Lead Enrichment Pipeline
Pricing
Pay per usage
Go to Apify Store
Lead Enrichment Pipeline
Pricing
Pay per usage
You can access the Lead Enrichment Pipeline programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.
{ "openapi": "3.0.1", "info": { "version": "1.0", "x-build-id": "AA7gY1faLd2IhJRer" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ryanclinton~lead-enrichment-pipeline/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ryanclinton-lead-enrichment-pipeline", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ryanclinton~lead-enrichment-pipeline/runs": { "post": { "operationId": "runs-sync-ryanclinton-lead-enrichment-pipeline", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ryanclinton~lead-enrichment-pipeline/run-sync": { "post": { "operationId": "run-sync-ryanclinton-lead-enrichment-pipeline", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "leads": { "title": "Leads (JSON array)", "minItems": 1, "type": "array", "description": "Array of lead objects. Each can have any combination of: firstName, lastName, fullName, email, phone, companyName, domain, website, title, linkedinUrl. Minimum: at least name+company, name+domain, or email.", "default": [ { "firstName": "Sarah", "lastName": "Chen", "companyName": "Acme Corp", "website": "acmecorp.com", "title": "CTO" }, { "email": "james@betaindustries.com", "companyName": "Beta Industries" }, { "fullName": "Maria Rodriguez", "domain": "pinnacle.io" } ] }, "csvUrl": { "title": "CSV file URL (alternative to JSON)", "type": "string", "description": "Public URL to a CSV file with lead data. Headers auto-mapped to fields. Use this OR the leads JSON array, not both." }, "enrichEmail": { "title": "Find missing emails", "type": "boolean", "description": "Run waterfall email discovery for leads without email addresses. Uses website scraping, pattern detection, and PDL enrichment.", "default": true }, "enrichPhone": { "title": "Find missing phone numbers", "type": "boolean", "description": "Run phone number discovery for leads without phone numbers.", "default": false }, "verifyEmails": { "title": "Verify email deliverability", "type": "boolean", "description": "Run MX + SMTP verification on all discovered emails. Adds emailVerified, emailStatus, emailConfidence fields.", "default": true }, "enrichCompany": { "title": "Enrich company data", "type": "boolean", "description": "Run deep company research for leads with a domain. Adds company description, employee count, tech stack, social profiles.", "default": false }, "scoreLeads": { "title": "Score and grade leads", "type": "boolean", "description": "Run lead scoring engine on all leads with sufficient data. Adds score (0-100) and grade (A-F).", "default": true }, "crmPush": { "title": "Push to CRM", "enum": [ "none", "hubspot", "salesforce" ], "type": "string", "description": "Optionally push enriched leads to a CRM after processing.", "default": "none" }, "hubspotApiKey": { "title": "HubSpot API Key", "type": "string", "description": "Required if pushing to HubSpot. Your private app access token." }, "salesforceCredentials": { "title": "Salesforce Credentials (JSON string)", "type": "string", "description": "Required if pushing to Salesforce. Paste a JSON string: {\"instanceUrl\":\"https://...\",\"accessToken\":\"...\"}. Stored encrypted." }, "outputCsv": { "title": "Generate downloadable CSV", "type": "boolean", "description": "Write enriched leads as a CSV file to the Key-Value Store. Download link appears in the summary record.", "default": true }, "maxLeads": { "title": "Max leads to process", "minimum": 0, "type": "integer", "description": "Maximum number of leads to process. Set to 0 for unlimited.", "default": 0 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } }}OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.
OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.
By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.
You can download the OpenAPI definitions for Lead Enrichment Pipeline from the options below:
If you’d like to learn more about how OpenAPI powers GPTs, read our blog post.
You can also check out our other API clients: