How to Get Recommended by ChatGPT: The Business Guide

Why ChatGPT doesn't recommend you today

When someone asks ChatGPT which provider fits, the AI names two or three – with reasons. It does not pick these names at random: it draws on what it has read about providers. On websites, in directories, in articles, in reviews.

The most common cause of invisibility is therefore mundane: the AI simply finds no solid answer about you for the specific question. Not because you are bad – but because your strengths are written down nowhere in a form a machine can use as an answer. "We offer unforgettable experiences" answers no customer question. "Dogs welcome, 15 euros per night, fenced run" answers a very specific one.

The second cause: your facts contradict each other. Three portals, three different opening hours – the AI does not know which is right, and in doubt leaves you out or guesses wrong. For an answer machine, reliability beats advertising.

Step 1: Measure first, then work

Before changing anything, you need the baseline. Otherwise you optimize blindly and can never prove what worked. A proper baseline measurement asks hundreds of real customer questions to several AIs – multiple times, because AI answers vary – and scores: are you mentioned? Recommended? First? And who gets recommended instead?

The result is more than a number. Every lost question is a concrete work order: it tells you exactly which answer is missing on the web. The measurement is the blueprint for everything that follows – which is why it comes first, not last.

Step 2: Build answer pages for lost questions

The strongest lever: for every important question where you are missing, create a page that answers exactly that question completely. One question, one page, one clear answer in the first paragraph – self-contained, with concrete facts, no marketing language.

The first paragraph decides. It must stand alone, as if it were the answer: who, what, where, what it costs, what makes it special. Everything else – images, details, stories – may follow, but the answer comes first. That produces text an AI can adopt verbatim.

Prioritize by purchase proximity: questions close to a decision ("Which provider…", "What does … cost") before general knowledge questions. Ten precise answer pages beat a hundred thin subpages.

Step 3: Make the website machine-readable

AIs read differently from people. They need structure: headings that carry the question. Facts in lists and tables instead of florid prose. Structured data (Schema.org) that pins down name, location, hours, and offering. And an llms.txt – a short table of contents for machines that says where the most important answers live.

None of this is magic, and none of it is visible to visitors. But it decides whether a machine grasps your content in seconds – or gives up and cites the competitor whose site it understands.

Step 4: Build mentions where AIs read

AIs do not trust your website alone – they check what others write about you. Industry directories, trade press, regional media, review platforms: every consistent mention is evidence that you exist and deliver what you promise.

Quality beats volume. One clean entry in the leading industry directory and one article in regional media do more than fifty directory duplicates with diverging data. Every new mention should carry the same core facts: same name, same address, same description of services.

Step 5: Clear out contradictions

Before new content can work, old errors must go. A consistency audit collects every place your business appears online and reconciles the facts: hours, prices, address, services. Every contradiction is an invitation for the AI to guess – and hallucinations arise precisely where sources disagree.

It is unglamorous work with a large effect: cheap, quick, and it raises the reliability of every other measure.

Step 6: Re-measure – every month, same questions

The loop closes with the repeat measurement: same questions, same AIs, same yardstick. Only then do you see whether the curve rises, which action worked, and which questions come next. AI visibility is not a project with an end date but a control loop: measure, close, prove.

Realistic expectation: first movement after four to eight weeks; substantial shifts take half a year. Starting early buys a lead that is hard to catch – AIs, too, only learn as fast as new material appears.

The 90-day roadmap

This is the sequence when it all comes together:

• Weeks 1–2: baseline measurement. Score, lost questions, competitor picture – the blueprint stands.
• Weeks 3–6: foundation. Clear contradictions, make the website machine-readable, build the first answer pages for the most important lost questions.
• Weeks 7–12: reach. Build mentions in directories and media, more answer pages, second measurement.
• Day 90: comparison report. Baseline versus status quo – in black and white, what moved and what comes next.