More and more people no longer type "restaurants near me" into a search box. They ask an assistant: "Where should we go for a good Italian dinner in Fitzroy tonight?" And they get back three or four names — not ten blue links. If your venue is not one of those names, you are invisible at the exact moment someone decides where to eat.
The good news: AI recommendations are not a black box built on ad spend. They are built on what already exists about you online. This guide covers how AI assistants choose venues, why yours may be getting skipped, and the specific things that get you into the answer.
Not sure where you stand? Run your venue through the free AI Visibility Checker — it asks ChatGPT and Gemini the questions your customers actually type and shows you who gets recommended instead of you.
How ChatGPT, Gemini and Google AI actually pick restaurants
There is no "AI ranking index" you can submit to. When someone asks for a recommendation, the model assembles an answer from three kinds of material:
- Your Google Business Profile — rating, review count, review text, category, photos, hours, and whether the profile is complete and consistent.
- What other websites say about you — local guides, "best of" lists, food blogs, news write-ups, booking platforms, tourism sites, forums like Reddit.
- Your own website — but only the parts a machine can read. A beautiful site that hides the menu in a PDF or an image tells the model almost nothing.
The model then matches that material against the phrasing of the question. This is why two venues with identical food get wildly different results: one has hundreds of reviews describing "wood-fired pizza", "date night" and the suburb by name; the other has hundreds of reviews saying "great!".
Why AI doesn't recommend your restaurant
In practice, it is almost always one of five reasons:
- Not enough recent reviews. Volume and recency both matter. A venue with 90 reviews rarely beats a neighbour with 900.
- Reviews that say nothing specific. If nobody mentions the dish, the occasion or the neighbourhood, there is nothing for the model to match a question against.
- You're missing from the guides. The "best restaurants in [city]" lists that already rank are the raw material for AI answers. If you are not on them, you are not in the answer.
- Thin online footprint. Few posts, few photos, few tags, nobody talking about you. Less material means fewer chances to be surfaced.
- An unreadable website. Menu as an image, no address in text, no cuisine description, no structured data.
Step 1: Get more reviews that contain the words people search
This is the single highest-leverage move, and it is not the same as "get more reviews". A five-star review that says "lovely night" adds almost nothing. A five-star review that says "the best wood-fired pizza we've had in Carlton — great for a date night" is a direct match for a question someone will ask an assistant tonight.
Practically:
- Write down the ten questions you want to win: "best pizza in Carlton", "where to take a date in Carlton", "good group dinner Carlton". Those phrases are your target vocabulary.
- When you ask a happy guest for a review, prompt the specifics: the dish they had, the occasion, and the suburb. "If you loved the porchetta, it really helps if you mention it."
- Ask consistently, not in bursts. A steady trickle beats one campaign a year, because recency counts.
- Reply to reviews using the same natural language — your replies are readable text too.
Step 2: Create more content, more often
AI models describe venues using whatever material exists. If there are twelve photos and six posts about you, the picture is thin. Post the menu changes, the specials, the room, the people, the events. Put the cuisine, the suburb and the occasion into the captions in plain words — not just hashtags.
Your own website matters here too. Put the menu on the page as real text. Write a paragraph that says what you are, where you are and who you are for. Add opening hours, address and cuisine in text, not baked into a graphic.
Step 3: Get more people talking about you online
Everything above is you talking about you. What moves the needle hardest is other people doing it. Guests posting and tagging, bloggers writing you up, local guides listing you, someone recommending you in a Reddit thread about the neighbourhood.
- Find the guides that already rank for your category and city, and pitch yourself into them — most accept submissions.
- Claim and complete every listing that matters locally: booking platforms, tourism sites, precinct and council directories.
- Make it easy and worth it for guests to post and tag — this is where a reward system does the heavy lifting, because it turns a one-off ask into an ongoing habit.
This is the same engine behind reviews, referrals and user-generated content — one system, three outputs. If you reward customers for the things that create public proof, your online footprint grows every week without you posting more.
Step 4: Measure whether it worked
You cannot check AI visibility in Google Search Console. The practical method is to ask the assistants the questions your customers ask, and record whether you appear and in what position — then repeat monthly. Track it per phrase, both for your suburb and city-wide, because the two behave very differently.
That is exactly what our free AI Visibility Checker automates: it runs the real questions through ChatGPT and Gemini, scores where you stand against the venues that do get recommended, audits your Google profile and website, and writes you a specific improvement plan.
How long does it take?
Website and listing fixes can show up within weeks. Review-driven change is slower — you are shifting the weight of what people say about you, which takes a few months of consistent asking. Guides and mentions land whenever they land, but each one compounds.
The venues winning AI recommendations are not the ones with the biggest ad budget. They are the ones with the most people saying specific, recent, positive things about them in public. Build the habit that produces that, and the recommendations follow.
Want the habit built for you? See how a reward system turns regular customers into a steady stream of reviews, posts and referrals — the exact signals AI assistants read.