Best Klenty Phone Validation API & Email Verification Integration 2025

import requests
import json
import asyncio
from typing import Dict, List, Optional
from dataclasses import dataclass, asdict
from datetime import datetime

@dataclass
class SalesEngagementContact:
    email: str
    first_name: Optional[str] = None
    last_name: Optional[str] = None
    position: Optional[str] = None
    company: Optional[str] = None
    domain: Optional[str] = None
    phone: Optional[str] = None
    linkedin_url: Optional[str] = None
    crm_id: Optional[str] = None
    email_validation: Optional[Dict] = None
    phone_validation: Optional[Dict] = None
    engagement_intelligence: Optional[Dict] = None
    automation_data: Optional[Dict] = None
    deliverability_score: int = 0
    engagement_score: int = 0
    automation_priority: str = "medium"
    channel_preferences: Optional[Dict] = None
    engagement_readiness: str = "pending"
    klenty_optimization: Optional[Dict] = None
    enhancement_timestamp: str = ""

class Klenty1lookupIntegration:
    def __init__(self, klenty_api_key: str, lookup_api_key: str):
        self.klenty_api_key = klenty_api_key
        self.lookup_api_key = lookup_api_key
        self.klenty_base_url = "https://app.klenty.com/api/v1"
        self.lookup_base_url = "https://api.1lookup.app/v1"
    
    async def optimize_sales_engagement_cadence(self, cadence_id: str) -> Dict:
        """
        Optimize existing Klenty cadence with comprehensive sales engagement intelligence
        """
        try:
            # Step 1: Get cadence prospects from Klenty
            cadence_prospects = await self._get_klenty_cadence_prospects(cadence_id)
            
            if not cadence_prospects:
                return {"error": "No prospects found in cadence"}
            
            # Step 2: Enhance all prospects with sales engagement intelligence
            enhanced_prospects = await self._bulk_enhance_for_sales_engagement(cadence_prospects)
            
            # Step 3: Generate engagement optimization strategies
            engagement_strategies = await self._generate_engagement_strategies(enhanced_prospects)
            
            # Step 4: Create multi-channel enhancement recommendations
            multichannel_optimization = self._generate_multichannel_optimization(enhanced_prospects)
            
            # Step 5: Update Klenty cadence with enhanced data
            update_results = await self._update_klenty_cadence(cadence_id, enhanced_prospects)
            
            return {
                "cadence_id": cadence_id,
                "total_prospects": len(cadence_prospects),
                "enhanced_prospects": len(enhanced_prospects),
                "engagement_stats": self._calculate_engagement_stats(enhanced_prospects),
                "engagement_strategies": engagement_strategies,
                "multichannel_optimization": multichannel_optimization,
                "update_results": update_results
            }
            
        except Exception as e:
            return {"error": f"Sales engagement cadence optimization failed: {e}"}
    
    async def create_enhanced_engagement_cadence(self, cadence_name: str,
                                               prospects: List[Dict],
                                               cadence_config: Dict = None) -> Dict:
        """
        Create enhanced Klenty cadence with comprehensive sales engagement optimization
        """
        try:
            # Step 1: Enhance prospects with sales engagement intelligence
            enhanced_prospects = await self._bulk_enhance_for_sales_engagement(prospects)
            
            # Step 2: Generate optimized cadence structure
            cadence_structure = await self._generate_engagement_cadence_structure(
                enhanced_prospects, cadence_config
            )
            
            # Step 3: Create Klenty cadence with optimization
            cadence_data = await self._create_klenty_cadence(cadence_name, cadence_structure)
            
            if not cadence_data:
                return {"error": "Failed to create Klenty cadence"}
            
            # Step 4: Apply sales engagement optimizations
            optimization_results = await self._apply_sales_engagement_optimizations(
                cadence_data['id'], enhanced_prospects
            )
            
            return {
                "cadence_id": cadence_data['id'],
                "cadence_name": cadence_name,
                "total_prospects": len(enhanced_prospects),
                "engagement_optimization": self._analyze_engagement_optimization(enhanced_prospects),
                "optimization_results": optimization_results
            }
            
        except Exception as e:
            return {"error": f"Enhanced engagement cadence creation failed: {e}"}
    
    async def _bulk_enhance_for_sales_engagement(self, prospects: List[Dict]) -> List[SalesEngagementContact]:
        """Enhance multiple prospects for optimal sales engagement"""
        enhancement_tasks = []
        
        for prospect_data in prospects:
            engagement_contact = SalesEngagementContact(
                email=prospect_data.get('email', ''),
                first_name=prospect_data.get('first_name') or prospect_data.get('firstName'),
                last_name=prospect_data.get('last_name') or prospect_data.get('lastName'),
                position=prospect_data.get('position') or prospect_data.get('title'),
                company=prospect_data.get('company'),
                domain=prospect_data.get('domain'),
                linkedin_url=prospect_data.get('linkedin_url'),
                crm_id=prospect_data.get('crm_id'),
                enhancement_timestamp=datetime.now().isoformat()
            )
            
            task = self._enhance_single_contact_for_sales_engagement(engagement_contact)
            enhancement_tasks.append(task)
        
        # Process all enhancements in parallel
        await asyncio.gather(*enhancement_tasks, return_exceptions=True)
        
        return [task.result() for task in enhancement_tasks if not isinstance(task.result(), Exception)]
    
    async def _enhance_single_contact_for_sales_engagement(self, contact: SalesEngagementContact) -> None:
        """Comprehensive single contact enhancement for sales engagement"""
        enhancement_tasks = []
        
        # Email validation with engagement optimization
        if contact.email:
            task1 = self._validate_email_for_sales_engagement(contact.email)
            enhancement_tasks.append(task1)
        
        # Phone enrichment for multi-channel engagement
        if contact.first_name and contact.last_name:
            task2 = self._enrich_phone_for_sales_engagement(contact)
            enhancement_tasks.append(task2)
        
        # Sales engagement intelligence gathering
        task3 = self._gather_sales_engagement_intelligence(contact)
        enhancement_tasks.append(task3)
        
        # Execute all enhancements
        results = await asyncio.gather(*enhancement_tasks, return_exceptions=True)
        
        # Process email validation
        if len(results) > 0 and not isinstance(results[0], Exception):
            contact.email_validation = results[0]
            contact.deliverability_score = results[0].get('deliverability_score', 0)
        
        # Process phone enrichment
        if len(results) > 1 and not isinstance(results[1], Exception) and results[1]:
            contact.phone_validation = results[1]
            contact.phone = results[1].get('phone_number')
        
        # Process engagement intelligence
        if len(results) > 2 and not isinstance(results[2], Exception):
            contact.engagement_intelligence = results[2]
        
        # Generate automation data for Klenty
        contact.automation_data = self._generate_klenty_automation_data(contact)
        
        # Calculate engagement scores and optimizations
        contact.engagement_score = self._calculate_sales_engagement_score(contact)
        contact.automation_priority = self._determine_automation_priority(contact)
        contact.channel_preferences = self._analyze_channel_preferences(contact)
        contact.engagement_readiness = self._assess_engagement_readiness(contact)
        contact.klenty_optimization = self._generate_klenty_optimization_recommendations(contact)
    
    async def _validate_email_for_sales_engagement(self, email: str) -> Dict:
        """Advanced email validation optimized for sales engagement"""
        url = f"{self.lookup_base_url}/email/validate"
        headers = {
            'Authorization': f'Bearer {self.lookup_api_key}',
            'Content-Type': 'application/json'
        }
        
        payload = {
            'email': email,
            'sales_engagement_focus': True,
            'automation_optimization': True,
            'engagement_prediction': True,
            'professional_validation': True
        }
        
        async with aiohttp.ClientSession() as session:
            async with session.post(url, json=payload, headers=headers) as response:
                if response.status == 200:
                    result = await response.json()
                    
                    # Calculate sales engagement deliverability score
                    score = 0
                    if result.get('deliverable'): score += 35
                    if result.get('valid_format'): score += 20
                    if not result.get('spam_trap'): score += 20
                    if result.get('professional_email', True): score += 25
                    
                    result['deliverability_score'] = score
                    result['engagement_suitability'] = 'excellent' if score >= 85 else 'good' if score >= 70 else 'fair' if score >= 50 else 'poor'
                    return result
                return {'deliverable': False, 'deliverability_score': 0, 'engagement_suitability': 'poor'}
    
    async def _enrich_phone_for_sales_engagement(self, contact: SalesEngagementContact) -> Optional[Dict]:
        """Phone enrichment optimized for sales engagement campaigns"""
        url = f"{self.lookup_base_url}/enrich/phone"
        headers = {
            'Authorization': f'Bearer {self.lookup_api_key}',
            'Content-Type': 'application/json'
        }
        
        payload = {
            'first_name': contact.first_name,
            'last_name': contact.last_name,
            'company': contact.company,
            'domain': contact.domain,
            'email': contact.email,
            'sales_engagement_focus': True,
            'multi_channel_optimization': True
        }
        
        try:
            async with aiohttp.ClientSession() as session:
                async with session.post(url, json=payload, headers=headers) as response:
                    if response.status == 200:
                        result = await response.json()
                        
                        # Assess sales engagement value
                        if result.get('valid'):
                            result['engagement_value'] = 95 if result.get('business_phone') and result.get('direct_dial') else 80 if result.get('business_phone') else 65
                            result['sales_engagement_capability'] = 'high'
                        else:
                            result['engagement_value'] = 0
                            result['sales_engagement_capability'] = 'low'
                        
                        return result
        except:
            pass
        
        return None
    
    async def _gather_sales_engagement_intelligence(self, contact: SalesEngagementContact) -> Optional[Dict]:
        """Gather comprehensive intelligence for sales engagement optimization"""
        url = f"{self.lookup_base_url}/enrich/sales_engagement"
        headers = {
            'Authorization': f'Bearer {self.lookup_api_key}',
            'Content-Type': 'application/json'
        }
        
        payload = {
            'first_name': contact.first_name,
            'last_name': contact.last_name,
            'company': contact.company,
            'email': contact.email,
            'position': contact.position,
            'linkedin_url': contact.linkedin_url,
            'engagement_optimization': True,
            'automation_intelligence': True
        }
        
        try:
            async with aiohttp.ClientSession() as session:
                async with session.post(url, json=payload, headers=headers) as response:
                    if response.status == 200:
                        return await response.json()
        except:
            pass
        
        return None
    
    def _calculate_sales_engagement_score(self, contact: SalesEngagementContact) -> int:
        """Calculate comprehensive sales engagement effectiveness score"""
        score = 0
        
        # Email deliverability (0-35 points)
        score += contact.deliverability_score * 0.35
        
        # Multi-channel capability (0-30 points)
        channels = 1  # Email baseline
        if contact.phone_validation and contact.phone_validation.get('valid'):
            score += 15
            channels += 1
        if contact.linkedin_url:
            score += 10
            channels += 1
        if channels >= 3:
            score += 5  # Bonus for full multi-channel capability
        
        # Professional relevance (0-20 points)
        if contact.position and contact.company:
            score += 10
            position_lower = contact.position.lower()
            if any(title in position_lower for title in ['ceo', 'founder', 'president']):
                score += 10
            elif any(title in position_lower for title in ['vp', 'director', 'head']):
                score += 7
            elif any(title in position_lower for title in ['manager', 'lead', 'senior']):
                score += 5
        
        # Engagement intelligence bonus (0-10 points)
        if contact.engagement_intelligence:
            intelligence_quality = len([v for v in contact.engagement_intelligence.values() if v])
            score += min(intelligence_quality * 2, 10)
        
        # Contact completeness (0-5 points)
        completeness = 0
        if contact.first_name and contact.last_name: completeness += 2
        if contact.company: completeness += 2
        if contact.domain: completeness += 1
        score += completeness
        
        return min(int(score), 100)
    
    def _determine_automation_priority(self, contact: SalesEngagementContact) -> str:
        """Determine automation priority based on engagement score"""
        if contact.engagement_score >= 85:
            return "critical"
        elif contact.engagement_score >= 70:
            return "high"
        elif contact.engagement_score >= 50:
            return "medium"
        else:
            return "low"
    
    def _analyze_channel_preferences(self, contact: SalesEngagementContact) -> Dict:
        """Analyze optimal channel preferences for sales engagement"""
        preferences = {
            'email': 0,
            'phone': 0,
            'linkedin': 0
        }
        
        # Email preference (baseline)
        if contact.email_validation and contact.email_validation.get('deliverable'):
            preferences['email'] = contact.deliverability_score
        
        # Phone preference
        if contact.phone_validation and contact.phone_validation.get('valid'):
            preferences['phone'] = contact.phone_validation.get('engagement_value', 0)
        
        # LinkedIn preference
        if contact.linkedin_url and contact.engagement_intelligence:
            linkedin_data = contact.engagement_intelligence.get('linkedin_data', {})
            if linkedin_data.get('active_user'):
                preferences['linkedin'] = 75
            elif linkedin_data.get('profile_complete'):
                preferences['linkedin'] = 60
            else:
                preferences['linkedin'] = 40
        
        return preferences
    
    def _assess_engagement_readiness(self, contact: SalesEngagementContact) -> str:
        """Assess contact's readiness for sales engagement campaigns"""
        readiness_factors = 0
        
        # Email deliverability
        if contact.email_validation and contact.email_validation.get('deliverable'):
            readiness_factors += 2
        
        # Multi-channel capability
        available_channels = len([ch for ch, score in contact.channel_preferences.items() if score > 50]) if contact.channel_preferences else 1
        readiness_factors += min(available_channels, 3)
        
        # Professional information
        if contact.position and contact.company:
            readiness_factors += 2
        
        # Engagement intelligence
        if contact.engagement_intelligence:
            readiness_factors += 1
        
        if readiness_factors >= 7:
            return "excellent"
        elif readiness_factors >= 5:
            return "good"
        elif readiness_factors >= 3:
            return "fair"
        else:
            return "limited"
    
    def _generate_klenty_optimization_recommendations(self, contact: SalesEngagementContact) -> Dict:
        """Generate Klenty-specific optimization recommendations"""
        optimization = {
            'cadence_type': 'standard',
            'channel_sequence': ['email'],
            'personalization_level': 'basic',
            'automation_triggers': {},
            'crm_sync_settings': {},
            'engagement_optimization': {}
        }
        
        # Cadence type based on engagement score
        if contact.engagement_score >= 85:
            optimization['cadence_type'] = 'premium_multichannel'
        elif contact.engagement_score >= 70:
            optimization['cadence_type'] = 'enhanced_engagement'
        elif contact.engagement_score >= 50:
            optimization['cadence_type'] = 'standard_automated'
        else:
            optimization['cadence_type'] = 'basic_sequence'
        
        # Channel sequence optimization
        if contact.channel_preferences:
            sorted_channels = sorted(
                contact.channel_preferences.items(),
                key=lambda x: x[1],
                reverse=True
            )
            optimization['channel_sequence'] = [ch for ch, score in sorted_channels if score > 50]
        
        # Personalization level
        engagement_intel_count = len(contact.engagement_intelligence.values()) if contact.engagement_intelligence else 0
        if engagement_intel_count >= 5:
            optimization['personalization_level'] = 'ultra_deep'
        elif engagement_intel_count >= 3:
            optimization['personalization_level'] = 'deep'
        elif engagement_intel_count >= 1:
            optimization['personalization_level'] = 'medium'
        
        # Automation triggers
        optimization['automation_triggers'] = {
            'high_value_prospect': contact.engagement_score >= 80,
            'phone_follow_up': bool(contact.phone_validation and contact.phone_validation.get('valid')),
            'linkedin_sequence': bool(contact.linkedin_url),
            'immediate_engagement': contact.engagement_readiness in ['excellent', 'good']
        }
        
        # CRM sync settings
        optimization['crm_sync_settings'] = {
            'sync_frequency': 'real_time' if contact.engagement_score >= 70 else 'hourly',
            'bidirectional_sync': contact.engagement_score >= 60,
            'field_mapping_priority': 'high' if contact.engagement_readiness in ['excellent', 'good'] else 'standard'
        }
        
        # Engagement optimization settings
        optimization['engagement_optimization'] = {
            'sequence_length': min(3 + (contact.engagement_score // 20), 8),
            'follow_up_intervals': self._calculate_optimal_engagement_intervals(contact),
            'response_tracking': True,
            'multichannel_coordination': len(optimization['channel_sequence']) > 1
        }
        
        return optimization
    
    def _calculate_engagement_stats(self, contacts: List[SalesEngagementContact]) -> Dict:
        """Calculate comprehensive sales engagement statistics"""
        total = len(contacts)
        if total == 0:
            return {}
        
        deliverable = sum(1 for c in contacts if c.email_validation and c.email_validation.get('deliverable'))
        high_engagement = sum(1 for c in contacts if c.engagement_score >= 70)
        multichannel_ready = sum(1 for c in contacts if c.channel_preferences and len([ch for ch, score in c.channel_preferences.items() if score > 50]) > 1)
        engagement_ready = sum(1 for c in contacts if c.engagement_readiness in ['excellent', 'good'])
        phone_enhanced = sum(1 for c in contacts if c.phone_validation and c.phone_validation.get('valid'))
        
        return {
            'total_contacts': total,
            'deliverable_rate': (deliverable / total) * 100,
            'high_engagement_rate': (high_engagement / total) * 100,
            'multichannel_ready_rate': (multichannel_ready / total) * 100,
            'engagement_ready_rate': (engagement_ready / total) * 100,
            'phone_enhanced_rate': (phone_enhanced / total) * 100,
            'average_engagement_score': sum(c.engagement_score for c in contacts) / total,
            'critical_priority_contacts': sum(1 for c in contacts if c.automation_priority == 'critical')
        }

# Example usage for Klenty sales engagement optimization
async def main():
    integration = Klenty1lookupIntegration(
        klenty_api_key="your_klenty_api_key",
        lookup_api_key="your_1lookup_api_key"
    )
    
    # Sample prospects for engagement enhancement
    sample_prospects = [
        {
            'email': 'john@example.com',
            'first_name': 'John',
            'last_name': 'Smith',
            'position': 'Sales Director',
            'company': 'Example Corp',
            'linkedin_url': 'https://linkedin.com/in/johnsmith'
        },
        # Add more prospects...
    ]
    
    # Create enhanced engagement cadence
    cadence_result = await integration.create_enhanced_engagement_cadence(
        cadence_name="Enhanced Sales Engagement Q1",
        prospects=sample_prospects,
        cadence_config={
            "multichannel": True,
            "personalization_level": "deep",
            "crm_integration": True
        }
    )
    
    print("Klenty Sales Engagement Enhancement Results:")
    print(f"Cadence ID: {cadence_result.get('cadence_id')}")
    print(f"Total Prospects: {cadence_result.get('total_prospects')}")
    
    engagement_optimization = cadence_result.get('engagement_optimization', {})
    print(f"
Engagement Optimization:")
    print(f"High Engagement Rate: {engagement_optimization.get('high_engagement_rate', 0):.1f}%")
    print(f"Multichannel Ready Rate: {engagement_optimization.get('multichannel_ready_rate', 0):.1f}%")
    print(f"Average Engagement Score: {engagement_optimization.get('average_engagement_score', 0):.1f}")

# Run the integration
if __name__ == "__main__":
    import aiohttp
    asyncio.run(main())