Buyers now ask AI to recommend a firm.It answers with a few names.
That shortlist is becoming the first step in B2B buying. The names on it are shaped by signals an assistant can read: technical access, structured content, corroborated authority. Most established firms hold the authority and carry none of the signals. The gap is measurable, and it can be closed.
Five minutes. Your domain. The answer AI returns about you today.
How an assistant decides which firms to name.
An assistant does not browse. It reconstructs an answer from what it can reach, parse and corroborate. Three conditions decide whether a firm appears.
Can it access your site?
Assistants read through crawlers such as GPTBot, ClaudeBot and PerplexityBot. If those are blocked, or your content renders only in the browser, the firm is invisible before anything else is considered.
Can it parse your content?
A model favours content it can lift cleanly: a direct answer near the top, clear headings, structured facts. Prose written only for human skim-reading is hard to quote, so it is rarely quoted.
Does the web corroborate you?
A model weights consistency across independent sources. A firm described the same way across its profiles, directories and press reads as a known entity. Fragmented signals read as uncertainty.
Discovery is moving from a page of links to one answer.
The share of buying journeys that begin with an assistant is rising. The figures below describe direction, not a finish line.
The opening is structural, and it is open now.
Because assistants weight consistency and corroboration, the firms that become readable first tend to stay named. Early position compounds: each citation makes the next more likely. While most of your peers remain invisible, the cost of moving is low and the position is winnable. That changes as the field fills in.
The AI-fy TRIAD.
AI visibility is a data-governance problem, not a marketing campaign. The TRIAD addresses the three layers an assistant reads, in order, and scores each on a transparent scale.
Reachable and readable
Crawler access, server-side rendering, Organization and Person schema, an llms.txt file, a clean sitemap. The technical foundation an assistant needs before anything else counts.
Structured to be quoted
Priority pages rebuilt answer-first, headings phrased as the questions buyers ask, facts in parseable tables and lists, the evidence density that earns a citation.
Corroborated as the source
A verified entity web, author signals, and consistent mentions on the third-party sources assistants cite. The layer that moves share of answer.
Firms whose reputation runs ahead of their visibility.
The essentials, in plain terms.
What is the difference between LLMO and SEO?
SEO earns a position on a page of links. LLMO earns inclusion in the single answer an assistant gives. SEO weights keywords and backlinks. Assistants weight technical access, structured content and corroborated authority. The two measure different things and move on different timelines.
How is AI visibility measured?
By share of answer: a fixed set of buying-intent prompts is run across ChatGPT, Gemini, Claude and Perplexity, and the rate at which a firm is named or cited is recorded. It is a percentage, tracked against a baseline, not a keyword position.
How long does it take to move?
The technical layer is readable within the first weeks. Movement in share of answer typically appears inside one to two months, and compounds from month four as corroboration accumulates. Progress is reported against the starting baseline at each stage.
Is this work compliant with EU data rules?
Yes. AI-fy.me is EU-based and works to European data-governance standards, distinguishing crawling from scraping and documenting the logic behind each change. For German firms, consulting of this kind is often eligible for BAFA support.
You can see the exact answer AI returns for your firm today.
It takes five minutes and your domain. The result is a measured starting point, not a sales call.
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