The rise of AI-driven search engines is reshaping how customers discover local businesses. Platforms like Perplexity AI, ChatGPT, and Gemini no longer simply return lists of links—they provide synthesized, context-aware answers.
For SaaS SEO providers managing multi-location brands, this shift introduces both challenges and opportunities. These engines interpret local data differently than traditional search engines, prioritizing structured, contextual, and verified information to determine which businesses to recommend.
Understanding how these platforms process local data is critical for building SEO strategies that keep multi-location clients visible in an AI-first discovery landscape.
In This Article:
Why Local Data Is Different in AI Search
Traditional search engines relied heavily on keywords and backlinks, structured NAP (name, address, phone number) consistency and map pack signals.
AI search engines add new layers such:
- Entity understanding: they recognize a business as part of a knowledge graph.
- Contextual reasoning: factor in query context like “near my office” or “open now.”
- Multi-source verification: check data across multiple listings, reviews, and platforms for accuracy.
This means businesses aren’t ranked in a list. They’re basically either recommended or ignored. For multi-location brands, being discoverable in this model requires proactive syndication of rich, AI-readable data.
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How Major AI Search Engines Handle Local Data
1. Perplexity AI
Perplexity emphasizes real-time and sourced answers. For local discovery, it pulls data from:
- Map providers like Google, Bing and OpenStreetMap.
- Directories and aggregators such as Yelp and TripAdvisor.
- Verified brand sources.
Perplexity often surfaces citations and links, meaning data accuracy and source diversity are critical. If a business is missing or inconsistent in a key directory, it definitely risks being excluded.
2. ChatGPT (with Browse + Plugins/Actions)
ChatGPT integrates with external APIs and live search connectors. So, the local data processing involves:
- Structured listings across directories like Google Business Profiles, Apple Maps, Yelp and many others.
- API feeds (reservation systems, delivery apps, inventory APIs)
- Conversational interpretation of intent
For example, a query like “Where can I get sushi open late near Union Square?” is parsed into: cuisine, time constraint, location context. If the business listing lacks those structured signals, it won’t appear.
3. Gemini (Google’s AI)
Gemini benefits from Google’s deep integration with Maps, Search, and Knowledge Graph. This gives it unmatched access to three key data points:
- Verified Google Business Profiles
- Reviews and user-generated content
- Schema markup on websites
Gemini prioritizes entities with strong structured data and consistent attributes across Google’s ecosystem. Meaning that for multi-location brands or even SMBs, gaps in Google Business Profile data directly impact AI-driven visibility.
Key Signals AI Engines Use for Local Data
Across Perplexity, ChatGPT, and Gemini, four themes emerge:
- Structured Data First: Listings with schema markup, product/service data, and attributes are favored because they’re machine-readable.
- Consistency Across Sources: If NAP data or hours differ across directories, engines down-rank or exclude the business.
- Contextual Fit: AI models prioritize businesses that fit query nuance: “open now,” “with WiFi,” “family-friendly,” etc.
- Review Semantics: LLMs parse review language. If customers consistently mention “fast delivery,” the business is more likely to surface in related queries.
Challenges for SaaS SEO Providers
Multi-location brands face unique hurdles in this environment:
- Scale of Updates: Hundreds of locations must maintain consistent, enriched data across dozens of directories.
- Fragmented Ecosystems: AI engines pull from multiple sources, not just Google.
- Opaque Ranking: AI models don’t disclose weighting of signals, requiring providers to infer patterns through testing.
- Rapid Change: AI platforms evolve faster than traditional search engines, demanding continuous monitoring.
How Providers Can Optimize for AI Search Engines
1. Syndicate Data Broadly
Ensure coverage across Google, Apple Maps, Bing, Yelp, TripAdvisor, and AI-visible directories. Platforms like Ezoma simplify syndication at scale.
2. Prioritize Rich Attributes
Go beyond name and hours. Include amenities, accessibility info, seasonal promotions, and service options.
3. Monitor Emerging Engines
Test how your brand surfaces in Perplexity, ChatGPT, and Gemini. Identify where competitors show up and why. For brands looking to monitor their presence across AI platforms, tools like Scrunch help track brand mentions and identify content gaps in AI-driven search results, providing a centralized knowledge hub to ensure accurate brand data reaches these emerging engines.
4. Optimize Reviews
Encourage reviews that naturally mention services, products, and attributes AI engines use to infer relevance.
5. Implement Schema Everywhere
Product schema, service schema, and location schema should be standard across websites and landing pages.
The Role of Ezoma
Ezoma enables multi-location brands to stay competitive in AI-first search by re-formatting data into AI-ingestable ways. Meaning it will grab the data provided, structure it and publish it in EZOMA, to be read in one source, but fed from data across directories.
Ezoma is both a distribution engine and a competitive edge that helps clients remain discoverable as AI search evolves.
AI search engines like Perplexity, ChatGPT, and Gemini are redefining local SEO. Visibility is now about being the trusted entity these platforms choose to recommend.
And the path forward is clear:
- Standardize and enrich local data.
- Syndicate broadly across ecosystems.
- Continuously test visibility in AI-driven engines.
Those who adapt now will secure discoverability for their clients as AI transforms the way customers search for local businesses.
Future-proof your multi-location brand’s visibility in AI search engines with Ezoma
