The Companies API SDK for Ruby

gem 'thecompaniesapi'

require 'thecompaniesapi'

tca = TheCompaniesAPI::Client.new(api_token: 'your-api-token')

# Search companies by industry and size
response = tca.search_companies(
  query: [
    { attribute: 'about.industries', operator: 'or', sign: 'equals', values: ['computer-software'] },
    { attribute: 'about.totalEmployees', operator: 'or', sign: 'equals', values: ['10-50'] }
  ],
  size: 25
)

companies = response['companies'] # Companies that match the query
meta = response['meta'] # Meta information

response = tca.search_companies_by_name(
  name: 'The Companies API',
  size: 2
)

companies = response['companies'] # Companies that match the name
meta = response['meta'] # Meta information

# Search 25 companies for a specific prompt
response = tca.search_companies_by_prompt(
  prompt: 'SaaS Companies in the United States with more than 100 employees',
  size: 25
)

companies = response['companies'] # Companies that match the prompt
meta = response['meta'] # Meta information

# Search 25 companies that are similar to Crisp and Intercom
response = tca.search_similar_companies(
  domains: ['crisp.chat', 'intercom.com'],
  size: 25
)

companies = response['companies'] # Companies that are similar to the domains
meta = response['meta'] # Meta information

# Count how many companies are in the computer-software industry
response = tca.count_companies(
  query: [
    {
      attribute: 'about.industries',
      operator: 'or',
      sign: 'equals',
      values: ['computer-software']
    }
  ]
)

count = response # Number of companies that match the query

# Fetch company data from our database without enrichment (faster response)
response = tca.fetch_company(domain: 'microsoft.com')

company = response # The company profile

# Fetch company data and re-analyze it in real-time to get fresh, up-to-date information (slower but more accurate)
response = tca.fetch_company(
  domain: 'microsoft.com',
  refresh: true
)

company = response # The company profile

# Fetch the company profile behind a professional email address
response = tca.fetch_company_by_email(email: 'jack@openai.com')

company = response # The company profile

# Fetch the company profile behind a social network URL
response = tca.fetch_company_by_social(linkedin: 'https://www.linkedin.com/company/apple')

company = response # The company profile

# Fetch the company email patterns for a specific domain
response = tca.fetch_company_email_patterns(domain: 'apple.com')

patterns = response # The company email patterns

# Ask what products a company offers using its domain
response = tca.ask_company(
  domain: 'microsoft.com',
  question: 'What products does this company offer?',
  model: 'large', # 'small' is also available
  fields: [
    {
      key: 'products',
      type: 'array|string',
      description: 'The products that the company offers'
    }
  ]
)

answer = response['answer'] # Structured AI response
meta = response['meta'] # Meta information

# Get AI-generated strategic insights about a company
response = tca.fetch_company_context(domain: 'microsoft.com')

context = response['context'] # Includes market, model, differentiators, etc.
meta = response['meta'] # Meta information

# Analyze company distribution by business type
response = tca.fetch_companies_analytics(
  attribute: 'about.businessType',
  query: [
    {
      attribute: 'locations.headquarters.country.code',
      operator: 'or',
      sign: 'equals',
      values: ['us', 'gb', 'fr']
    }
  ]
)

analytics = response['data'] # Aggregated values
meta = response['meta'] # Meta information

# Export analytics to CSV
response = tca.export_companies_analytics(
  format: 'csv',
  attributes: ['about.industries', 'about.totalEmployees'],
  query: [
    {
      attribute: 'technologies.active',
      operator: 'or',
      sign: 'equals',
      values: ['shopify']
    }
  ]
)

analytics = response['data'] # Aggregated values
meta = response['meta'] # Meta information

# Request an enrichment job on multiple companies
response = tca.request_action(
  domains: ['microsoft.com', 'apple.com'],
  job: 'enrich-companies',
  estimate: false
)

actions = response['actions'] # Track this via fetch_actions
meta = response['meta'] # Meta information

# Fetch recent actions
response = tca.fetch_actions(
  status: 'completed',
  page: 1,
  size: 5
)

actions = response['actions'] # Actions that match the query
meta = response['meta'] # Meta information

# Search industries by keyword
response = tca.search_industries(
  search: 'software',
  size: 10
)

industries = response['industries'] # Industries that match the keyword
meta = response['meta'] # Meta information

# Find industries similar to given ones
response = tca.search_industries_similar(industries: ['saas', 'fintech'])

similar = response['industries'] # Industries that are similar to the given ones
meta = response['meta'] # Meta information

# Search technologies by keyword
response = tca.search_technologies(
  search: 'shopify',
  size: 10
)

technologies = response['technologies'] # Technologies that match the keyword
meta = response['meta'] # Meta information

# Search cities by name
response = tca.search_cities(
  search: 'new york',
  size: 5
)

cities = response['cities'] # Cities that match the name
meta = response['meta'] # Meta information

# Search counties by name
response = tca.search_counties(
  search: 'orange',
  size: 5
)

counties = response['counties'] # Counties that match the name
meta = response['meta'] # Meta information

# Search states by name
response = tca.search_states(
  search: 'california',
  size: 5
)

states = response['states'] # States that match the name
meta = response['meta'] # Meta information

# Search countries by name
response = tca.search_countries(
  search: 'france',
  size: 5
)

countries = response['countries'] # Countries that match the name
meta = response['meta'] # Meta information

# Search continents by name
response = tca.search_continents(
  search: 'asia',
  size: 5
)

continents = response['continents'] # Continents that match the name
meta = response['meta'] # Meta information

# Enrich "chief marketing officer"
response = tca.enrich_job_titles(name: 'chief marketing officer')

job_title = response # Contains department, seniority, etc.

# Fetch your lists
response = tca.fetch_lists

lists = response['lists'] # Lists that match the query
meta = response['meta'] # Meta information

# Create a list of companies
response = tca.create_list(
  name: 'My SaaS List',
  type: 'companies'
)

new_list = response # The new list

# Fetch companies in a list
response = tca.fetch_companies_in_list(list_id: 1234)

companies = response['companies'] # Companies that match the list
meta = response['meta'] # Meta information

# Add companies to a list
response = tca.toggle_companies_in_list(
  list_id: 1234,
  companies: ['apple.com', 'stripe.com']
)

list = response # The updated list

# Fetch your team details
response = tca.fetch_team

team = response # Your team details

# Check API health status
response = tca.fetch_api_health

health = response # The health of the API

# Fetch OpenAPI schema
response = tca.fetch_open_api

schema = response # The OpenAPI schema