How to Get Recommended by ChatGPT: The Business Guide

Why ChatGPT doesn't recommend you today

When someone asks ChatGPT which provider fits their situation, it names two or three options with reasons attached. It isn't guessing: it's drawing on what it has read about each provider, on websites, in directories, in articles, in reviews.

The most common reason for being left out is mundane: the model simply can't find a solid answer to that specific question about you. Not because your business is weak, but because your strengths live nowhere in a form a machine can lift and use as an answer. "We offer unforgettable experiences" answers no customer question. "Dogs welcome, 15 euros a night, fenced run" answers a very specific one.

The second reason is that your facts disagree with each other. Three listings, three different sets of hours, and the model has no way to know which one is right, so it either leaves you out or guesses wrong. For a system built to answer questions, being reliable matters more than being persuasive.

Step 1: measure before you touch anything

Before you change a single page, you need a baseline. Without one you're optimizing blind, and you'll never be able to prove what actually worked. A real baseline puts hundreds of genuine customer questions to several AI models, repeated more than once since AI answers vary, and scores the results: are you mentioned? Recommended? Named first? Who gets recommended instead?

The output is more than a score. Every question you lose is a concrete work order — it tells you exactly which answer is missing from the web. That baseline becomes the blueprint for everything after it, which is why it comes first and not last.

Step 2: build answer pages for the questions you're losing

The strongest lever you have: for every important question where you're currently missing, build a page that answers that exact question, completely. One question, one page, one clear answer in the opening paragraph — self-contained, backed by concrete facts, no marketing language.

The opening paragraph is what decides it. It has to stand on its own as the answer: who, what, where, what it costs, what sets it apart. Everything else — images, detail, backstory — can follow, but the answer comes first. That's the kind of text a model can pick up and use directly.

Prioritize by how close a question sits to a purchase decision. "Which provider should I use for…" and "what does … cost" outrank general background questions. Ten precise answer pages beat a hundred thin ones.

Step 3: make your website machine-readable

AI models don't read the way people do. They need structure: headings phrased as the question itself, facts in lists and tables instead of flowing prose, and structured data (Schema.org) that pins down your name, location, hours, and offering precisely. Google's own guidance on AI features is explicit that none of this requires special AI-only markup or a separate llms.txt file to be found — there's no dedicated format the major engines require, and writing a separate version of your content "for AI" is something Google specifically warns against.

None of this is invisible magic and none of it is something a visitor notices. But it decides whether a model understands your content in seconds or gives up and cites the competitor whose page it could actually parse.

Step 4: earn mentions where AI models actually read

AI models don't take your word for it — they check what other sources say about you. Industry directories, trade press, regional media, review platforms: every consistent mention is evidence that you exist and that you deliver on what you claim. This matters more than it might look: an Ahrefs analysis of roughly 75,000 brands found that how often a brand is mentioned across the web correlates with its AI citation rate at about 0.664 — roughly three times stronger than the correlation for backlinks, at about 0.218.

Quality beats volume here. One clean listing in the leading industry directory and one piece of regional press coverage do more than fifty duplicate directory entries with conflicting details. Every new mention should carry the same core facts: same name, same address, same description of what you do.

Step 5: clear out the contradictions

Before new content can do its job, old errors have to go. A consistency audit collects every place your business shows up online and reconciles the facts — hours, prices, address, services. Every contradiction is an invitation for a model to guess, and hallucinations show up exactly where sources disagree with each other.

It's unglamorous work with an outsized payoff: it's cheap, it's fast, and it raises the reliability of everything else you do.

Step 6: re-measure, every month, same questions

The loop closes with a repeat measurement: same questions, same models, same yardstick. Only then can you see whether the curve is moving, which action actually worked, and which questions to go after next. AI visibility isn't a project with an end date — it's a control loop: measure, act, prove it.

A realistic expectation is early movement in four to eight weeks, with substantial shifts taking closer to half a year. Starting now buys you a lead that's hard for competitors to close later — models only learn as fast as new material about you appears, and worth noting: an Ahrefs study found only about 6–8% of URLs ChatGPT cites overlap with a page's Google top-10 ranking for the same query, and around 80% of ChatGPT's cited URLs don't even appear in Google's top 100. Getting cited by an AI model is a different game from ranking on Google, run by different rules.

The 90-day roadmap

Here's the sequence that makes all of this come together:

• Weeks 1–2: baseline measurement. Score, lost questions, and the competitive picture — your blueprint is set.
• Weeks 3–6: foundation. Clear out contradictions, make the website machine-readable, build the first answer pages for the highest-value questions you're losing.
• Weeks 7–12: reach. Build mentions across directories and media, add more answer pages, run the second measurement.
• Day 90: comparison report. Baseline versus current state, in black and white — what moved, and what's next.