How to turn a local business website into structured data

A business website often contains the exact operational context that generic company databases miss: what the company sells, where it serves customers, how people contact it, when it is open and how its pages describe those services. The hard part is turning inconsistent HTML into dependable fields.

Why local-business extraction is harder than it looks

Local business sites are inconsistent by design. One company lists services in navigation, another uses visual page-builder blocks, another puts service areas in a footer, and another exposes the best data only through JSON-LD. Site builders can also leak telemetry addresses, accessibility labels, tracking URLs and interface text into the raw page source. A useful extractor therefore has to do more than scrape visible strings.

The fields that become useful

For local SEO tools, lead enrichment, directories and automation, the highest-value website-derived fields are usually business identity, services, service areas, phone numbers, email addresses, locations, opening hours, social profiles, service URLs and schema types. Those fields describe the customer-facing business rather than just its corporate profile.

{
  "business_name": "Example HVAC",
  "services": ["AC Repair", "Heating Repair", "Duct Cleaning"],
  "service_areas": ["Greenville, SC", "Greer, SC"],
  "phones": ["864-555-0123"],
  "opening_hours": ["Monday-Friday 8:00am-5:00pm"]
}

Try it on a real business website

SeaTree built a free Local Business Website Analyzer so you can see the extraction model on a public website before using the API in an application. The demo intentionally exposes only a subset of the full response.