Endpoint: `POST /v1/businesses`

Description

The Fetch Businesses endpoint allows users to retrieve business records based on filters such as country, industry, company size, and revenue. It returns a structured dataset optimized for further filtering, deduplication, or previewing records before enrichment. This enables precise data-driven decision-making and lead generation. It is designed to help users efficiently find relevant businesses based on their specific criteria.

Coverage

80M+ businesses across 150+ countries.
Advanced filtering options for precise data extraction.
Optimized for efficiency, returning only the minimal data needed for further processing.

How It Works

Input: Define filter parameters such as country, industry, revenue range, and company size.
Processing: The system searches across multiple datasets to retrieve businesses that meet the criteria.
Output: A structured response containing business records for further analysis or enrichment.

Request Schema

{
  "filters": {
    "country_code": {
      "values": ["us", "ca"]
    },
    "company_size": {
      "values": ["11-50", "51-200"]
    }
  }
}

Pagination, Filtering & Modes Parameters

Data Fetching: Pagination, Filtering & Modes

The API supports flexible data fetching through pagination, filtering, and optional modes to customize the amount and structure of the returned data.

Pagination

To efficiently retrieve large datasets, use the following parameters to paginate the results:

Parameter	Type	Description
`size`	Number	The total maximum number of records to return across all pages. Must be ≤ 10,000.
`page_size`	Number	Number of records to return per page. Maximum value: 100.
`page`	Number	The page number to retrieve (1-based index). Defaults to 1 if not specified.

Behavior Notes:

If size is smaller than page_size * page, the last page may contain fewer records or be empty.
If pagination parameters are omitted, the API may return a default page size (implementation-specific).

Filtering

Filters allow you to narrow down results based on specific field values.
All filters must be included in the request body under the "filters" key, as a JSON object.

Each filter field accepts:

values: for multiple possible matches (e.g. "values": ["11-50", "51-200"])

Example Request Body:

{
  "filters": {
    "company_size": {
      "values": ["11-50", "51-200"]
    }
  }
}

Filter Matching Logic:

Fields using values are matched using an OR condition.
Filters are combined using an AND across different fields.

Mode Parameter

The optional mode parameter defines the level of detail in the returned data.

Mode	Description
`full`	Returns the complete data object for each prospect. Use this when full detail is required for processing or display.
`preview`	Returns a lightweight version of the data, including only key fields. For businesses, this includes: `business_id`, `name`, `domain`, `logo`, `country_name`.

Default Behavior:
If mode is not specified, the API may return a summary version of the data optimized for performance.

Best Practices

Always use page_size to control payload size for better performance.
Use size to set an upper limit and avoid unintentionally requesting large datasets.
Apply filters whenever possible to reduce response size and processing time.
Avoid using mode=full unless full object data is necessary—especially in high-volume requests.

🔎 Note on Filter Usage

Only one category filter can be used per request: choose from either Google category, LinkedIn industry, or NAICS code. Combining them is not supported.

Business Filters

Parameter	Description	Notes / Best Practices
`country_code`	Filter accounts by their HQ’s country using alpha-2 country codes. Example: `["us", "ca"]`	Use autocomplete to search by country name and select the correct two-letter code. Uses a list of `values`. Logic checks whether the country code matches the account location.
`region_country_code`	Filter accounts by their HQ’s region using ISO 3166-2 country-subdivision codes. Example: `["us-ut", "us-ca"]`	Use autocomplete to search for a region after selecting a country. Format will automatically apply as `country-region` (e.g., `us-ca`). Uses multiple `values`. Logic verifies if the region code matches the structured field.
`city_region_country`	Filter accounts by geographic region, down to city-level granularity. Example: `["San Francisco, CA, US"]`	Use autocomplete with cascading dropdowns (country → region → city). Accepts a list of full location strings. Logic checks address fields for match.
`company_size`	Filter accounts by the number of employees at all company sites. Categories: `["1-10", "11-50", ..., "10001+"]`	Use autocomplete to search and select a size bracket from the predefined list. Uses multiple `values`. Logic maps input values to normalized size brackets.
`company_revenue`	Filter accounts by annual revenue generated at all company sites. Categories: `["0-500K", ..., "10B-100B"]`	Use autocomplete to search and select a revenue range from the predefined list. Accepts a list of `values`. Logic compares revenue input against company revenue fields.
`company_age`	Filter accounts by how many years since they were established. Categories: `["0-3", "3-6", ..., "20+"]`	Use autocomplete to select a range representing company age. Accepts multiple `values`. Logic compares establishment year against current date to determine match.
`google_category`	Filter accounts by their classified Google business category. Example: `["Paving contractor", "Retail"]`	Use autocomplete to search and select from Google’s list of business categories. Accepts `values`. Logic filters companies based on Google’s classification field.
`naics_category`	Filter accounts by their 2017 NAICS industry code. Example: `["23", "5611"]`	Use autocomplete to search and select valid NAICS industry codes. Uses multiple `values`. Logic matches selected code to structured industry metadata.
`linkedin_category`	Filter accounts by their classified LinkedIn business category. Example: `["software development", "investment banking"]`	Use autocomplete to search and select from LinkedIn’s list of industry categories. Accepts a list of `values`. Logic filters based on LinkedIn business category field.
`company_tech_stack_category`	Filter accounts by the technology categories they use. Example: `["Marketing", "CRM", "Cloud Services"]`	Use autocomplete to search and select technology categories from a predefined list. Uses multiple `values`. Logic checks whether the account is associated with technologies in those categories.
`company_tech_stack_tech`	Filter accounts by the specific technologies they use. Example: `["JavaScript", "HTML5", "Apache"]`	Use autocomplete to search and select specific technologies from the available list. Accepts a list of `values`. Logic checks for presence of selected technologies in the company’s tech stack.
`company_name`	Filter accounts by specific company names. Example: `["Google", "Walmart"]`	Use autocomplete to search and select company names. Accepts one or more `values`. Logic performs exact or fuzzy match depending on configuration.
`number_of_locations`	Filter accounts by how many office locations they operate. Example: `["1", "2-5", "6+"]`	Use autocomplete to select location range. Accepts a list of `values`. Logic checks for company location count metadata.
`website_keywords`	Filter accounts by specific keywords mentioned on their websites. Example: `["sustainability", "machine learning"]`	Use keyword search to find companies mentioning certain terms. Accepts free-text keywords or predefined tags. Logic performs keyword match in indexed website content.

🎯 Targeting Mid-Sized B2B Software Companies in California for CRM Sales

🔍 Use Case:

A CRM software vendor wants to sell customer relationship management tools to mid-sized software companies in California. The filters ensure that companies already use CRM tools but may be looking for an upgrade.

📌 Filters:

Country: ["us"]
Region (State): ["us-ca"]
Company Size: ["201-500"]
Company Revenue: ["10M-25M"]
LinkedIn Category: ["software development"]
Company Tech Stack Category: ["CRM"]

🎯 Finding Large Public Healthcare Companies in Texas for IT Security Solutions

🔍 Use Case:

A cybersecurity provider wants to target large, publicly traded healthcare companies in Texas that use cloud services and have significant revenue, indicating budget availability for IT security improvements.

📌 Filters:

Country: ["us"]
Region (State): ["us-tx"]
Company Size: ["1001-5000", "5001-10000", "10001+"]
Company Revenue: ["200M-500M", "500M-1B", "1B-10B", "10B-100B"]
NAICS Category: ["62"] (Healthcare & Social Assistance)
Company Tech Stack Category: ["Cloud Services", "Cybersecurity"]

Example Request (cURL)

curl -X POST \
  "https://api.explorium.ai/v1/businesses" \
  -H "API_KEY: your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "mode": "full",
    "size": 10000,    
    "page_size": 100,
    "page": 1,
    "exclude": ["00000230f8f8d86e167e189c7c3b4bd6"],
    "filters": {
      "country_code": {
        "values": ["us"]
      },
      "company_size": {
        "values": ["11-50", "51-200"]
      }
    },
    "request_context": {}
  }'

📌 For detailed endpoint explanations, request examples, and integration tips, explore the documentation sections above.

Body Params - Try Me Example

mode: full
size: 10000
page_size: 100
page: 1
Filters:
  country_code: us
  company_size: 11-50, 51-200

Endpoint: POST /v1/businesses

Description

Data Fetching: Pagination, Filtering & Modes

Pagination

Filtering

Mode Parameter

Best Practices

🔎 Note on Filter Usage

🎯 Targeting Mid-Sized B2B Software Companies in California for CRM Sales

🔍 Use Case:

📌 Filters:

🎯 Finding Large Public Healthcare Companies in Texas for IT Security Solutions

🔍 Use Case:

📌 Filters:

Endpoint: `POST /v1/businesses`