The Problem
Gemini and ChatGPT can surface some local business data when asked, but the results are inconsistent, unstructured, and limited by the chatbot's willingness to provide business details. You cannot get a complete list of businesses in a category and location, you cannot get structured hours, ratings, and review counts, and you cannot run the same query programmatically a thousand times for different zip codes. Chatbots are conversation interfaces, not data APIs, and the gap between what they show and what you need for market research is enormous.
The Scavio Solution
Scavio's Google search endpoint returns local pack results with structured business data: name, address, rating, review count, phone number, and category. You query any category plus location combination and get back the same local results Google shows, but in JSON you can process programmatically. Run it across 500 zip codes in a loop and you have a complete market map. No chatbot limitations, no conversation context to manage, no inconsistent formatting to parse.
Before
Before Scavio, local market research meant either asking a chatbot and getting inconsistent results, or subscribing to a $200/mo local data provider for a feature used once per quarter.
After
After Scavio, a simple loop across locations returns structured local business data in JSON. Market research that took a week of manual work completes in an hour of automated queries.
Who It Is For
Market researchers, franchise scouts, and local SEO agencies who need structured local business data across multiple locations without relying on inconsistent chatbot responses or expensive specialized providers.
Key Benefits
- Structured local pack data with ratings, reviews, and contact info
- Programmatic access to any category plus location combination
- Run across hundreds of zip codes in a single script
- Consistent JSON schema unlike chatbot free-text responses
- No conversation limits or session management required
Python Example
import requests
import json
import time
from pathlib import Path
API_KEY = "your_scavio_api_key"
def local_search(category: str, location: str) -> list[dict]:
res = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY},
json={"platform": "google", "query": f"{category} in {location}"},
timeout=15,
)
res.raise_for_status()
data = res.json()
return data.get("local_pack", [])
def market_scan(category: str, locations: list[str]) -> list[dict]:
all_businesses = []
for loc in locations:
businesses = local_search(category, loc)
for biz in businesses:
biz["search_location"] = loc
all_businesses.extend(businesses)
time.sleep(0.5)
return all_businesses
locations = ["Austin TX", "Denver CO", "Portland OR", "Nashville TN"]
results = market_scan("coffee shops", locations)
Path("market_research.json").write_text(json.dumps(results, indent=2))
print(f"Found {len(results)} businesses across {len(locations)} locations")JavaScript Example
const API_KEY = "your_scavio_api_key";
async function localSearch(category, location) {
const res = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST",
headers: { "x-api-key": API_KEY, "content-type": "application/json" },
body: JSON.stringify({ platform: "google", query: `${category} in ${location}` }),
});
if (!res.ok) throw new Error(`scavio ${res.status}`);
const data = await res.json();
return (data.local_pack ?? []).map((biz) => ({ ...biz, searchLocation: location }));
}
async function marketScan(category, locations) {
const allBusinesses = [];
for (const loc of locations) {
const businesses = await localSearch(category, loc);
allBusinesses.push(...businesses);
await new Promise((r) => setTimeout(r, 500));
}
return allBusinesses;
}
const locations = ["Austin TX", "Denver CO", "Portland OR", "Nashville TN"];
const results = await marketScan("coffee shops", locations);
const fs = await import("fs/promises");
await fs.writeFile("market_research.json", JSON.stringify(results, null, 2));
console.log(`Found ${results.length} businesses across ${locations.length} locations`);Platforms Used
Web search with knowledge graph, PAA, and AI overviews