Transform Your Business with Smart AI Automation

# Neural Business Intelligence System
import tensorflow as tf
from neural_optimizer import BusinessProcessor, ModelOrchestrator
from automation_core import WorkflowEngine

# Initialize AI components
processor = BusinessProcessor(learning_rate=0.001)
orchestrator = ModelOrchestrator()
workflow = WorkflowEngine()

# Smart trigger system
def intelligent_trigger(data, context):
    predictions = processor.analyze(data)
    if predictions.confidence > 0.85:
        workflow.execute(predictions.action)
        return "Automation executed successfully"
    return "Waiting for higher confidence"

# Advanced lead scoring with ML
def score_prospects(leads_data):
    features = processor.extract_features(leads_data)
    scores = orchestrator.predict_conversion(features)
    
    for lead, score in zip(leads_data, scores):
        if score > 0.7:
            workflow.prioritize_lead(lead, priority="high")
            notify_sales_team(lead, score)
    
# Real-time model optimization
def continuous_learning(feedback_data):
    model_performance = orchestrator.evaluate(feedback_data)
    
    if model_performance.accuracy < 0.9:
        orchestrator.retrain_model(feedback_data)
        processor.update_parameters()
        
# Customer behavior prediction
def predict_churn(customer_data):
    risk_scores = processor.calculate_churn_risk(customer_data)
    
    for customer, risk in risk_scores.items():
        if risk > 0.6:
            workflow.trigger_retention_campaign(customer)
            
return "AI optimization complete"