Documentation Index
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The schema parameter defines the structure of your response data. It must be a valid JSON Schema.
Basic Schemas
Simple Array of Companies
Basic company information in an array format.{
"type": "array",
"items": {
"type": "object",
"properties": {
"name": { "type": "string" },
"domain": { "type": "string" },
"description": { "type": "string" }
}
}
}
[
{
"name": "Example Corp",
"domain": "example.com",
"description": "A technology company"
}
]
Single company information as an object.{
"type": "object",
"properties": {
"name": { "type": "string" },
"domain": { "type": "string" },
"founded_year": { "type": "number" },
"headquarters": { "type": "string" },
"industry": { "type": "string" }
}
}
{
"name": "Example Corp",
"domain": "example.com",
"founded_year": 2020,
"headquarters": "San Francisco, CA",
"industry": "Technology"
}
Companies with Executive Contacts
Financial Data
Financial metrics and funding information.{
"type": "array",
"items": {
"type": "object",
"properties": {
"company_name": { "type": "string" },
"revenue": { "type": "string" },
"employees": { "type": "number" },
"funding_stage": { "type": "string" },
"total_funding": { "type": "string" },
"last_funding_date": { "type": "string" },
"valuation": { "type": "string" }
}
}
}
Investor and funding round information.{
"type": "array",
"items": {
"type": "object",
"properties": {
"company_name": { "type": "string" },
"round_type": { "type": "string" },
"amount_raised": { "type": "string" },
"date": { "type": "string" },
"lead_investor": { "type": "string" },
"participating_investors": {
"type": "array",
"items": { "type": "string" }
}
}
}
}
Industry-Specific
Schema optimized for SaaS company data.{
"type": "array",
"items": {
"type": "object",
"properties": {
"company_name": { "type": "string" },
"domain": { "type": "string" },
"product_name": { "type": "string" },
"product_category": { "type": "string" },
"pricing_model": { "type": "string" },
"target_market": { "type": "string" },
"integrations": {
"type": "array",
"items": { "type": "string" }
}
}
}
}
Schema for e-commerce and retail companies.{
"type": "array",
"items": {
"type": "object",
"properties": {
"brand_name": { "type": "string" },
"website": { "type": "string" },
"product_categories": {
"type": "array",
"items": { "type": "string" }
},
"marketplaces": {
"type": "array",
"items": { "type": "string" }
},
"annual_revenue": { "type": "string" },
"headquarters": { "type": "string" }
}
}
}
Schema for healthcare and medical organizations.{
"type": "array",
"items": {
"type": "object",
"properties": {
"facility_name": { "type": "string" },
"facility_type": { "type": "string" },
"address": { "type": "string" },
"specialties": {
"type": "array",
"items": { "type": "string" }
},
"bed_count": { "type": "number" },
"accreditation": { "type": "string" }
}
}
}
Complex Structures
Complex nested structure with multiple levels.{
"type": "array",
"items": {
"type": "object",
"properties": {
"company": {
"type": "object",
"properties": {
"name": { "type": "string" },
"domain": { "type": "string" },
"description": { "type": "string" }
}
},
"leadership": {
"type": "object",
"properties": {
"ceo": { "type": "string" },
"cto": { "type": "string" },
"cfo": { "type": "string" }
}
},
"metrics": {
"type": "object",
"properties": {
"employees": { "type": "number" },
"revenue": { "type": "string" },
"growth_rate": { "type": "string" }
}
}
}
}
}
Schema with required and optional fields.{
"type": "array",
"items": {
"type": "object",
"properties": {
"company_name": { "type": "string" },
"domain": { "type": "string" },
"industry": { "type": "string" },
"founded_year": { "type": "number" },
"description": { "type": "string" }
},
"required": ["company_name", "domain"],
"additionalProperties": false
}
}
Fields listed in required must be present in the response. Optional fields may be omitted if data is unavailable.
Field Types Reference
String
Number
Boolean
Array
Object
Text data of any length.{
"field_name": { "type": "string" }
}
{
"email": { "type": "string", "format": "email" },
"url": { "type": "string", "format": "uri" },
"date": { "type": "string", "format": "date" }
}
Numeric data (integers or decimals).{
"employees": { "type": "number" },
"revenue": { "type": "number" }
}
{
"age": {
"type": "number",
"minimum": 0,
"maximum": 150
}
}
True/false values.{
"is_public": { "type": "boolean" },
"has_funding": { "type": "boolean" }
}
Lists of values.{
"tags": {
"type": "array",
"items": { "type": "string" }
}
}
{
"employees": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": { "type": "string" },
"role": { "type": "string" }
}
}
}
}
Nested data structures.{
"address": {
"type": "object",
"properties": {
"street": { "type": "string" },
"city": { "type": "string" },
"country": { "type": "string" }
}
}
}
Best Practices
Be Specific
Define clear property names that match the data you’re requesting
Use Formats
Specify formats for emails, URLs, dates to ensure proper validation
Mark Required Fields
Use required array to specify which fields must be present
Keep It Simple
Start with simple schemas and add complexity as needed
Tips for Better Results
Match Query to Schema
Ensure your natural language query asks for the same fields defined in your schema
Use Descriptive Names
Use clear, descriptive property names like ceo_email instead of e
Test Incrementally
Start with a few fields and gradually add more to your schema
Review Responses
Check the raw field in responses to understand how the AI interpreted your query
Next Steps
Create Query
Use these schemas in your API queries
Quick Start
See complete examples with schemas
