Inspiration & Use Cases

User: "I'm preparing for a sales call with Stripe. Can you research their payments infrastructure team and identify the key decision makers I should connect with?"

AI Agent: I'll help you research Stripe's payments infrastructure team. Let me gather comprehensive information about their organization and key personnel.

[Agent calls get_organization_ids with Stripe's LinkedIn URL]
[Agent calls get_employee_list with infrastructure-focused filters]

Based on my research of Stripe's organization:

**Company Overview:**
- Organization ID: 550e8400-e29b-41d4-a716-446655440000
- Name: Stripe
- LinkedIn ID: 2135371
- Total employees analyzed: 1,247

**Payments Infrastructure Team (47 members):**

**Key Decision Makers:**
1. **Sarah Chen** - VP of Infrastructure Engineering
   - Seniority: VP_DIRECTOR
   - Focus: Platform reliability and scalability

2. **Michael Rodriguez** - Director of Payments Platform
   - Seniority: VP_DIRECTOR
   - Focus: Core payments processing

**Technical Leadership:**
- 12 Senior Engineers focused on payments processing
- 8 Staff Engineers working on platform infrastructure
- 5 Principal Engineers leading architecture decisions

**Recommended Approach:**
1. Start with Sarah Chen (VP Infrastructure) for high-level platform discussions
2. Connect with Michael Rodriguez for payments-specific conversations
3. Engage technical leaders for detailed implementation discussions

Would you like me to research any specific aspects of their technology stack or recent hiring patterns?

class SalesWorkflowEnhancer:
    def __init__(self, lookc_client, crm_client):
        self.lookc = lookc_client
        self.crm = crm_client
    
    async def enrich_lead(self, lead_id: str) -> dict:
        """Automatically enrich CRM leads with LookC data."""
        
        # Get lead information from CRM
        lead = await self.crm.get_lead(lead_id)
        company_url = lead.get('company_linkedin_url')
        
        if not company_url:
            return {'status': 'no_linkedin_url'}
        
        # Research company using LookC
        org_info = await self.lookc.call_tool('get_organization_ids', {
            'linkedin_url': company_url
        })
        
        # Get key stakeholders
        stakeholders = await self.lookc.call_tool('get_employee_list', {
            'org_id': org_info['orgId'],
            'seniority_levels': ['PARTNER_CXO', 'VP_DIRECTOR'],
            'limit': 50
        })
        
        # Update CRM with enriched data
        enrichment_data = {
            'company_size': len(stakeholders['employees']),
            'decision_makers': [
                {
                    'name': emp['name'],
                    'title': emp['title'],
                    'seniority': emp['seniority_level']
                }
                for emp in stakeholders['employees']
                if emp['seniority_level'] == 'PARTNER_CXO'
            ]
        }
        
        await self.crm.update_lead(lead_id, enrichment_data)
        return {'status': 'enriched', 'data': enrichment_data}

class CompanyResearchAgent:
    def __init__(self, lookc_client, llm):
        self.lookc = lookc_client
        self.llm = llm
        
    async def research_for_sales_call(self, company_linkedin_url: str, focus_area: str) -> str:
        """Conduct comprehensive research for sales preparation."""
        
        # Step 1: Get company information
        org_info = await self.lookc.call_tool('get_organization_ids', {
            'linkedin_url': company_linkedin_url
        })
        
        # Step 2: Research relevant team members
        team_members = await self.lookc.call_tool('get_employee_list', {
            'org_id': org_info['orgId'],
            'title_regex': self._get_title_pattern(focus_area),
            'seniority_levels': ['PARTNER_CXO', 'VP_DIRECTOR', 'MANAGER'],
            'limit': 100
        })
        
        # Step 3: Generate insights using LLM
        insights = await self._analyze_research_data(org_info, team_members, focus_area)
        
        return self._format_research_report(org_info, team_members, insights)
    
    def _get_title_pattern(self, focus_area: str) -> str:
        """Map focus areas to relevant job title patterns."""
        patterns = {
            'infrastructure': 'infrastructure|platform|devops|sre|reliability',
            'security': 'security|ciso|infosec|compliance|privacy',
            'data': 'data|analytics|ml|ai|science',
            'payments': 'payment|billing|fintech|financial',
            'engineering': 'engineer|developer|architect|technical'
        }
        return patterns.get(focus_area.lower(), focus_area)