If you’ve asked ChatGPT to recommend a plumber, then asked Gemini the same question, you’ve probably noticed something: you don’t get the same answers. That’s not a mistake. Each AI platform is running its own scoring system, built on its own data pipeline, weighting completely different signals. A business that dominates ChatGPT’s answers can be invisible in Perplexity’s, and vice versa. We asked seven major AI platforms the same direct question — what factors matter when you recommend a local service business? — and cross-referenced their answers against the empirical research on how they actually behave. The result is a side-by-side framework comparison showing where these platforms agree, where they differ, and what those differences mean for businesses trying to improve their visibility in 2026.
AI Search, GEO, and LLM SEO
Before we get into the data, a bit of housekeeping. Because you’ll see all of these terms used in the wild, sometimes interchangeably, sometimes not, and it can be confusing even for people who do this every day.
AI search is the broadest term. It just means someone getting an answer from an AI tool instead of a traditional list of blue links. That can be a user asking ChatGPT a question directly, or Googling something and having an AI Overview answer it before scrolling down to the actual results.
GEO (Generative Engine Optimization) is the practice of trying to influence AI search. It’s the discipline, not the search itself. Think of it as the AI-era counterpart to SEO: instead of optimizing to rank in a list of links, you’re optimizing to get cited, quoted, or recommended inside an AI-generated answer.
LLM SEO (sometimes called LLM optimization) is a narrower term you’ll see used for the same general idea, usually with more of a focus on the standalone chatbot platforms specifically, such as ChatGPT, Claude, Gemini, and Perplexity, rather than AI features embedded inside a traditional search engine, like Google AI Overviews.
You may also run into AEO (Answer Engine Optimization), which usually refers to structuring content so it directly and clearly answers a specific question. This is the kind of formatting that gets pulled into featured snippets, FAQ sections, and AI-generated answers alike.
In practice, these terms describe overlapping pieces of the same ecosystem, not competing strategies. For the rest of this post, we’ll use AI search as the umbrella term, since it’s the one that makes sense to the largest audience.
AI Search Isn’t One Channel; It’s At Least Seven
It’s tempting to think of AI search optimization as a single strategy. It isn’t.
Every major AI platform uses its own retrieval methods, ranking signals, citation habits, and local business preferences. The result is that success on one platform does not automatically translate to another.
The research tells that story from two different angles.
AI Recommendations Are Surprisingly Selective
SOCi’s 2026 Local Visibility Index analyzed roughly 350,000 business locations across 2,751 multi-location brands to measure how often specific locations appeared in AI-generated local recommendations.
| Platform | Share Recommended |
| ChatGPT | 1.2% |
| Perplexity | 7.4% |
| Gemini | 11% |
| Google Local 3-Pack | 35.9% |
Source: SOCi 2026 Local Visibility Index.
These figures don’t measure how often AI answers a question. Every platform produces an answer. Instead, they measure how often a particular business is actually named.
It’s also important to understand the scope of the study. SOCi examined large multi-location brands, and its research notes that AI systems often favor independent businesses with a single location because their reviews, hours, and business information tend to be more consistent. A local dental office or law firm is not necessarily competing under the same conditions as a national chain.
The takeaway is simple: AI recommendations are highly selective.
The Platforms Rarely Agree on Who They Recommend
Even when businesses are recommended, they usually aren’t the same businesses across AI platforms.
Research from Princeton found that only about 11% of cited domains were shared across ChatGPT, Gemini, and Perplexity. Search Atlas reached a similar conclusion after analyzing citations across more than 104,000 URLs.
In other words, visibility isn’t simply difficult; it’s fragmented.
A business that appears in ChatGPT’s answer may never appear in Perplexity’s version of the exact same search. Gemini may recommend someone else entirely.
That’s why treating AI search as one marketing channel creates blind spots. Optimizing for a single platform means competing in only one part of a much larger AI ecosystem.
Seven Models, Seven Scoring Systems
Below is what each platform told us matters most, weighted as a percentage, cross-checked against independent research on how it behaves in practice. No AI model publishes its actual ranking weights. This is a synthesis, not a leaked algorithm. Treat the percentages as directional, rather than exact.
Claude (Anthropic)
Anchored on specialization match and verified credentials. 8 variables.
Claude’s framework is anchored on two equally weighted signals — service/specialization match and review quality, both at 20% — with licensing/credentials and location close behind at 15% each. Review recency (12%) and years in business (8%) fill out the trust picture. Claude is also the only one of the seven models that scores pricing transparency as its own standalone factor, even if it’s a small slice at 5%.
ChatGPT (OpenAI)
Built around reputation breadth and multi-platform signals. 10 variables.
ChatGPT leads with review quality and sentiment at 20%, then location at 15%. What sets it apart is how much weight it puts on signals outside a business’s own website: multi-platform brand mentions (12%) and third-party directory presence (10%) are both higher here than in any other framework, reflecting how heavily ChatGPT leans on sources like Yelp, Foursquare, and BBB rather than a business’s own site.
Gemini (Google)
Tightly fused with Google’s Knowledge Graph and Business Profile data. 9 variables.
Gemini’s framework is built around Google’s own ecosystem: Google Business Profile quality leads at 22%, followed by review volume, rating, and content at 20%. Location (15%) and Knowledge Graph entity strength (12%) round out the top four. Outside of Google AI Overviews and Copilot, no other model weighs business profile completeness anywhere near this heavily.
Google AI Overviews
A 4-signal stack powered by Google’s organic index. 8 variables.
Google AI Overviews puts more weight on a single factor than any other model in this comparison: GBP completeness and quality at 25%. Review signals follow at 22%, then relevance and authority signals at 15%. Together, GBP quality and reviews account for nearly half of this framework’s total weight — a strong argument for making profile completeness the first priority, not an afterthought.
Perplexity
A live-web research engine that values freshness and source diversity over profile completeness. 8 variables.
Perplexity rewards relevance to the query above all else (22%), followed by source authority (18%) and content freshness — also 18%, the highest freshness weighting of any model here. Because Perplexity researches the live web on every query instead of relying on a cached index, a page updated last month can outrank a better-optimized page that’s gone stale.
Microsoft Copilot
Bing-powered, with the heaviest structured-data weighting of any model. 8 variables.
Copilot leads with Bing Places profile quality (20%) and website FAQ/answer content (18%) — the highest FAQ-content weighting of any model. It’s also the heaviest schema-markup weighter of the seven at 13%, making Copilot the platform where structured data and clearly formatted answer content do the most work.
Meta AI
Semantic understanding via dense retrieval — the only model that doesn’t score location as a standalone factor. 7 variables.
Meta AI is the most content-driven framework of the seven: semantic relevance leads at 25%, followed by topical authority and depth (20%) and source trust/E-E-A-T (18%). It’s also the only model in this comparison that doesn’t score location as a standalone ranking factor at all — its dense retrieval system prioritizes semantic match over geographic proximity.
Where the Weight Actually Sits: The Unified Comparison
What Do These Categories Actually Mean?
Some of the terms in that table are self-explanatory. Others are SEO shorthand that don’t mean much if you don’t live in this world every day. Here’s what each one actually refers to, in plain English.
- Review Quality & Sentiment — not just your star rating, but what your reviews actually say. A string of five-star reviews that all say “great job” tells an AI model a lot less than reviews that mention specific services, specific staff members, or specific outcomes.
- Service/Specialization Match — how clearly your content tells an AI model exactly what you do. A general contractor’s site that says “we do it all” matches fewer specific searches than one with a dedicated page for “kitchen remodeling” or “roof replacement.”
- Location/Proximity — how close you are to the person searching, and how clearly your content states where you actually operate. This one’s straightforward, but it’s worth noting: vague location language (“serving the Southeast”) is a lot weaker than a clear, specific service area.
- Business Profile Completeness — how fully filled out your Google Business Profile or Bing Places listing is: hours, categories, services, photos, attributes, the works. An empty or half-finished profile is invisible to the models that weight this heavily.
- Credentials & Trust Signals — the proof that you are who you say you are: licenses, certifications, industry memberships, awards, insurance, verified badges. For a contractor, that might be a state license number. For a law firm, it’s bar admission and standing. For a medical practice, board certification.
- Content Quality & Depth — whether your website actually demonstrates expertise, or just states that you have it. A page that walks through how a process works, written by someone who clearly knows the subject, outperforms a thin page that just lists services.
- Multi-Platform Presence — whether your business shows up consistently across the sources AI models pull from beyond your own website: Yelp, Facebook, industry-specific directories, local news mentions, and so on.
- Content Freshness & Recency — how recently your site (and your reviews) have been updated. A page that hasn’t changed since 2022 signals staleness to models like Perplexity that search the live web on every query.
- Schema/Structured Data — code added behind the scenes on your website that labels information for machines — this is your business name, hours, and services; this is a frequently asked question and its answer. It doesn’t change what a visitor sees, but it makes your content easier for AI models to read accurately.
- Data Accuracy & NAP Consistency — NAP stands for Name, Address, Phone. This is simply whether that information matches, exactly, everywhere it appears online. A business listed as “123 Main St.” on its website and “123 Main Street, Suite A” on Yelp creates the kind of inconsistency that erodes trust signals.
- Business Stability/Years — track record. How long you’ve been operating, and whether that history is easy to verify.
- Engagement & Responsiveness — how actively you respond to reviews, answer questions, and stay active on your listings, rather than setting them up once and never touching them again.
- Pricing Transparency — how clearly you communicate what things cost, even in a general range. Vague or hidden pricing is a small but real detractor in at least one model’s framework (Claude’s).
Where This Fits Into a Comprehensive SEO Strategy
None of the twelve categories above exists in a bubble, and that’s really the point. Look back through that list, and you’ll notice most of it isn’t new. It’s the same fundamentals that have always mattered for traditional Google rankings, just being read by a different kind of engine. Content depth, credentials, structured data, review management, and consistent business information are SEO fundamentals first, and AI ranking factors second. A business with a strong existing SEO foundation isn’t starting from zero here; it’s already doing most of the work that AI models are scoring.
Where this extends beyond content is the website itself. AI optimization isn’t just about what you publish; it’s also about the environment you’re publishing it on. Fast load times, intuitive navigation, clean site architecture, and a well-maintained website help users find information more easily while making it simpler for search engines and AI models to interpret your content. The same investments that improve traditional SEO also make your business more competitive in AI search, because both depend on a website that’s organized, trustworthy, and built for a good user experience.
Eight Things This Comparison Reveals
The category definitions above tell you what each factor is. This is about what happens when you put all seven frameworks side by side, where the platforms quietly disagree with each other, and what that disagreement actually means for where you spend your time.
1. Reviews are one of the highest-value things you can invest in
Reviews matter, not just in quantity, but in quality. Having a real process in place to consistently earn new, high-value reviews helps with AI search visibility, and it does just as much work as a trust factor for the human consumers weighing you against competitors. It may not be a standalone factor for every AI platform — Perplexity and Meta AI don’t weight it the same way the other five do — but where it counts, it carries substantial weight, and it’s rarely wasted effort even on the platforms that score it lightly.
2. If you can only fix one thing, this is probably it
No other factor carries as much single-line weight in any framework as Business Profile completeness does for Gemini and Google AI Overviews — 22% and 25%, respectively, the two highest individual weights anywhere in this comparison. Unlike a lot of what’s on this list, it’s genuinely within reach: filling in every service category, adding real photos, keeping hours accurate, and posting regularly are all metrics that help. It’s also free.
3. “Set it and forget it” content quietly loses ground on Perplexity
A page you haven’t touched since 2022 can still perform fine on most platforms. On Perplexity, it’s actively losing to a competitor’s page that was updated last month. Content freshness carries more weight there than on any other model in this comparison, because Perplexity is searching the live web on every single query rather than relying on a cached index. In practice, that means revisiting old service pages and blog posts periodically isn’t just good housekeeping. Updating a statistic, refreshing an example, or adding a new section signals to Perplexity that a website is actively maintained, not just still in business. It’s also one of the lower-effort items on this list, since it’s editing existing content rather than creating something new.
4. Price transparency doesn’t mean publishing an exact number
Claude is the only model that scores pricing transparency, and even there it’s a modest 5% — not something to build a strategy around by itself. The underlying idea still matters well beyond that one platform. Plenty of industries can’t quote an exact price up front; a contractor isn’t going to advertise that every bathroom remodel costs $10,000 flat. What helps is giving people a general range or a starting point, so they have some idea what to expect before they call. That’s less about chasing a ranking factor and more about being the business that answered the question everyone else made them ask.
5. Schema markup is worth doing, but don’t oversell it internally
If schema is pitched as a universal AI-ranking fix, that’s overstating it. SearchAtlas’s citation study found it shows little to no correlation with ranking for most of the models in this comparison. Where it clearly pays off is Copilot (13%) and Google AI Overviews (10%), both of which lean on structured data to pull FAQ content and service details directly into their answers. Practically, that means adding LocalBusiness and FAQ schema to a site is still worth the technical investment. It’s a one-time setup that keeps paying off, but it shouldn’t be positioned as the centerpiece of an AI visibility strategy. Content quality and profile completeness are doing far more work across the other five platforms.
6. A great website isn’t enough to win in ChatGPT
ChatGPT leans harder on outside validation than any other model. Multi-platform brand mentions carry 12% of its weight, the highest of any factor in that framework, and research shows nearly half of its citations come from third-party sites like Yelp, TripAdvisor, and BBB rather than a business’s own website. A business with a strong, well-built site but no presence on the directories ChatGPT actually pulls from is starting from behind here, no matter how good the website is. Claiming and filling out those third-party profiles, not just building a better homepage, is the more direct path to showing up in ChatGPT’s answers specifically.
7. Location still matters most of the time; it’s just not universal
Five of the seven platforms weigh proximity as a real factor, often in the 12–15% range. So specifying and reinforcing your service area is still one of the more reliable things to get right. The exceptions are Perplexity and Meta AI, which will recommend a business farther away if its content and sourcing are stronger. The takeaway isn’t that location doesn’t matter; it clearly does, for the majority of platforms. It’s that being closest isn’t a substitute for the other factors on every single one of them.
8. There’s no such thing as a universal AI SEO checklist
The research points to one conclusion: AI search is not a single ecosystem. Citation patterns, ranking signals, and business recommendations vary from platform to platform, which means there is no universal checklist guaranteed to improve visibility everywhere. Instead of chasing platform-specific tactics, businesses should focus on the fundamentals that appear across multiple AI models, then layer in optimizations for the platforms that matter most to their customers.
Same Framework, Different Weights By Industry
The ranking factors in this guide represent the broad patterns that appear across AI search platforms, but they are not applied equally to every industry. While the core framework remains largely consistent, the importance of individual signals shifts depending on the type of business being evaluated.
A law firm, a dental practice, a roofing company, and a restaurant all build authority in different ways. The directories AI platforms reference, the credentials they emphasize, the types of reviews they value, and the content they prioritize can all vary from one vertical to another. Those differences don’t replace the fundamentals—they simply change which factors carry the most weight.
Turn AI Visibility Into Business Growth
From technical SEO and Google Business Profile optimization to content and authority building, we help businesses compete where AI search is headed next.
A Note on Methodology
Each framework above was built from two sources layered together: direct queries to each AI model asking how it evaluates local businesses, and empirical research on how each model actually behaves — citation studies, local visibility indexes, and platform-specific analyses. No AI model publishes its official ranking weights, so the percentages here are a synthesis, not a leaked formula. They’re directionally reliable, not exact, and they’ll shift as every one of these models continues to update.
Primary sources: SearchAtlas (104,855-citation study across OpenAI, Gemini, Perplexity, Grok, Copilot, and Google AI Mode), SOCi 2026 Local Visibility Index, Yext AI Citation Study, Princeton GEO Research (KDD 2024), Cited.com’s 12-market study, and platform-specific usage research from Similarweb, StatCounter, BrightEdge, and Semrush.
