Our support team was drowning. 10K tickets/day, 24-hour response time, customers frustrated. I built an AI support system.

Results: 30-second response, 85% automation, $500K saved. Here’s how.

Table of Contents

The Problem

Before AI:

  • 10,000 tickets/day
  • 24-hour average response time
  • 50 support agents
  • $1.2M annual cost
  • Customer satisfaction: 3.2/5

Pain Points:

  • Repetitive questions (70%)
  • Slow escalation
  • Inconsistent answers
  • 24/7 coverage expensive

Solution Architecture

class AICustomerSupport:
    def __init__(self):
        self.llm = OpenAI(model="gpt-4")
        self.knowledge_base = VectorStore()
        self.ticket_system = TicketSystem()
        self.escalation_rules = EscalationRules()
    
    async def handle_ticket(self, ticket):
        """Main ticket handling flow."""
        # 1. Classify ticket
        classification = await self._classify(ticket)
        
        # 2. Check if can auto-resolve
        if classification['confidence'] > 0.8 and classification['type'] in self.auto_resolve_types:
            return await self._auto_resolve(ticket, classification)
        
        # 3. Generate suggested response for agent
        elif classification['confidence'] > 0.6:
            return await self._assist_agent(ticket, classification)
        
        # 4. Escalate complex issues
        else:
            return await self._escalate(ticket, classification)
    
    async def _classify(self, ticket):
        """Classify ticket type and urgency."""
        prompt = f"""
Classify this customer support ticket:

Subject: {ticket['subject']}
Message: {ticket['message']}

Classify:
1. Type: billing, technical, account, shipping, other
2. Urgency: low, medium, high, critical
3. Sentiment: positive, neutral, negative, angry
4. Can auto-resolve: yes/no
5. Confidence: 0-1

Return JSON.
"""
        
        response = await self.llm.apredict(prompt)
        return json.loads(response)
    
    async def _auto_resolve(self, ticket, classification):
        """Automatically resolve ticket."""
        # Search knowledge base
        relevant_docs = self.knowledge_base.search(ticket['message'], k=3)
        
        # Generate response
        response = await self._generate_response(ticket, relevant_docs)
        
        # Send to customer
        await self._send_response(ticket['id'], response)
        
        # Close ticket
        await self.ticket_system.close(ticket['id'], resolution=response)
        
        return {
            'status': 'auto_resolved',
            'response': response,
            'time': '30s'
        }
    
    async def _generate_response(self, ticket, context):
        """Generate customer response."""
        prompt = f"""
You are a helpful customer support agent.

Customer question:
{ticket['message']}

Relevant information:
{context}

Provide a helpful, friendly response that:
1. Addresses the customer's question
2. Is clear and concise
3. Includes specific steps if applicable
4. Offers additional help

Tone: Professional but friendly
"""
        
        return await self.llm.apredict(prompt)

Knowledge Base

from langchain.vectorstores import Pinecone
from langchain.embeddings import OpenAIEmbeddings

class KnowledgeBase:
    def __init__(self):
        self.embeddings = OpenAIEmbeddings()
        self.vectorstore = Pinecone.from_existing_index(
            index_name="support-kb",
            embedding=self.embeddings
        )
    
    def add_document(self, doc):
        """Add document to knowledge base."""
        self.vectorstore.add_texts(
            texts=[doc['content']],
            metadatas=[{
                'title': doc['title'],
                'category': doc['category'],
                'last_updated': doc['updated']
            }]
        )
    
    def search(self, query, k=3):
        """Search for relevant documents."""
        results = self.vectorstore.similarity_search_with_score(query, k=k)
        
        return [{
            'content': doc.page_content,
            'metadata': doc.metadata,
            'relevance': score
        } for doc, score in results]
    
    def update_from_resolved_tickets(self):
        """Learn from resolved tickets."""
        # Get recently resolved tickets
        resolved = self.ticket_system.get_resolved(days=7)
        
        # Extract common patterns
        for ticket in resolved:
            if ticket['rating'] >= 4:  # High satisfaction
                # Add to knowledge base
                self.add_document({
                    'title': ticket['subject'],
                    'content': f"Q: {ticket['question']}\nA: {ticket['resolution']}",
                    'category': ticket['category'],
                    'updated': datetime.now()
                })

Ticket Classification

class TicketClassifier:
    def __init__(self):
        self.llm = OpenAI(model="gpt-4")
        self.categories = [
            'billing', 'technical', 'account', 
            'shipping', 'product', 'refund'
        ]
    
    async def classify(self, ticket):
        """Classify ticket with high accuracy."""
        # Use few-shot learning
        examples = self._get_classification_examples()
        
        prompt = f"""
Classify customer support tickets.

Examples:
{examples}

New ticket:
Subject: {ticket['subject']}
Message: {ticket['message']}

Classification:
"""
        
        response = await self.llm.apredict(prompt)
        classification = json.loads(response)
        
        # Validate
        if classification['confidence'] < 0.6:
            # Use ensemble approach
            classification = await self._ensemble_classify(ticket)
        
        return classification
    
    def _get_classification_examples(self):
        """Get few-shot examples."""
        return """
1. "My payment failed" → billing, high urgency, 0.95 confidence
2. "How do I reset password?" → account, low urgency, 0.98 confidence
3. "Product not working" → technical, medium urgency, 0.90 confidence
"""
    
    async def _ensemble_classify(self, ticket):
        """Use multiple models for better accuracy."""
        # Get classifications from multiple models
        gpt4_result = await self._classify_with_model("gpt-4", ticket)
        claude_result = await self._classify_with_model("claude-3", ticket)
        
        # Combine results
        if gpt4_result['category'] == claude_result['category']:
            return gpt4_result  # Agreement
        else:
            # Use highest confidence
            return max([gpt4_result, claude_result], key=lambda x: x['confidence'])

Auto-Resolution

class AutoResolver:
    def __init__(self, knowledge_base):
        self.kb = knowledge_base
        self.llm = OpenAI(model="gpt-4")
        self.auto_resolve_types = ['password_reset', 'order_status', 'faq']
    
    async def can_auto_resolve(self, ticket, classification):
        """Determine if ticket can be auto-resolved."""
        return (
            classification['type'] in self.auto_resolve_types and
            classification['confidence'] > 0.85 and
            classification['urgency'] != 'critical'
        )
    
    async def resolve(self, ticket):
        """Auto-resolve ticket."""
        # Get relevant information
        context = self.kb.search(ticket['message'], k=5)
        
        # Generate resolution
        resolution = await self._generate_resolution(ticket, context)
        
        # Verify quality
        if await self._verify_quality(resolution):
            return resolution
        else:
            # Escalate if quality is poor
            return None
    
    async def _generate_resolution(self, ticket, context):
        """Generate resolution with context."""
        prompt = f"""
Resolve this customer support ticket:

Ticket: {ticket['message']}

Relevant information:
{self._format_context(context)}

Provide:
1. Direct answer to the question
2. Step-by-step instructions if applicable
3. Links to relevant resources
4. Offer for additional help

Be helpful, clear, and concise.
"""
        
        return await self.llm.apredict(prompt)
    
    async def _verify_quality(self, resolution):
        """Verify resolution quality."""
        prompt = f"""
Rate this customer support response quality (0-10):

Response: {resolution}

Criteria:
- Addresses the question
- Clear and actionable
- Professional tone
- Complete information

Score:
"""
        
        score = await self.llm.apredict(prompt)
        return int(score) >= 8

Agent Assistance

class AgentAssistant:
    def __init__(self, knowledge_base):
        self.kb = knowledge_base
        self.llm = OpenAI(model="gpt-4")
    
    async def assist(self, ticket):
        """Provide suggested response for human agent."""
        # Search knowledge base
        context = self.kb.search(ticket['message'], k=5)
        
        # Generate suggested response
        suggestion = await self._generate_suggestion(ticket, context)
        
        # Find similar past tickets
        similar = await self._find_similar_tickets(ticket)
        
        return {
            'suggested_response': suggestion,
            'similar_tickets': similar,
            'relevant_docs': context,
            'confidence': 0.75
        }
    
    async def _generate_suggestion(self, ticket, context):
        """Generate suggested response."""
        prompt = f"""
Generate a suggested response for this customer support ticket:

Ticket: {ticket['message']}

Context: {context}

The response will be reviewed by a human agent before sending.
Provide a helpful, professional response.
"""
        
        return await self.llm.apredict(prompt)
    
    async def _find_similar_tickets(self, ticket):
        """Find similar resolved tickets."""
        # Use embeddings to find similar tickets
        similar = self.kb.vectorstore.similarity_search(
            ticket['message'],
            k=3,
            filter={'status': 'resolved', 'rating': {'$gte': 4}}
        )
        
        return similar

Escalation System

class EscalationSystem:
    def __init__(self):
        self.rules = self._load_escalation_rules()
        self.agents = self._load_agent_availability()
    
    async def escalate(self, ticket, classification):
        """Escalate ticket to appropriate agent."""
        # Determine escalation level
        level = self._get_escalation_level(classification)
        
        # Find available agent
        agent = await self._find_agent(level, classification['type'])
        
        # Assign ticket
        await self._assign_ticket(ticket, agent)
        
        # Notify agent
        await self._notify_agent(agent, ticket, classification)
        
        return {
            'status': 'escalated',
            'agent': agent['name'],
            'level': level
        }
    
    def _get_escalation_level(self, classification):
        """Determine escalation level."""
        if classification['urgency'] == 'critical':
            return 'senior'
        elif classification['sentiment'] == 'angry':
            return 'experienced'
        else:
            return 'standard'
    
    async def _find_agent(self, level, category):
        """Find best available agent."""
        available_agents = [
            a for a in self.agents 
            if a['level'] == level and 
               category in a['specialties'] and
               a['available']
        ]
        
        # Sort by current workload
        available_agents.sort(key=lambda x: x['current_tickets'])
        
        return available_agents[0] if available_agents else None

Results

After 6 Months:

MetricBeforeAfterImprovement
Response Time24h30s99.9%
Resolution Time48h2h95.8%
Automation Rate0%85%+85%
Customer Satisfaction3.2/54.7/5+47%
Support Agents5015-70%
Annual Cost$1.2M$700K-42%

Breakdown:

  • Auto-resolved: 85% (8,500/day)
  • Agent-assisted: 12% (1,200/day)
  • Escalated: 3% (300/day)

Cost Savings:

  • Agent reduction: $500K/year
  • Faster resolution: $200K/year (customer retention)
  • AI costs: -$200K/year
  • Net savings: $500K/year

Lessons Learned

  1. Knowledge base is critical: Quality in = quality out
  2. Don’t aim for 100% automation: 85% is optimal
  3. Human oversight essential: For complex/sensitive issues
  4. Continuous learning: Update KB from resolved tickets
  5. Monitor quality: Not just speed

Conclusion

AI customer support works. 85% automation, 30-second response, $500K saved.

Key takeaways:

  1. 99.9% faster response (24h → 30s)
  2. 85% automation rate
  3. Customer satisfaction +47%
  4. $500K annual savings
  5. Human agents for complex issues

Automate support. Keep customers happy.