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POST
/
v1
/
businesses
/
match
Match Businesses
curl --request POST \
  --url https://api.explorium.ai/v1/businesses/match \
  --header 'Content-Type: application/json' \
  --header 'api_key: <api-key>' \
  --data '{
  "request_context": {},
  "businesses_to_match": [
    {
      "name": "<string>",
      "domain": "<string>",
      "url": "<string>",
      "linkedin_url": "<string>"
    }
  ]
}'
{
  "response_context": {
    "correlation_id": "<string>",
    "request_status": "success",
    "time_took_in_seconds": 123
  },
  "total_results": 123,
  "total_matches": 1,
  "matched_businesses": []
}

​
Description

The Match Businesses endpoint is the first step in the data enrichment process. It enables users to accurately identify businesses based on their name or domain, returning a unique Business ID that acts as the foundation for all subsequent API interactions. This endpoint is designed to provide high-accuracy business matching by leveraging multiple data sources, validation layers, and proprietary algorithms to ensure precise identification. Once a business is successfully matched, its Business ID becomes the primary key for retrieving enriched company data, accessing financial insights, monitoring real-time business events, and performing additional analytics through other Explorium APIs.
AttributeCoverage Details
Total Businesses80M+ businesses across 150+ countries
Matching AccuracyAdvanced entity resolution for precise business identification
Real-Time UpdatesEnsures the latest business records are used for matching

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How It Works

Input: A list of business identifiers, which can include name, website (domain), and linkedin_company_url.
Processing: The system cross-references multiple internal datasets to determine the best possible match.
Output: A structured response containing the matched Business IDs, preserving the exact order of the input list.

​
Matching Strategy

When using the Business Match endpoint, the system applies a multi-step resolution strategy based on the fields you provide — primarily name and website.

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1. Primary Matching — Smart Fuzzy Logic (Name + Website)

We begin by attempting to match the business using both the company name and website domain.
The company name is evaluated using a smart fuzzy matching algorithm designed to handle:
  • Common variations and abbreviations
  • Typos and noisy CRM inputs
  • Token normalization, semantic similarity, and industry-aware string processing
The website (domain) serves as a strong anchoring signal to disambiguate between similarly named companies and to boost confidence.
This phase ensures high-precision matching while remaining robust to imperfect input data.

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2. Fallback Matching — Website Only

If the system fails to find a confident match using both name and domain, it automatically falls back to matching by website alone.
Because domains are typically unique and consistent across organizations, this fallback ensures matches can still be returned when the company name is incomplete, inconsistent, or incorrect.

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Important Notes

  • Fallback matching from name + website → website only occurs only when both fields are provided.
  • If only the website is supplied, it will be used directly for matching.
  • If only the name is provided, matching relies solely on fuzzy name resolution without domain anchoring.
  • This logic is aligned with the AgentSource model, which emphasizes main site-level resolution (not individual branches).

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Example Scenarios

InputMatching Outcome
Name: Starbucks EMEA, Website: starbucks.comMatch using name + website (fuzzy supported)
Name: abcxyz, Website: starbucks.comFallback — match using website only
Name: Starbucks, Website: fakeurl.xyzNo match — invalid domain
Name: (empty), Website: starbucks.comMatch using website only
Name: Starbuks Intl Ltd, Website: starbucks.comMatch using fuzzy logic + website

​
Recommendation

To get the best possible results:
  • Always include both name and website when calling the Business Match endpoint.
  • This enables the system to apply smart fuzzy matching and fallback recovery for unmatched names — maximizing accuracy, coverage, and confidence.
FieldTypeDescription
businesses_to_matchArrayList of business identifiers to match (can include name, domain, LinkedIn)
nameStringBusiness name provided for matching
domainStringBusiness domain provided for matching
linkedin_urlStringLinkedIn company profile URL (optional, improves matching accuracy)
business_idStringUnique identifier for the matched business (null if not found)
  • Always store the Business ID – It serves as the key for all future enrichment and analytics.
  • Use multiple identifiers (e.g., name + domain, or LinkedIn URL) for higher match accuracy.
  • Batch requests efficiently to optimize API performance.
  • Handle null values gracefully to account for unmatched businesses.
  • Update and validate input data regularly to ensure the most accurate matches.
{
  "request_context": {
    "seller_id": "sample_seller"
  },
  "businesses_to_match": [
    {
      "name": "microsoft",
      "linkedin_url": "https://linkedin.com/company/microsoft"
    }
  ]
}
For detailed endpoint explanations, request examples, and integration tips, explore the documentation sections above.

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Body Params - Try Me Example

name: Apple
domain: apple.com

name: Microsoft
domain: microsoft.com

Authorizations

​
api_key
string
header
required

Body

application/json
​
businesses_to_match
BusinessMatchInput · object[]
required
Required array length: 1 - 50 elements
​
request_context
object

Response

Successful Response

This is base response model for all responses in partner service.

​
response_context
object
required
​
total_results
integer
required

The total_results number matched businesses

Required range: x > 0
​
total_matches
integer
required

The total number of matches.

Required range: x >= 0
​
matched_businesses
Matched Businesses · array

A list of all businesses. If they not matched business_id will None.

  • BusinessMatchOutputWithErrors
  • BusinessMatchOutput
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