. Everyone else notices the vendor whose answer matched their problem. Reputation alone won't get you in.
Visitors from AI search convert at roughly four times standard organic traffic. Half a per cent of visits, twelve per cent of signups. Small channel, ready buyers.
Sources: Forrester 2026, Semrush 2026, 6sense 2025, G2 2026, Ahrefs 2025.
A buyer rules you out in an AI chat. They never open your site, never fill in a form and never reach your CRM. No dashboard shows it, so nobody on your team has a reason to act on it.
Between 2024 and 2026, the share of Google searches producing at least one click fell by 9.5 points. When an AI Overview appears, clicks to the top result drop by roughly 58%. Rankings stay but traffic doesn't follow. Most teams blame it on a Google update.
Optimises your own pages so they rank in Google's results.
Makes sure the pages an LLM reads while writing its answer include yours.
The two overlap, and most SEO retainers don't cover this. It's a scope gap, not a performance problem with your agency.
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The model reads a specific set of pages and paraphrases what they say. Those pages can be found, and you can appear on more of them.
We ask ChatGPT, Claude, Gemini and Perplexity the questions your buyers ask, and show you who gets named.
We work out what your buyers actually type into an LLM. Not the keywords they used to type into Google.
There are eight kinds of pages a model tends to pull from. We tell you which ones matter in your category, and in what order to go after them.
Comparison pages, review site listings, analyst submissions, community threads, the odd Wikipedia edit. We do the work, or brief your team.
We ask the same questions every month and tell you what changed. Where you gained ground, where you slipped, what to do next.
Every release moves the answers around. We watch that so you find out from us and not from a quiet quarter.
ChatGPT, Claude, Gemini and Perplexity give different answers, and each update changes them. So we check all four.
Most pages a model reads belong to someone else. We cover your pages, the ones you can influence, and the ones you can join.
Fixed price, fixed scope. You read what we found, then decide. If it isn't useful, you stop there.
We publish placements, rerun the questions and send you a report each month. This is ongoing work, not a one-off.
At a mid-sized services company we worked with, the share of inbound leads coming from AI-driven discovery went from five per cent to thirty over the course of the engagement. Name withheld at their request.
We run twenty of the questions your buyers actually ask, in the major LLMs, and send back what came out. Then we can talk about what to do with it.
