Local SEO & AI Search API: How to Track Local Brand Visibility in ChatGPT & Perplexity

Local SEO is being reshaped by AI search. When a potential customer asks ChatGPT "best pizza near me in Brooklyn" or Perplexity "top-rated plumber in Denver," these AI engines return specific business recommendations - often with reasoning about why each business stands out. For local businesses, this is a new discovery channel that most competitors aren't tracking yet.

This guide shows you how to use the Sellm API to monitor your local business visibility across AI search engines, compare how different providers handle local queries, and track the signals that influence AI recommendations.

The Local AI Search Landscape

AI search engines handle local queries differently from traditional search, and each platform has its own approach:

ChatGPT Local Recommendations

ChatGPT now provides specific local business recommendations when users include location context. Ask "best Italian restaurant in San Francisco" and you'll get named businesses with descriptions of their cuisine, ambiance, and price range. ChatGPT pulls from a mix of web sources, review aggregators, and its training data to generate these recommendations. For local businesses, being mentioned in a ChatGPT response carries significant trust because users treat the output as curated advice rather than a list of ads.

Perplexity Local Results

Perplexity takes a source-first approach to local queries. When someone asks about local businesses, Perplexity cites specific review sites, directories, and articles alongside its recommendations. This means your presence on Yelp, Google Business Profile, TripAdvisor, and local media outlets directly influences whether Perplexity mentions your business. The citation trail also makes it possible to trace exactly which sources are driving your AI visibility.

Google AI Overviews for Local

Google's AI Overviews are appearing for an increasing number of local queries. These AI-generated summaries sit above traditional search results and can include business recommendations drawn from Google's own data (Maps, Business Profiles, Reviews) as well as third-party sources. For businesses that already invest in Google Business Profile optimization, AI Overviews represent both an opportunity and a risk - your existing local SEO work may or may not translate into AI visibility.

Why Traditional Local SEO Tools Aren't Enough

If you're using tools like BrightLocal, Whitespark, or Moz Local, you're tracking important signals - but only for traditional search. Here's what they miss:

• No AI response tracking. Traditional tools monitor Google Maps pack rankings, local finder positions, and directory listings. They cannot tell you whether ChatGPT, Perplexity, or Claude mention your business when a user asks a local-intent question.
• Different ranking factors. Google Maps rankings depend heavily on proximity, Google Business Profile completeness, and review volume. AI engines weigh factors differently - editorial mentions, content authority, and cross-platform consistency matter more.
• No cross-provider comparison. Your business might rank #1 in ChatGPT for "best coffee shop in Portland" but not appear at all in Perplexity's answer to the same question. Traditional tools can't surface this kind of gap.
• No sentiment analysis. Even when AI engines mention your business, the framing matters. Being mentioned as "a decent option" versus "the top choice locals swear by" has very different conversion implications. AI visibility tracking captures this nuance.

How to Use the Sellm API for Local Brand Monitoring

The Sellm API lets you submit prompts to multiple AI providers and get back structured analysis of whether (and how) your brand is mentioned. For local businesses, three capabilities are especially relevant:

Geo-Targeted Queries with the Country Parameter

When creating prompts through the API, you can specify a country parameter that influences how AI providers handle location context. This is important because AI responses to "best restaurant" can differ depending on the user's apparent location. Configure your project's geographic targeting via the settings endpoint:

curl -X PUT https://sellm.io/api/v1/settings/schedule \
  -H "Authorization: Bearer sellm_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "country": "US"
  }'

Local-Intent Prompts

The key to accurate local AI visibility tracking is crafting prompts that match how real users ask about local businesses. Include city names, neighborhoods, and local qualifiers:

• "best dentist in Austin Texas"
• "top restaurants near downtown Seattle"
• "recommended auto repair shops in Chicago"
• "best family lawyer in Miami"
• "where to get the best tacos in Los Angeles"

Multi-Provider Comparison

Different AI engines may recommend entirely different businesses for the same local query. Tracking across ChatGPT, Perplexity, and other providers reveals where your visibility gaps are:

curl -X POST https://sellm.io/api/v1/async-analysis \
  -H "Authorization: Bearer sellm_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "best dentist in Austin TX",
    "providers": ["chatgpt", "claude", "perplexity", "gemini", "grok"],
    "country": "US",
    "replicates": 3
  }'

Step-by-Step: Local AI Visibility Tracking with Python

Here's a complete workflow for tracking local business visibility across AI providers using the Sellm API.

1. Set Up Local-Intent Prompts

Start by creating prompts that reflect how customers search for your type of business in your area:

import requests
import os

API_KEY = os.environ["SELLM_API_KEY"]
BASE_URL = "https://sellm.io/api/v1"
HEADERS = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json",
}

# Define local-intent prompts for a dentist in Austin
local_prompts = [
    "best dentist in Austin Texas",
    "top-rated dental clinic in Austin",
    "who is the best cosmetic dentist in Austin TX",
    "affordable dentist near downtown Austin",
    "best pediatric dentist in Austin",
    "dentist recommendations in Austin Texas",
    "emergency dentist in Austin open now",
    "best dentist for implants in Austin",
]

# Submit each prompt for async analysis across all providers
providers = ["chatgpt", "claude", "perplexity", "gemini", "grok"]
analysis_ids = []

for prompt_text in local_prompts:
    resp = requests.post(
        f"{BASE_URL}/async-analysis",
        headers=HEADERS,
        json={
            "prompt": prompt_text,
            "providers": providers,
            "country": "US",
            "replicates": 3,
        },
    )
    if resp.status_code == 200:
        analysis_id = resp.json()["analysisId"]
        analysis_ids.append(analysis_id)
        print(f"Submitted: {prompt_text} -> {analysis_id}")
    else:
        print(f"Error submitting '{prompt_text}': {resp.status_code}")

2. Run Analysis and Compare Providers

Trigger an analysis run and then compare how each AI engine handles your local queries:

import time

def poll_analysis(analysis_id):
    """Poll an async analysis until it completes."""
    while True:
        resp = requests.get(
            f"{BASE_URL}/async-analysis/{analysis_id}",
            headers=HEADERS,
        )
        resp.raise_for_status()
        data = resp.json()
        status = data["status"]
        if status == "succeeded":
            print(f"Analysis {analysis_id} completed")
            return data
        if status == "failed":
            raise RuntimeError(f"Analysis {analysis_id} failed")
        print(f"  Status: {status}, waiting...")
        time.sleep(15)


def compare_providers(result):
    """Compare local visibility across AI providers."""
    breakdown = result["providerBreakdown"]
    sov_data = breakdown.get("sovByProvider", [])

    print("\n=== Local Visibility by AI Provider ===")
    print(f"{'Provider':<14} {'SOV%':>6} {'Coverage%':>10} {'Avg Pos':>8} {'Sentiment':>10}")
    print("-" * 52)
    for p in sorted(sov_data, key=lambda x: x.get("sovPct", 0), reverse=True):
        print(
            f"{p['provider']:<14} {p.get('sovPct', 0):>5.1f}% "
            f"{p.get('coverage', 0):>9.1f}% {p.get('avgPos', 0):>7.1f} "
            f"{p.get('sentiment', 0):>9.1f}"
        )
    return breakdown


# Poll each analysis and compare providers
for aid in analysis_ids:
    result = poll_analysis(aid)
    compare_providers(result)

3. Track Local Competitors

The prompt-level breakdown reveals which competitors are mentioned alongside your business for each local query:

def analyze_local_competitors(result):
    """See which competitors appear in local AI results."""
    prompt_rows = result.get("promptBreakdown", [])

    print("\n=== Local Competitor Analysis ===")
    for p in prompt_rows:
        query = p["prompt"][:60]
        competitors = p.get("topCompetitors", [])
        your_position = p.get("avgPos", "N/A")
        mentioned = p.get("coverage", 0) > 0

        status = f"Position: {your_position}" if mentioned else "NOT MENTIONED"
        print(f"\n  Query: "{query}"")
        print(f"  Status: {status}")
        if competitors:
            print(f"  Competitors ahead: {', '.join(competitors[:5])}")


# Analyze competitors for a completed analysis
result = poll_analysis(analysis_ids[0])
analyze_local_competitors(result)

Example output:

=== Local Competitor Analysis ===

  Query: "best dentist in Austin Texas"
  Status: Position: 2.0
  Competitors ahead: Westlake Dental, Austin Dental Works

  Query: "top-rated dental clinic in Austin"
  Status: Position: 1.0

  Query: "affordable dentist near downtown Austin"
  Status: NOT MENTIONED
  Competitors ahead: Castle Dental, Bright Smiles Austin

Key Metrics for Local AI Visibility

When monitoring local business visibility in AI search, focus on these metrics:

Local Citation Signals That Influence AI Recommendations

AI engines pull information from a broad range of sources to generate local recommendations. While the exact algorithms are opaque, consistent patterns emerge in which signals correlate with stronger AI visibility:

NAP Consistency

Your business Name, Address, and Phone number should be identical across every online listing. AI models synthesize information from dozens of sources, and conflicting NAP data creates uncertainty. If your address is "123 Main St" on Yelp but "123 Main Street, Suite A" on your website, AI engines may treat these as ambiguous or even as separate businesses. Audit your NAP consistency across Google Business Profile, Yelp, Bing Places, Apple Maps, and industry-specific directories.

Review Volume and Quality

AI engines heavily weight review content when generating local recommendations. This goes beyond star ratings - the actual text of reviews matters. When multiple reviews mention specific strengths ("Dr. Chen is amazing with kids" or "fastest oil change in town"), AI engines pick up on these patterns and reflect them in their recommendations. Focus on generating authentic reviews that describe specific experiences rather than generic praise.

Directory and Aggregator Listings

Your presence on major data aggregators (Foursquare, Data Axle, Localeze, Neustar) feeds into the broader data ecosystem that AI engines access. Beyond the big aggregators, industry-specific directories matter too. A law firm should be listed on Avvo, Justia, and FindLaw. A restaurant should be on OpenTable, Resy, and local food blogs. These specialized sources carry higher authority signals for their respective verticals.

Structured Data and Website Content

LocalBusiness schema markup on your website helps AI engines understand your business details programmatically. Include your service area, hours, accepted payment methods, and service categories. Beyond schema, having dedicated pages for each service you offer in each location you serve creates more entry points for AI models to learn about your business.

Pricing

Tracking local AI visibility through the Sellm API is cost-effective for businesses of any size. Analysis runs cost less than 1 cent per prompt per provider. A typical local business monitoring 20 prompts across 5 AI providers runs 100 individual checks per analysis.

• Paid plans: Automated scheduled runs with weekly prompt quotas, manual triggers, and coverage across all providers. Each prompt analysis costs less than 1 cent.

For a single-location business, a basic paid plan covers comprehensive local monitoring with room to spare. Multi-location businesses and agencies can scale up for tracking visibility across many markets simultaneously.