How ChatGPT Recommends Local Businesses (And How to Get Chosen)

To understand how to get recommended, you need to understand what happens inside these AI systems when someone asks a local business question. The process is very different from how Google’s traditional search works.

The Entity Model

AI systems don’t think in terms of websites. They think in terms of entities — distinct, recognized things in the world. Your business is an entity. So is your competitor. So is the category you operate in, the city you serve, and the services you offer. The AI’s job is to connect user queries to the most relevant entities.

When someone asks ChatGPT "Who’s the best family dentist in Scottsdale?", the AI is performing a complex entity-matching operation. It’s looking for entities that match: (1) the category "family dentist," (2) the location "Scottsdale," and (3) the qualifier "best," which triggers an authority evaluation.

The Recommendation Pipeline

Here’s a simplified version of what happens:

The critical insight: if the AI can’t find you as a clear entity, you can’t be recommended. It doesn’t matter how good your services are or how long you’ve been in business. If your digital entity profile is weak, incomplete, or contradictory, the AI will choose a competitor with cleaner data. For a broader understanding of this landscape, see our complete guide to Answer Engine Optimization.

One of the biggest misconceptions in AEO is that optimizing your website is enough. AI tools don’t just read your website — they read everything about you from everywhere. Here are the primary data sources and what they contribute:

Your Website

This is your most controllable data source. AI systems extract your service descriptions, location information, credentials, team details, and any structured data (schema markup) you provide. Schema markup is particularly important because it gives the AI explicit, unambiguous information rather than requiring it to interpret natural language.

Google Business Profile

Your GBP is one of the most authoritative entity sources for local business queries. It provides verified name, address, phone, hours, category, attributes, photos, and reviews. An incomplete or outdated GBP is one of the most common reasons businesses fail to get AI recommendations.

Review Platforms (Yelp, Google Reviews, Industry-Specific Sites)

Reviews provide some of the strongest signals AI systems use. Not just star ratings — the actual text of reviews contains entity-relevant keywords, service descriptions, and sentiment data that AI systems analyze. A review saying "Best emergency plumber in town, showed up in 30 minutes at midnight" gives the AI specific signals about service type, responsiveness, and availability.

Industry Directories and Citations

Platforms like Angi, Avvo, Healthgrades, Houzz, and industry-specific directories contribute to your entity profile. Each consistent listing reinforces who you are, what you do, and where you operate. Inconsistencies — different phone numbers, outdated addresses, old business names — degrade your entity strength.

Social Media

Facebook, Instagram, LinkedIn, and other platforms provide recency signals, engagement data, and additional entity context. Active social profiles with consistent branding signal a living, active business.

News and Press Mentions

Mentions in local news, press releases, community publications, and industry blogs serve as third-party authority signals. AI systems give extra weight to information that appears in editorially curated sources.

While the exact algorithms behind ChatGPT and Perplexity aren’t public, extensive testing and analysis reveal clear patterns in what drives AI recommendations. Here are the signals that matter most:

1. Entity Clarity

How clearly and unambiguously is your business defined as a distinct entity? Businesses with unique names, clear service categories, and well-defined service areas have stronger entity signals. If your business name is generic (e.g., "Premier Services"), you need stronger supporting signals to differentiate your entity.

2. Cross-Platform Consistency

How uniform is your business information across all data sources? Name, address, phone number, business hours, service descriptions, and categories should be identical everywhere. This is the foundation of entity trust.

3. Review Signals

AI systems analyze reviews on multiple dimensions:

4. Structured Data Quality

Schema markup gives AI systems explicit, machine-readable information about your business. Businesses with comprehensive, error-free schema markup have a measurable advantage over those without. This includes LocalBusiness, Service, FAQPage, and Review schema types.

5. Content Authority and Depth

Does your website demonstrate genuine expertise in your field? AI systems evaluate the depth, specificity, and authoritativeness of your content. Generic, thin service pages lose to detailed, expert-level content that answers specific customer questions.

6. Geographic Relevance

For local queries, the AI must confirm your business serves the requested location. Clear service area definitions in your schema markup, GBP, and website content ensure the AI includes you in geographically relevant queries.

7. Recency and Freshness

AI systems favor businesses with recently updated information. A website last updated in 2021, a GBP with no recent posts, and stale review activity all signal a potentially inactive or outdated business.

Here’s the step-by-step playbook we use at First Answer to get local businesses into AI recommendations:

Step 1: Run an AI Visibility Audit

Before optimizing anything, establish your baseline. Ask ChatGPT, Perplexity, and Google AI Overviews the top 10-15 questions your customers ask. Document which businesses get recommended, what the AI says about your business (if anything), and where the gaps are. This reveals your exact starting position.

Step 2: Clean Your Data Everywhere

Audit every platform where your business appears. Your Google Business Profile, Yelp listing, website, social media profiles, and all industry directories must have identical, current, and complete information. This is tedious but non-negotiable. Even one inconsistency can weaken your entity signal.

Step 3: Implement Comprehensive Schema Markup

Add JSON-LD structured data to your website. Go beyond basic LocalBusiness schema. Include:

Step 4: Restructure Your Content

Rewrite your service pages to lead with the customer’s question and immediately provide a clear, direct answer. Add comprehensive FAQ sections to every key page. Create content that addresses the specific, natural-language queries people ask AI tools. The format matters as much as the substance — AI systems are better at extracting answers from well-structured content.

Step 5: Accelerate and Optimize Reviews

Implement a systematic review generation process. Encourage customers to mention specific services, outcomes, and experiences in their reviews. Respond to every review — positive and negative — to signal active engagement. Focus on recency: a steady flow of new reviews is more valuable than a large but stale collection.

Step 6: Build Authority Signals

Seek mentions in local publications, industry blogs, and community resources. Contribute expert content to relevant platforms. Build a presence on industry-specific directories. Each authoritative mention strengthens your entity profile in AI knowledge systems.

Step 7: Monitor and Iterate

AI recommendations change. Query the AI tools weekly for your target queries. Track which businesses appear, how your mentions change, and what new competitors emerge. Adjust your strategy based on real data, not assumptions. To learn how AEO fits within your broader search strategy, read our guide on AEO vs SEO for local businesses.

We’ve audited hundreds of local businesses for AI visibility. These are the mistakes we see over and over again:

Mistake 1: Assuming Good SEO = Good AEO

This is the most dangerous assumption. We regularly see businesses with strong Google rankings that are completely invisible to AI recommendation engines. SEO and AEO have overlapping but distinct requirements. A top-ranking website with no schema markup and inconsistent directory listings will underperform in AI search.

Mistake 2: Incomplete Schema Markup

Many businesses implement only the most basic schema — often auto-generated by their website platform — and assume it’s sufficient. Basic schema is better than nothing, but comprehensive schema covering your services, FAQs, reviews, and organization is what sets recommended businesses apart from invisible ones.

Mistake 3: Neglecting Data Consistency

Your business name is listed differently on Yelp than on your website. Your phone number on your Facebook page is your old number. Your GBP hours don’t match your website. Every one of these inconsistencies makes the AI less confident about recommending you. AI systems would rather recommend no one than recommend uncertain information.

Mistake 4: Generic, Thin Content

Pages that say "We offer quality roofing services at competitive prices" give the AI nothing to work with. AI systems need specific, detailed content: what types of roofing, what areas you serve, what materials you use, what your process looks like, what your qualifications are. Specificity wins over generality every time.

Mistake 5: Ignoring Reviews

Some businesses view reviews as a passive byproduct of doing business. In AEO, reviews are an active optimization channel. The volume, recency, sentiment, and keyword content of your reviews directly influence whether AI tools recommend you. A business with 50 recent, detailed reviews will consistently outperform a competitor with 200 reviews from three years ago.

Mistake 6: Set-It-and-Forget-It Mentality

AEO is not a one-time project. AI systems are constantly re-evaluating entities based on new data. Your competitors are optimizing. The AI platforms themselves are evolving. Businesses that treat AEO as an ongoing discipline will maintain and grow their AI visibility. Those that optimize once and walk away will fade.