Every time I run a new client audit, I run the same set of prompts across all four major AI engines before we start any work. Same query, same phrasing, same device and browser profile with no history, and more than one run per engine, because these answers move. Then I write down which businesses are named, what source each name is cited from, and how the response is put together.
The spread is almost always striking. The same prompt — "best optometrist in Crown Heights" or "private BJJ lessons Park Slope" — returns materially different answers on different engines, names different businesses, and draws on different underlying data sources. Understanding that spread is the entire job of AI search optimization. You can't optimize for one engine and expect the results to carry over. The four systems are making fundamentally different decisions.
How I Run It
Every prompt is a Brooklyn local business query, written as a natural-language question rather than a keyword string — the way a customer actually types it. I spread them across categories, because AI local search behaves differently in a category with heavy consumer review coverage (restaurants, fitness) than in one without it (optometry, home services), and a business in a thin category needs a different plan than one in a crowded one.
Repeat runs matter more than people expect. Ask the same engine the same question twice and you can get two different lists. Where the answer is unstable, that instability is the finding: it means the engine does not confidently know who the right businesses are in that category, which is exactly the situation a well-structured business can walk into and win.
Four surfaces, always logged out: ChatGPT with search enabled, Perplexity in its default live-web mode, Google AI Overviews triggered in a clean Chrome profile, and Gemini. Model versions change every few months; the retrieval behaviour described below has been steadier than the version numbers.
Why Each Engine Cites Differently
Before going into the results, the structural reason for the spread: each engine is retrieving information from a different underlying data layer, through a different retrieval mechanism, with different confidence thresholds for naming a specific local business.
ChatGPT: Training data plus Bing
When ChatGPT search is enabled, it's running a Bing web search and synthesizing results with its training data. This means its local answers have two layers: what the model learned during training (which has a knowledge cutoff and reflects whatever sources made it into the training corpus) and what Bing returns for the live query. ChatGPT tends to be more confident naming businesses with strong Bing-indexed web presences and significant review volume on platforms Bing indexes well. It also shows a slight bias toward businesses that appear in Yelp results, since Yelp content is prominent in Bing's local results.
Perplexity: Live web retrieval
Perplexity runs a live web search for every query, then synthesizes cited sources into a response. It shows its sources explicitly, usually several per answer. This is the most transparent engine to analyze because you can see exactly which pages it's drawing from. Perplexity tends to favor newer, clearly structured pages over older authoritative ones, because its retrieval model is asking "which page best answers this specific question right now?" rather than "which page has the most authority overall?" It cites Google Business Profile pages, individual business websites, and local directories, in roughly that order of frequency.
Google AI Overviews: The search index
Google AI Overviews runs directly on Google's search index, which means it reflects Google's own ranking signals — PageRank, E-E-A-T, structured data, local pack prominence. Businesses that rank in the local 3-pack or have strong organic rankings tend to appear in AI Overviews for the same queries. This makes Google AI Overviews the most path-dependent of the four: your existing Google SEO work carries over more directly here than anywhere else. It also means older, more established businesses tend to perform better in AI Overviews than in Perplexity or ChatGPT, simply because their legacy signals are baked into the index.
Gemini: Google Knowledge Graph
Gemini's local search answers lean heavily on the Google Knowledge Graph and Google Business Profile data. It tends to be more conservative than the other three — more likely to give a generic answer ("here are some well-reviewed optometrists in Crown Heights") with a list format populated from GBP data than to give a confident single recommendation. When Gemini does name a specific business confidently, that business typically has a well-completed GBP profile with strong review signals. Gemini is the engine most sensitive to GBP health, in my experience.
Where the Engines Split
The split shows up most clearly on a question with intent baked into it — "who is the best optometrist in Crown Heights who takes my insurance", or "where can I take my first BJJ class in Park Slope as a complete beginner".
Perplexity and ChatGPT tend to read the qualifier. Asked for a beginner's first class, they can match that to a business whose site actually answers the beginner question, even a small one. Google AI Overviews and Gemini tend to fall back on the businesses with the strongest location signals — a fixed address, a long review history, backlinks — which is to say the businesses Google already ranks.
Perplexity is answering the question. Google is ranking the businesses it already knows best for the category.
That difference decides who a small business is visible to. Brooklyn BJJ Lessons is private instruction with no storefront, which is a structural disadvantage on any surface that leans on the local pack — and it was cited first in ChatGPT for "BJJ private lessons Brooklyn" 41 days after launch, because the site answered the question being asked. Nostrand Optical is the same story from the other direction: an independent practice in a category with thin local coverage, made citable by structure rather than by review volume.
A business can be first on one engine and absent from another on the same day, for the same query. Optimising for Perplexity and ChatGPT pulls different levers than optimising for Google AI Overviews. Being found in all four means working all the layers at once.
What the Businesses in All Four Engines Have in Common
Only a small number of businesses turn up in all four engines for the same query. The ones that do tend to share the same combination of signals:
- A Google Business Profile filled in completely, with a genuine review history behind it
- A website with clear entity prose, structured data markup, and service-specific pages
- Directory listings that agree with each other on name, address and phone
- Some form of external citation — press mentions, blog features, or directory reviews beyond Yelp and Google
Businesses that appeared in only one or two engines typically had a strong signal in that engine's preferred data source and weak signals elsewhere. A business with an excellent Yelp profile but a minimal website showed up in ChatGPT (Bing/Yelp-indexed) but not Perplexity. A business with good Google organic rankings showed up in Google AI Overviews but not Perplexity. Fragmented presence equals fragmented AI visibility.
What This Means for Optimization Strategy
The spread across engines means a single-track optimization strategy won't get you to omnipresence. Here's how I think about the four engines in terms of what to prioritize:
For Perplexity visibility
Invest in structured, declarative content on your own site. Perplexity's live web retrieval is the most responsive to content quality and freshness. FAQ sections, service pages with specific entity-declarative prose, and Schema markup on every relevant page will move your Perplexity ranking faster than anything else.
For ChatGPT visibility
Yelp, Bing, and Healthgrades (for health businesses) are your leverage points. ChatGPT's search integration leans on Bing's index, and Bing surfaces Yelp content prominently. A complete, review-rich Yelp profile with recent activity is the fastest path to ChatGPT citation for most local Brooklyn businesses.
For Google AI Overviews
Your existing Google SEO is the foundation. Local pack presence, E-E-A-T signals, and strong review velocity on GBP all carry directly into AI Overviews. There's less lift you can get from content optimization here that isn't already captured by traditional local SEO work.
For Gemini
GBP completeness is the most direct lever. Every field in your Google Business Profile — categories, services, Q&A, photos, hours, attributes — feeds directly into Gemini's local answer quality. A stripped-down GBP profile is a Gemini visibility problem.
This four-engine test is the first thing I do for any new client. It takes a couple of hours to run properly, write down and read. If you want to see where your business stands across all four engines — and where your competitors are filling the gaps you're leaving open — that's exactly what the free 20-minute audit covers, in live screen-share format.
The bottom line from running this before every build: you should assume you are invisible in at least one of the four engines right now, regardless of how strong your Google presence is. The engines are drawing on different data, asking different questions, and reaching different conclusions about who the right answer is for any given local query. That's not a problem you can solve with one fix. It's a problem you solve by building presence across all the layers those engines draw from.