Customer Support Agent

Build an intelligent customer support system with BroxiAI for automated ticket handling and resolution

Learn how to create a comprehensive AI-powered customer support system that can handle inquiries, escalate complex issues, and provide 24/7 assistance to your customers.

What You'll Build

A sophisticated customer support system that:

• Handles common customer inquiries automatically

• Accesses knowledge base and documentation

• Escalates complex issues to human agents

• Integrates with ticketing systems

• Provides multi-language support

• Tracks customer satisfaction

Prerequisites

• BroxiAI account with API access

• OpenAI API key or other LLM provider

• Vector database (Pinecone recommended)

• Customer support knowledge base

• Integration access (Zendesk, Freshdesk, etc.)

Architecture Overview

Step 1: Set Up Knowledge Base

Document Preparation

Organize Support Content

Knowledge Base Structure:
  FAQ:
    - Common questions and answers
    - Product information
    - Pricing and billing
    - Technical troubleshooting

  Procedures:
    - Step-by-step guides
    - Installation instructions
    - Configuration tutorials
    - Best practices

  Policies:
    - Return and refund policies
    - Terms of service
    - Privacy policy
    - Service level agreements

Document Processing Pipeline

# Document ingestion workflow
knowledge_base_pipeline = {
    "components": [
        {
            "name": "Document Loader",
            "type": "FileLoader",
            "config": {
                "supported_formats": [".pdf", ".docx", ".md", ".txt"],
                "extract_metadata": True,
                "preserve_structure": True
            }
        },
        {
            "name": "Content Splitter", 
            "type": "RecursiveTextSplitter",
            "config": {
                "chunk_size": 800,
                "chunk_overlap": 100,
                "separators": ["\n\n", "\n", ". ", " "],
                "keep_separator": True
            }
        },
        {
            "name": "Metadata Enrichment",
            "type": "MetadataProcessor",
            "config": {
                "extract_categories": True,
                "add_timestamps": True,
                "source_attribution": True
            }
        },
        {
            "name": "Embeddings",
            "type": "OpenAIEmbeddings",
            "config": {
                "model": "text-embedding-ada-002",
                "batch_size": 1000
            }
        },
        {
            "name": "Vector Store",
            "type": "Pinecone",
            "config": {
                "index_name": "customer-support-kb",
                "namespace": "knowledge_base"
            }
        }
    ]
}

Knowledge Base Enhancement

Add Structured Data

{
  "structured_knowledge": {
    "product_info": {
      "pricing_plans": {
        "basic": {"price": 29, "features": ["Feature A", "Feature B"]},
        "pro": {"price": 99, "features": ["All Basic", "Feature C", "Feature D"]},
        "enterprise": {"price": 299, "features": ["All Pro", "Priority Support"]}
      },
      "integrations": ["Slack", "Teams", "Zendesk", "Salesforce"],
      "supported_languages": ["English", "Spanish", "French", "German"]
    },
    "policies": {
      "refund_period": "30 days",
      "support_hours": "24/7 for Enterprise, 9-5 EST for others",
      "response_time": {
        "enterprise": "1 hour",
        "pro": "4 hours", 
        "basic": "24 hours"
      }
    }
  }
}

Step 2: Build Intent Classification

Intent Detection Component

Create Intent Classifier

intent_classifier_config = {
    "component": "IntentClassifier",
    "config": {
        "model": "gpt-3.5-turbo",
        "temperature": 0.1,
        "system_prompt": """
You are an intent classifier for customer support. Classify the customer's message into one of these categories:

CATEGORIES:
- billing: Questions about payments, invoices, pricing, refunds
- technical: Technical issues, bugs, troubleshooting, how-to questions
- account: Account management, password reset, profile changes
- general: General inquiries, product information, features
- complaint: Complaints, negative feedback, service issues
- escalation: Requests to speak with human agent, complex issues

Respond with only the category name.
        """,
        "max_tokens": 50
    }
}

Confidence Scoring

confidence_scorer_config = {
    "component": "ConfidenceScorer",
    "config": {
        "model": "gpt-3.5-turbo",
        "temperature": 0.0,
        "system_prompt": """
Rate your confidence in handling this customer support request on a scale of 1-10:

1-3: Low confidence - Complex issue requiring human intervention
4-6: Medium confidence - Can provide basic help but may need escalation
7-10: High confidence - Can fully resolve the issue

Consider factors:
- Clarity of the request
- Availability of relevant information
- Complexity of the issue
- Emotional tone (frustrated customers may need human touch)

Respond with only a number from 1-10.
        """,
        "max_tokens": 10
    }
}

Step 3: Create Specialized Support Agents

General Support Agent

Main Support Agent Configuration

general_support_agent = {
    "component": "SupportAgent",
    "config": {
        "model": "gpt-4",
        "temperature": 0.3,
        "system_prompt": """
You are a helpful and empathetic customer support agent. Your goal is to:

1. Understand the customer's issue clearly
2. Provide accurate, helpful information
3. Be friendly and professional
4. Escalate when necessary

PERSONALITY:
- Patient and understanding
- Clear and concise communication
- Proactive in offering solutions
- Empathetic to customer frustrations

GUIDELINES:
- Always acknowledge the customer's concern
- Use the knowledge base to provide accurate information
- If you're unsure, say so and offer to escalate
- End with asking if there's anything else you can help with

ESCALATION TRIGGERS:
- Billing disputes over $100
- Technical issues requiring account access
- Angry or very frustrated customers
- Requests outside your knowledge base
        """,
        "max_tokens": 1000,
        "tools": [
            "knowledge_base_search",
            "policy_lookup",
            "escalation_trigger"
        ]
    }
}

Technical Support Agent

Technical Specialist Configuration

technical_support_agent = {
    "component": "TechnicalAgent",
    "config": {
        "model": "gpt-4",
        "temperature": 0.2,
        "system_prompt": """
You are a technical support specialist. You excel at:

1. Diagnosing technical problems
2. Providing step-by-step solutions
3. Explaining complex concepts simply
4. Troubleshooting systematically

APPROACH:
- Ask clarifying questions to understand the issue
- Provide specific, actionable steps
- Explain what each step does
- Offer alternative solutions
- Follow up to ensure resolution

TECHNICAL AREAS:
- API integration issues
- Authentication problems
- Performance optimization
- Configuration errors
- Browser compatibility
- Mobile app issues

Always provide code examples when relevant and explain technical concepts in simple terms.
        """,
        "max_tokens": 1500,
        "tools": [
            "knowledge_base_search",
            "code_generator",
            "diagnostic_tools"
        ]
    }
}

Billing Support Agent

Billing Specialist Configuration

billing_support_agent = {
    "component": "BillingAgent",
    "config": {
        "model": "gpt-3.5-turbo",
        "temperature": 0.1,
        "system_prompt": """
You are a billing support specialist. You handle:

1. Billing questions and disputes
2. Payment issues
3. Plan changes and upgrades
4. Refund requests
5. Invoice explanations

SECURITY GUIDELINES:
- Never provide specific account details
- Always verify customer identity before discussing billing
- For disputes over $100, escalate to human agent
- Follow company refund policy strictly

AVAILABLE ACTIONS:
- Explain billing cycles and charges
- Provide general pricing information
- Initiate billing disputes (with approval)
- Schedule payment plan consultations
- Process basic refund requests (under policy limits)

Be empathetic about billing concerns while following company policies.
        """,
        "max_tokens": 800,
        "tools": [
            "billing_policy_lookup",
            "payment_verification",
            "refund_processor"
        ]
    }
}

Step 4: Implement Knowledge Base Search

Vector Search Configuration

Search Component Setup

knowledge_base_search = {
    "component": "VectorSearch",
    "config": {
        "vector_store": "pinecone",
        "index_name": "customer-support-kb",
        "top_k": 5,
        "similarity_threshold": 0.75,
        "metadata_filters": {
            "status": "active",
            "language": "en"
        },
        "rerank": True,
        "rerank_model": "cross-encoder"
    }
}

Hybrid Search Implementation

hybrid_search_config = {
    "component": "HybridSearch",
    "config": {
        "vector_weight": 0.7,
        "keyword_weight": 0.3,
        "vector_search": {
            "model": "text-embedding-ada-002",
            "top_k": 10
        },
        "keyword_search": {
            "algorithm": "bm25",
            "top_k": 10
        },
        "fusion_method": "reciprocal_rank_fusion"
    }
}

Context Enhancement

Context Aggregation

context_enhancer = {
    "component": "ContextEnhancer",
    "config": {
        "max_context_length": 3000,
        "prioritization": "relevance_score",
        "include_metadata": True,
        "context_template": """
RELEVANT KNOWLEDGE:
{context_chunks}

CUSTOMER INFORMATION:
- Support Plan: {customer_plan}
- Account Status: {account_status}
- Previous Interactions: {interaction_count}

COMPANY POLICIES:
{relevant_policies}
        """
    }
}

Step 5: Build Escalation Logic

Escalation Decision Engine

Escalation Rules

escalation_rules = {
    "component": "EscalationDecision",
    "config": {
        "model": "gpt-3.5-turbo",
        "temperature": 0.0,
        "system_prompt": """
Determine if this customer support case should be escalated to a human agent.

ESCALATION CRITERIA:
1. Customer explicitly requests human agent
2. Issue involves billing disputes over $100
3. Technical issues requiring account-level access
4. Complaint with legal implications
5. Confidence score below 4
6. Customer expresses high frustration/anger
7. Issue is outside knowledge base scope
8. Security or privacy concerns

ESCALATION LEVELS:
- IMMEDIATE: Security, legal, or very angry customers
- HIGH: Billing disputes, technical issues, confident score < 3
- NORMAL: General requests for human agent, confidence score 3-4
- NO_ESCALATION: Can be handled by AI

Respond with: IMMEDIATE, HIGH, NORMAL, or NO_ESCALATION
        """,
        "max_tokens": 50
    }
}

Escalation Workflow

def create_escalation_workflow():
    return {
        "trigger_conditions": [
            "escalation_decision != 'NO_ESCALATION'",
            "customer_satisfaction_score < 3",
            "interaction_count > 5"
        ],
        "escalation_actions": {
            "IMMEDIATE": {
                "priority": "urgent",
                "notify_manager": True,
                "max_response_time": "15_minutes"
            },
            "HIGH": {
                "priority": "high", 
                "assign_to": "senior_agent",
                "max_response_time": "1_hour"
            },
            "NORMAL": {
                "priority": "normal",
                "assign_to": "next_available",
                "max_response_time": "4_hours"
            }
        }
    }

Step 6: Add Multi-Language Support

Language Detection

Language Detection Component

language_detector = {
    "component": "LanguageDetector",
    "config": {
        "detection_model": "langdetect",
        "supported_languages": [
            "en", "es", "fr", "de", "it", "pt", "zh", "ja"
        ],
        "confidence_threshold": 0.8,
        "fallback_language": "en"
    }
}

Translation Service

translation_service = {
    "component": "TranslationService",
    "config": {
        "provider": "google_translate",
        "api_key": "${GOOGLE_TRANSLATE_API_KEY}",
        "preserve_formatting": True,
        "cache_translations": True,
        "quality_check": True
    }
}

Multilingual Responses

Language-Specific Agents

multilingual_agent_config = {
    "component": "MultilingualAgent",
    "config": {
        "base_model": "gpt-4",
        "temperature": 0.3,
        "language_prompts": {
            "es": "Responde en español de manera profesional y empática...",
            "fr": "Répondez en français de manière professionnelle et empathique...",
            "de": "Antworten Sie auf Deutsch professionell und einfühlsam...",
            "zh": "请用中文专业且富有同理心地回应..."
        },
        "cultural_considerations": {
            "es": "Use formal 'usted' unless customer uses informal tone",
            "fr": "Use formal 'vous' in professional context",
            "de": "Maintain formal 'Sie' throughout interaction",
            "zh": "Use respectful and polite language patterns"
        }
    }
}

Step 7: Integration with Support Systems

Ticketing System Integration

Zendesk Integration

zendesk_integration = {
    "component": "ZendeskConnector",
    "config": {
        "subdomain": "yourcompany",
        "email": "[e-mail]",
        "api_token": "${ZENDESK_API_TOKEN}",
        "default_assignee": "[e-mail]",
        "custom_fields": {
            "ai_handled": True,
            "confidence_score": "{{confidence_score}}",
            "escalation_reason": "{{escalation_reason}}"
        }
    }
}

Ticket Creation Workflow

def create_support_ticket(customer_data, issue_data, escalation_level):
    ticket_data = {
        "subject": f"[{escalation_level}] {issue_data['category']}: {issue_data['summary']}",
        "description": f"""
Customer Inquiry: {issue_data['original_message']}

AI Analysis:
- Category: {issue_data['category']}
- Confidence Score: {issue_data['confidence_score']}
- Escalation Reason: {issue_data['escalation_reason']}

Customer Information:
- Email: {customer_data['email']}
- Support Plan: {customer_data['plan']}
- Previous Interactions: {customer_data['interaction_count']}

Conversation History:
{issue_data['conversation_history']}
        """,
        "requester": {
            "name": customer_data['name'],
            "email": customer_data['email']
        },
        "priority": escalation_level.lower(),
        "tags": [
            "ai-escalated",
            issue_data['category'],
            f"confidence-{issue_data['confidence_score']}"
        ]
    }
    
    return zendesk_client.tickets.create(ticket_data)

CRM Integration

Salesforce Integration

salesforce_integration = {
    "component": "SalesforceConnector",
    "config": {
        "username": "${SALESFORCE_USERNAME}",
        "password": "${SALESFORCE_PASSWORD}",
        "security_token": "${SALESFORCE_SECURITY_TOKEN}",
        "domain": "login",  # or "test" for sandbox
        "api_version": "58.0"
    }
}

Customer Data Enrichment

def enrich_customer_data(email):
    """Fetch customer data from CRM"""
    
    customer_query = f"""
    SELECT Id, Name, Email, Account.Name, Account.Type, 
           Support_Plan__c, Total_Interactions__c, 
           Last_Interaction_Date__c, Customer_Health_Score__c
    FROM Contact 
    WHERE Email = '{email}'
    """
    
    result = salesforce_client.query(customer_query)
    
    if result['totalSize'] > 0:
        contact = result['records'][0]
        return {
            "customer_id": contact['Id'],
            "name": contact['Name'],
            "email": contact['Email'],
            "company": contact['Account']['Name'],
            "account_type": contact['Account']['Type'],
            "support_plan": contact['Support_Plan__c'],
            "interaction_count": contact['Total_Interactions__c'],
            "last_interaction": contact['Last_Interaction_Date__c'],
            "health_score": contact['Customer_Health_Score__c']
        }
    
    return None

Step 8: Implement Quality Monitoring

Conversation Analysis

Quality Assessment Component

quality_monitor = {
    "component": "QualityMonitor",
    "config": {
        "model": "gpt-4",
        "temperature": 0.1,
        "assessment_criteria": {
            "accuracy": "Was the information provided accurate?",
            "helpfulness": "Was the response helpful in resolving the issue?",
            "empathy": "Did the agent show empathy and understanding?",
            "professionalism": "Was the response professional and appropriate?",
            "completeness": "Was the issue fully addressed?"
        },
        "scoring_scale": "1-10",
        "minimum_acceptable_score": 7
    }
}

Sentiment Analysis

sentiment_analyzer = {
    "component": "SentimentAnalyzer",
    "config": {
        "model": "distilbert-base-uncased-finetuned-sst-2-english",
        "track_conversation_flow": True,
        "detect_emotion_changes": True,
        "alert_on_negative_trend": True,
        "sentiment_categories": [
            "very_negative", "negative", "neutral", "positive", "very_positive"
        ]
    }
}

Feedback Collection

Customer Satisfaction Survey

satisfaction_survey = {
    "component": "SatisfactionSurvey",
    "config": {
        "trigger_conditions": [
            "conversation_ended",
            "issue_resolved",
            "escalation_completed"
        ],
        "survey_questions": [
            {
                "question": "How satisfied are you with the support you received?",
                "type": "rating",
                "scale": "1-5"
            },
            {
                "question": "Was your issue resolved?",
                "type": "boolean"
            },
            {
                "question": "Any additional feedback?",
                "type": "text",
                "optional": True
            }
        ],
        "response_timeout": "24_hours"
    }
}

Step 9: Advanced Features

Predictive Support

Issue Prediction Model

predictive_support = {
    "component": "IssuePrediction",
    "config": {
        "model": "custom_ml_model",
        "prediction_factors": [
            "customer_usage_patterns",
            "recent_feature_changes",
            "historical_support_data",
            "customer_health_score"
        ],
        "prediction_horizon": "7_days",
        "confidence_threshold": 0.8,
        "proactive_outreach": True
    }
}

Proactive Support Workflow

def proactive_support_workflow():
    """Identify customers likely to need support and reach out proactively"""
    
    # Identify at-risk customers
    at_risk_customers = predictive_model.predict_support_needs()
    
    for customer in at_risk_customers:
        if customer['risk_score'] > 0.8:
            # Send proactive support message
            proactive_message = f"""
            Hi {customer['name']},
            
            We noticed you might be experiencing issues with {customer['predicted_issue_area']}. 
            Our support team is here to help!
            
            Common solutions for {customer['predicted_issue_area']}:
            {get_common_solutions(customer['predicted_issue_area'])}
            
            If you need further assistance, just reply to this message or visit our support portal.
            
            Best regards,
            The Support Team
            """
            
            send_proactive_support(customer['email'], proactive_message)

Self-Service Enhancement

Dynamic FAQ Generation

dynamic_faq = {
    "component": "DynamicFAQ",
    "config": {
        "update_frequency": "daily",
        "trending_threshold": 5,  # 5+ similar questions in 24h
        "auto_generate_answers": True,
        "human_review_required": True,
        "categories": [
            "billing", "technical", "account", "features"
        ]
    }
}

Smart Search Enhancement

smart_search = {
    "component": "SmartSearch",
    "config": {
        "search_suggestions": True,
        "auto_complete": True,
        "typo_correction": True,
        "semantic_search": True,
        "personalized_results": True,
        "search_analytics": True
    }
}

Step 10: Performance Monitoring

Key Metrics Dashboard

Support Metrics

Support KPIs:
  Efficiency:
    - First Contact Resolution Rate: >80%
    - Average Resolution Time: <4 hours
    - Escalation Rate: <15%
    - Self-Service Usage: >60%

  Quality:
    - Customer Satisfaction Score: >4.5/5
    - Quality Score: >85%
    - Accuracy Rate: >95%
    - Sentiment Improvement: Positive trend

  Volume:
    - Total Inquiries: Track daily/weekly trends
    - Channel Distribution: Email, chat, phone
    - Peak Hours: Identify staffing needs
    - Seasonal Patterns: Plan for fluctuations

Real-Time Monitoring

class SupportMetricsMonitor:
    def __init__(self):
        self.metrics = {
            "active_conversations": 0,
            "average_wait_time": 0,
            "resolution_rate": 0,
            "satisfaction_score": 0,
            "escalation_rate": 0
        }
    
    def update_metrics(self):
        """Update real-time support metrics"""
        
        # Get current metrics from database
        current_metrics = self.fetch_current_metrics()
        
        # Calculate derived metrics
        self.metrics.update({
            "first_contact_resolution": self.calculate_fcr(),
            "agent_utilization": self.calculate_agent_utilization(),
            "queue_health": self.assess_queue_health(),
            "trend_analysis": self.analyze_trends()
        })
        
        # Send alerts if thresholds exceeded
        self.check_alert_conditions()
        
        return self.metrics
    
    def check_alert_conditions(self):
        """Check for alert conditions"""
        
        if self.metrics["average_wait_time"] > 300:  # 5 minutes
            self.send_alert("High wait times detected")
        
        if self.metrics["escalation_rate"] > 0.20:  # 20%
            self.send_alert("High escalation rate")
        
        if self.metrics["satisfaction_score"] < 4.0:
            self.send_alert("Low satisfaction scores")

Testing and Optimization

A/B Testing Framework

Response Variation Testing

ab_testing_config = {
    "component": "ABTesting",
    "config": {
        "test_groups": {
            "control": {
                "percentage": 50,
                "agent_config": "standard_support_agent"
            },
            "variant_a": {
                "percentage": 25,
                "agent_config": "empathy_enhanced_agent"
            },
            "variant_b": {
                "percentage": 25,
                "agent_config": "efficiency_optimized_agent"
            }
        },
        "success_metrics": [
            "customer_satisfaction",
            "resolution_time",
            "escalation_rate"
        ],
        "test_duration": "14_days",
        "statistical_significance": 0.95
    }
}

Continuous Improvement

Learning Loop Implementation

def continuous_improvement_loop():
    """Implement continuous learning and improvement"""
    
    # Analyze recent interactions
    recent_data = analyze_recent_interactions()
    
    # Identify improvement opportunities
    improvements = identify_improvements(recent_data)
    
    # Update knowledge base
    if improvements['knowledge_gaps']:
        update_knowledge_base(improvements['knowledge_gaps'])
    
    # Retrain models if needed
    if improvements['model_performance'] < 0.85:
        retrain_classification_models()
    
    # Update agent prompts
    if improvements['response_quality'] < 0.90:
        optimize_agent_prompts(improvements['response_feedback'])
    
    # Generate improvement report
    return generate_improvement_report(improvements)

Deployment and Integration

Production Deployment

Gradual Rollout Strategy

Rollout Plan:
  Phase 1 (Week 1):
    - Traffic: 10%
    - Features: Basic FAQ and simple inquiries
    - Monitoring: Enhanced logging and metrics
  
  Phase 2 (Week 2-3):
    - Traffic: 30%
    - Features: Add technical support and billing
    - Monitoring: A/B testing implementation
  
  Phase 3 (Week 4-5):
    - Traffic: 60%
    - Features: Full feature set except complex cases
    - Monitoring: Quality assurance and optimization
  
  Phase 4 (Week 6+):
    - Traffic: 80%+
    - Features: Complete system with human backup
    - Monitoring: Continuous improvement and learning

Integration Checklist

• Knowledge base fully populated and tested

• All escalation pathways configured

• Quality monitoring systems active

• Multi-language support tested

• Ticketing system integration verified

• Customer satisfaction surveys configured

• Performance monitoring dashboards ready

• Staff training on AI-human handoff procedures

Best Practices

Customer Experience

• Always acknowledge customer emotions

• Provide clear, actionable solutions

• Set proper expectations for response times

• Follow up on escalated issues

• Continuously gather and act on feedback

Technical Excellence

• Maintain high accuracy in responses

• Optimize for speed without sacrificing quality

• Regular model updates and retraining

• Robust error handling and fallbacks

• Comprehensive testing of all scenarios

Business Impact

• Monitor cost savings from automation

• Track customer satisfaction improvements

• Measure agent productivity gains

• Analyze resolution time improvements

• Calculate ROI on AI implementation

Next Steps

After implementing your customer support agent:

1. Monitor Performance: Track key metrics and KPIs

2. Gather Feedback: Collect input from customers and agents

3. Iterate and Improve: Continuously enhance the system

4. Scale Gradually: Expand to handle more complex scenarios

5. Train Staff: Ensure smooth AI-human collaboration

Related Examples

• Document Q&A: Enhance knowledge base search

• Sales Assistant: Cross-functional AI agents

• Content Generation: Automated response creation

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You've built a comprehensive AI-powered customer support system! This foundation can handle the majority of customer inquiries while seamlessly escalating complex issues to human agents when needed.