Generate Personalized Sales Leads with Claude AI & Explorium for Gmail Outreach

Description

Outbound Agent - AI-Powered Lead Generation with Natural Language Prospecting

This n8n workflow transforms natural language queries into targeted B2B prospecting campaigns by combining Explorium's data intelligence with AI-powered research and personalized email generation. Simply describe your ideal customer profile in plain English, and the workflow automatically finds prospects, enriches their data, researches them, and creates personalized email drafts.

DEMO

Template Demo

Credentials Required

To use this workflow, set up the following credentials in your n8n environment:

Anthropic API
Type:** API Key
Used for:** AI Agent query interpretation, email research, and email writing
Get your API key at Anthropic Console

Explorium API
Type:** Generic Header Auth
Header:** Authorization
Value:** Bearer YOUR_API_KEY
Used for:** Prospect matching, contact enrichment, professional profiles, and MCP research
Get your API key at Explorium Dashboard

Explorium MCP
Type:** HTTP Header Auth
Used for:** Real-time company and prospect intelligence research
Connect to: https://mcp.explorium.ai/mcp

Gmail
Type:** OAuth2
Used for:** Creating email drafts
Alternative options: Outlook, Mailchimp, SendGrid, Lemlist

Go to Settings → Credentials, create these credentials, and assign them in the respective nodes before running the workflow.

Workflow Overview

Node 1: When chat message received
This node creates an interactive chat interface where users can describe their prospecting criteria in natural language.

Type:** Chat Trigger
Purpose:** Accept natural language queries like "Get 5 marketing leaders at fintech startups who joined in the past year and have valid contact information"
Example Prompts:**
"Find SaaS executives in New York with 50-200 employees"
"Get marketing directors at healthcare companies"
"Show me VPs at fintech startups with recent funding"

Node 2: Chat or Refinement
This code node manages the conversation flow, handling both initial user queries and validation error feedback.

Function:** Routes either the original chat input or validation error messages to the AI Agent
Dynamic Input:** Combines chatInput and errorInput fields
Purpose:** Creates a feedback loop for validation error correction

Node 3: AI Agent
The core intelligence node that interprets natural language and generates structured API calls.

Functionality:
Interprets user intent from natural language queries
Maps concepts to Explorium API filters (job levels, departments, company size, revenue, location, etc.)
Generates valid JSON requests with precise filter criteria
Handles off-topic queries with helpful guidance
Connected to MCP Client for real-time filter specifications

AI Components:
Anthropic Chat Model:** Claude Sonnet 4 for query interpretation
Simple Memory:** Maintains conversation context (100 message window)
Output Parser:** Structured JSON output with schema validation
MCP Client:** Connected to https://mcp.explorium.ai/mcp for Explorium specifications

System Instructions:
Expert in converting natural language to Explorium API filters
Can revise previous responses based on validation errors
Strict adherence to allowed filter values and formats
Default settings: mode: "full", size: 10000, page_size: 100, has_email: true

Node 4: API Call Validation
This code node validates the AI-generated API request against Explorium's filter specifications.

Validation Checks:
Filter key validity (only allowed filters from approved list)
Value format correctness (enums, ranges, country codes)
No duplicate values in arrays
Proper range structure for experience fields (total_experience_months, current_role_months)
Required field presence

Allowed Filters:
country_code, region_country_code, company_country_code, company_region_country_code
company_size, company_revenue, company_age, number_of_locations
google_category, naics_category, linkedin_category, company_name
city_region_country, website_keywords
has_email, has_phone_number
job_level, job_department, job_title
business_id, total_experience_months, current_role_months

Output:
isValid: Boolean validation status
validationErrors: Array of specific error messages

Node 5: Is API Call Valid?
Conditional routing node that determines the next step based on validation results.

If Valid:** Proceed to Explorium API: Fetch Prospects
If Invalid:** Route to Validation Prompter for correction

Node 6: Validation Prompter
Generates detailed error feedback for the AI Agent when validation fails.
This creates a self-correcting loop where the AI learns from validation errors and regenerates compliant requests by routing back to Node 2 (Chat or Refinement).

Node 7: Explorium API: Fetch Prospects
Makes the validated API call to Explorium's prospect database.

Method:** POST
Endpoint:** /v1/prospects/fetch
Authentication:** Header Auth (Bearer token)
Input:** JSON with filters, mode, size, page_size, page
Returns:** Array of matched prospects with prospect IDs based on filter criteria

Node 8: Pull Prospect IDs
Extracts prospect IDs from the fetch response for bulk enrichment.

Input:** Full fetch response with prospect data
Output:** Array of prospect_id values formatted for enrichment API

Node 9: Explorium API: Contact Enrichment
Single enrichment node that enhances prospect data with both contact and profile information.

Method:** POST
Endpoint:** /v1/prospects/enrich
Enrichment Types:** contacts, profiles
Authentication:** Header Auth (Bearer token)
Input:** Array of prospect IDs from Node 8

Returns:
Contacts:** Professional emails (current, verified), phone numbers (mobile, work), email validation status, all available email addresses
Profiles:** Full professional history, current role details, company information, skills and expertise, education background, experience timeline, job titles and seniority levels

Node 10: Clean Output Data
Transforms and structures the enriched data for downstream processing.

Node 11: Loop Over Items
Iterates through each prospect to generate individualized research and emails.

Batch Size:** 1 (processes prospects one at a time)
Purpose:** Enable personalized research and email generation for each prospect
Loop Control:** Processes until all prospects are complete

Node 12: Research Email
AI-powered research agent that investigates each prospect using Explorium MCP.

Input Data:
Prospect name, job title, company name, company website
LinkedIn URL, job department, skills

Research Focus:
Company automation tool usage (n8n, Zapier, Make, HubSpot, Salesforce)
Data enrichment practices
Tech stack and infrastructure (Snowflake, Segment, etc.)
Recent company activity and initiatives
Pain points related to B2B data (outdated CRM data, manual enrichment, static workflows)
Public content (speaking engagements, blog posts, thought leadership)

AI Components:
Anthropic Chat Model1:** Claude Sonnet 4 for research
Simple Memory1:** Maintains research context
Explorium MCP1:** Connected to https://mcp.explorium.ai/mcp for real-time intelligence

Output: Structured JSON with research findings including automation tools, pain points, personalization notes

Node 13: Email Writer
Generates personalized cold email drafts based on research findings.

Input Data:
Contact info from Loop Over Items
Current experience and skills
Research findings from Research Email agent
Company data (name, website)

AI Components:
Anthropic Chat Model3:** Claude Sonnet 4 for email writing
Structured Output Parser:** Enforces JSON schema with email, subject, message fields

Output Schema:
email: Selected prospect email address (professional preferred)
subject: Compelling, personalized subject line
message: HTML formatted email body

Node 14: Create a draft (Gmail)
Creates email drafts in Gmail for review before sending.

Resource:** Draft
Subject:** From Email Writer output
Message:** HTML formatted email body
Send To:** Selected prospect email address
Authentication:** Gmail OAuth2

After Creation: Loops back to Node 11 (Loop Over Items) to process next prospect

Alternative Output Options:
Outlook:** Create drafts in Microsoft Outlook
Mailchimp:** Add to email campaign
SendGrid:** Queue for sending
Lemlist:** Add to cold email sequence

Workflow Flow Summary

Input: User describes target prospects in natural language via chat interface
Interpret: AI Agent converts query to structured Explorium API filters using MCP
Validate: API call validation ensures filter compliance
Refine: If invalid, error feedback loop helps AI correct the request
Fetch: Retrieve matching prospect IDs from Explorium database
Enrich: Parallel bulk enrichment of contact details and professional profiles
Clean: Transform and structure enriched data
Loop: Process each prospect individually
Research: AI agent uses Explorium MCP to gather company and prospect intelligence
Write: Generate personalized email based on research
Draft: Create reviewable email drafts in preferred platform

This workflow eliminates manual prospecting work by combining natural language processing, intelligent data enrichment, automated research, and personalized email generation—taking you from "I need marketing leaders at fintech companies" to personalized, research-backed email drafts in minutes.

Customization Options

Flexible Triggers
The chat interface can be replaced with:
Scheduled runs for recurring prospecting
Webhook triggers from CRM updates
Manual execution for ad-hoc campaigns

Scalable Enrichment
Adjust enrichment depth by:
Adding more Explorium API endpoints (technographics, funding, news)
Configuring prospect batch sizes
Customizing data cleaning logic

Output Destinations
Route emails to your preferred platform:
Email Platforms:** Gmail, Outlook, SendGrid, Mailchimp
Sales Tools:** Lemlist, Outreach, SalesLoft
CRM Integration:** Salesforce, HubSpot (create leads with research)
Collaboration:** Slack notifications, Google Docs reports

AI Model Flexibility
Swap AI providers based on your needs:
Default: Anthropic Claude (Sonnet 4)
Alternatives: OpenAI GPT-4, Google Gemini

Setup Notes

Domain Filtering: The workflow prioritizes professional emails—customize email selection logic in the Clean Output Data node
MCP Configuration: Explorium MCP requires Header Auth setup—ensure credentials are properly configured
Rate Limits: Adjust Loop Over Items batch size if hitting API rate limits
Memory Context: Simple Memory maintains conversation history—increase window length for longer sessions
Validation: The AI self-corrects through validation loops—monitor early runs to ensure filter accuracy

This workflow represents a complete AI-powered sales development representative (SDR) that handles prospecting, research, and personalized outreach with minimal human intervention.