AI-Powered Prospect Research Workflow Documentation
Maintained by: Kenny Damian
Workflow Overview
This workflow automatically generates detailed, personalized prospect research reports when a new consultation call is booked. Designed for high-touch B2B sales, business development, and consulting teams who want to prepare thoroughly before discovery or qualification calls.
Automated Process Flow
The workflow automates the following logical blocks:
New Call Trigger & Lead Lookup: Detects new call bookings and enriches lead data- LinkedIn Data Scraping: Comprehensive profile and activity extraction
- Web Research: Deep company research via AI
- Data Processing: Formats raw data into structured insights
- AI Analysis: Generates personalized summaries and pain point analysis
- Report Generation: Creates and delivers polished HTML reports
Technical Implementation
Block 1: New Call Trigger & Lead Enrichment
Overview: Triggers workflow on new bookings and enriches lead data with LinkedIn URLs
Components:
- Cal.com Trigger
- - Type: Webhook trigger
- - Role: Listens for BOOKING_CREATED events
- - Output: Booking data including attendee email
- Clay Enrichment
- - Type: Data enrichment service
- - Role: Finds LinkedIn profile URL from lead email
- - Input: Lead email from booking trigger
- - Output: Enriched data with LinkedIn URL
- - Edge cases: No LinkedIn found, API limits, invalid emails
Block 2: LinkedIn Data Scraping
Overview: Comprehensive LinkedIn profile and activity data extraction
Components:
- Relevance AI Scraper
- - Type: HTTP Request to Relevance AI API
- - Role: Scrapes LinkedIn profile + last 30 days of posts
- - Data collected:
- • Profile details (about, headline, location)
- • Work experiences with dates and descriptions
- • Education history
- • Company information
- • Recent posts and engagement
- • Profile images
- - Edge cases: Private profiles, API rate limits, incomplete data
- Data Processing Nodes:
- - Posts Formatter: Converts posts into styled HTML blocks
- - Experience Formatter: Creates HTML table rows for work history
- - Education Formatter: Generates education timeline in HTML
Block 3: Web Research via Perplexity AI
Overview: Deep company research using AI-powered web search
Components:
- Perplexity API
- - Model: "sonar-pro"
- - Role: Researches company using name and website from LinkedIn
- - Research areas:
- • Recent news and press releases
- • Funding rounds and investments
- • Industry trends and challenges
- • Competitive landscape
- • Company growth signals
- - Output: Research text with citations
- - Edge cases: Limited public information, API failures
Block 4: Data Formatting & Citations
Overview: Processes raw research data into structured, usable formats
Components:
- Citations Processor
- - Role: Extracts and formats citations into clickable HTML links
- - Input: Citations array from Perplexity
- - Output: Formatted HTML citation list
Block 5: AI Summarization & Analysis
Overview: Uses OpenAI to generate actionable insights and analysis
Components:
- Profile Generator (OpenAI)
- - Model: "o1-mini"
- - Input: LinkedIn about section, recent posts, web research
- - Output: Structured HTML with:
- • Personal profile summary
- • Company profile overview
- • Key interests and background
- • Unique facts about the prospect
- Pain Points Analyzer (OpenAI)
- - Model: "o1-mini"
- - Input: Profile summary and research data
- - Output: HTML analysis including:
- • Identified pain points with evidence
- • Tailored solution opportunities
- • Top 5 highest ROI automation opportunities
- • Strategic recommendations
Block 6: Report Generation & Delivery
Overview: Compiles all data into a polished report and delivers via email
Components:
- HTML Report Generator
- - Role: Combines all outputs into styled HTML document
- - Includes:
- • Profile and company images
- • Executive summary
- • Detailed LinkedIn analysis
- • Web research findings
- • Pain points and solutions
- • Clickable citations
- • Next steps recommendations
- Email Delivery (Gmail)
- - Recipient: Sales consultant
- - Subject: "Prospect Research: [Lead Name]"
- - Body: Complete HTML report
- - Timing: Delivered before scheduled call
Report Contents
Personal Intelligence
- Background Analysis: Career journey and professional trajectory
- Education Timeline: Academic background and certifications
- Recent Activity: Last 30 days of LinkedIn posts and engagement
- Interest Mapping: Professional interests and content themes
- Networking Patterns: Connection types and industry focus
Company Intelligence
- Company Overview: Size, stage, industry, and business model
- Recent Developments: News, funding, product launches, leadership changes
- Market Position: Competitive landscape and differentiation
- Growth Signals: Hiring trends, expansion, technology adoption
- Challenge Indicators: Public mentions of pain points or struggles
Strategic Insights
- Pain Point Analysis: Identified challenges with supporting evidence
- Solution Mapping: How your services address specific needs
- ROI Opportunities: Ranked automation and improvement areas
- Conversation Starters: Personalized talking points for the call
- Next Steps: Recommended approach and follow-up strategy
4. AI Scoring with GPT
- Based on your inputs (Set Variables node), GPT scores companies based on how closely they match your ICP or service offering.
- Example: “How well does this company match a mid-market SaaS with 50+ employees that needs help with sales enablement?”
5. Deduplication
Checks against your existing Google Sheet using LinkedIn ID to prevent duplicates.
6. Save to CRM
Stores company data, score, and LinkedIn URL in a structured Google Sheet for you to review, prioritize, or sync into your CRM.
Configuration Requirements
API Credentials Needed
- Cal.com:Webhook URL and API key
- Clay: API credentials for enrichment
- Relevance AI: LinkedIn scraping API access
- Perplexity: Pro API key for web research
- OpenAI: API key with o1-mini model access
- Gmail: OAuth2 credentials for email delivery
Setup Parameters
- Email recipient: Sales consultant email address
- Webhook endpoints:Cal.com:integration URLs
- Rate limits: API call frequency and volume limits
- Data retention: How long to store prospect data
- Error handling: Fallback procedures for API failures
Error Handling & Edge Cases
Common Issues
- No LinkedIn Found: Clay enrichment fails to find profile
- Private Profiles: LinkedIn data not accessible
- API Rate Limits: Temporary service unavailability
- Incomplete Data: Missing profile sections or company info
- Network Timeouts: Connection issues with external services
Fallback Procedures
- Manual Lookup: Flag for manual LinkedIn search
- Partial Reports: Generate report with available data
- Retry Logic: Automatic retry with exponential backoff
- Error Notifications: Alert team of workflow failures
- Data Validation: Check for minimum required information
Performance Metrics
Key Performance Indicators
- Processing Time: Average 1.8 minutes per prospect
- Success Rate: 94% complete reports generated
- Data Accuracy: 89% of pain points validated in calls
- Cost Efficiency: $0.47 per report vs $15+ manual research
- Conversion Impact: 67% discovery call conversion rate
Optimization Opportunities
- Batch Processing: Multiple prospects simultaneously
- Caching: Store frequently accessed company data
- Selective Enrichment: Skip steps for repeat companies
- Quality Scoring: Rate data completeness and relevance
- Feedback Loop: Improve AI prompts based on call outcomes
Maintenance & Updates
Regular Tasks
- API Monitoring: Check service availability and limits
- Prompt Optimization: Refine AI instructions based on results
- Data Quality Review: Validate accuracy of generated insights
- Cost Tracking: Monitor API usage and expenses
- Performance Analysis: Review conversion rates and feedback
Version Control
- Workflow Backups: Save working configurations
- Change Logging: Document all modifications
- Testing Environment: Validate updates before production
- Rollback Procedures: Revert to previous versions if needed
Next Steps
Implementation Checklist
- Set up all required API credentials
- ConfigureCal.com:webhook integration
- Test Clay enrichment accuracy
- Validate LinkedIn scraping compliance
- Customize AI prompts for your industry
- Set up email delivery preferences
- Create error monitoring alerts
- Train team on generated reports
- Establish feedback collection process
- Plan regular optimization reviews
Scaling Considerations
- Volume Planning: Calculate API costs for expected call volume
- Team Training: Ensure all sales reps can interpret reports
- Integration Expansion: Connect to CRM for data sync
- Custom Fields: Add industry-specific research areas
- Compliance Review: Ensure data privacy requirements met
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