The AEO agency for B2B software.

An answer engine does not rank ten links and leave the choice to the reader. It reads a set of sources, decides which passages answer the question, and writes a response, sometimes with citations and sometimes without. Your job is to be a source it can use and a passage it can lift.

That depends on three things. The engine has to be able to read your page, which means clean, crawlable HTML and, where relevant, structured data. It has to understand what the page is about and who published it, which is an entity problem. And the passage itself has to answer the question directly, early and specifically, so it survives being pulled out of context.

Most B2B software sites fall down on the third point. Pages open with positioning language instead of an answer, bury the detail below the fold, or spread one idea across several pages, so there is nothing clean for an engine to quote.

On the page, we rewrite key content so each section opens with a direct answer of a few sentences, followed by the evidence and detail. We add comparison tables, FAQ sections and clear headings that mirror the questions buyers ask. We write for a reader who has asked ChatGPT the same thing, and for the model that is about to summarise the page.

Behind the page, we review and implement structured data so engines can read your organisation, your people, your services and your content correctly. We fix technical blockers such as crawler access, rendering issues and broken internal linking. Our analysis of schema and generative search explains why this is groundwork, not a shortcut.

Across the web, we check how your company is described on the sites engines trust, and make it consistent. That includes directories, review profiles and any public record that tells an engine who you are. If one source says you are one thing and another says something else, engines tend to resolve the conflict by leaving you out.

Buyers do not care which acronym applies. They ask a question and either your brand is in the answer or it is not. We use AEO to describe the structural work that makes your content quotable, and GEO to describe the wider effort to be recommended, which also depends on what other sites say about you.

Run in isolation, each falls short. A perfectly structured site with no third-party presence gives engines little reason to trust it. A strong third-party footprint pointing at pages engines cannot parse wastes the effort. That is why every engagement covers both, under one plan and one owner.

AEO does not guarantee a citation. Engines change how they work often, and no agency controls their answers. Structured data on its own does not create AI visibility, and adding markup to a page that engines already cite will not necessarily lift it. We are specific about this because overselling schema is one of the commonest ways an agency wastes a budget.

What AEO does is remove the reasons you are being skipped, and give engines a clear, consistent, quotable version of your business. Then we measure what changes, by engine and by prompt, and adjust.

We track a fixed set of buyer prompts across ChatGPT, Copilot, Claude, Gemini, Google AI Overviews, Google AI Mode and Perplexity. For each we record whether you are mentioned, whether your pages are cited, where you appear in the answer and which competitors appear instead. Over time that gives you citation rate, share of voice and mention position, by engine, benchmarked against named competitors.

Where AI traffic reaches your site, we tie it to leads and pipeline. The data sits inside our ContextualJourney™ platform, and the full method is on our methodology page.

We specialise in B2B software and SaaS brands, most often at Series A and B. AEO pays back most where buyers research vendors before they speak to sales and where your category has several credible competitors. It matters less for brands that sell mainly through direct relationships, enterprise procurement or local search, and it is premature for pre-product companies.

Five formats do most of the work. Definitive guides give a direct, complete answer to a real buyer question, structured for extraction. Comparison tables set out honest versus and alternatives, the format AI reaches for at the evaluation stage. Case studies name the role, the context, the problem and a measurable outcome, because specificity beats praise. Tool reviews are first-person and expert-tested. Industry reports carry original data and research that competitors cannot copy.

Buyers increasingly talk to an assistant, and soon that assistant will talk to your systems directly. Being readable by a machine is part of the job: semantic HTML, structured data in JSON-LD, a clean robots.txt for AI crawlers, an llms.txt index file, and sitemaps, RSS and JSON feeds. Most of this is good technical SEO. The protocol layer, such as MCP, is the new part and still emerging.

Format matters too. In one example published by Cloudflare, serving a page as clean markdown cut the tokens an agent needed from 16,180 to 3,150, a reduction of about 80%. That is one text-heavy page, not a benchmark, and Google and Microsoft have discouraged publishers from building separate versions for AI, so we focus on clean source HTML first.