AI Customer Support: Reducing Tickets with RAG

Customer support often represents 15-25% of a company's operational costs. A Gartner study reveals that 70% of tier-1 tickets concern repetitive questions whose answers already exist in documentation. RAG transforms this reality by automating responses while preserving service quality.

The Hidden Cost of Traditional Support

Anatomy of a Support Ticket

Each ticket follows an expensive cycle:

| Stage | Average Time | Estimated Cost | |-------|--------------|----------------| | Receipt and triage | 2-5 min | $3 | | Solution search | 5-15 min | $13 | | Response drafting | 3-8 min | $7 | | Follow-up and closure | 2-3 min | $3 | | Total | 12-31 min | $26 |

Multiply by thousands of monthly tickets, and support becomes a financial drain.

Questions That Keep Coming Back

Analyze your tickets: you'll find that 60-80% concern: • Where is my order? • How do I return a product? • What are your hours? • How do I modify my subscription? • Forgot password • Standard payment issues

These questions have clear, documented answers. Why mobilize human agents?

RAG Architecture for Customer Support

`` ┌─────────────────────────────────────────────────────────────┐ │ INPUT CHANNELS │ ├──────────┬──────────┬──────────┬──────────┬────────────────┤ │ Widget │ Email │ Live │ Slack │ External │ │ Web │ Parser │ Chat │ Bot │ API │ └────┬─────┴────┬─────┴────┬─────┴────┬─────┴───────┬────────┘ │ │ │ │ │ └──────────┴──────────┴──────────┴─────────────┘ │ ▼ ┌─────────────────────────┐ │ Classification │ │ Intent Detection │ └───────────┬─────────────┘ │ ┌────────────────┼────────────────┐ ▼ ▼ ▼ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ RAG │ │ Action │ │ Escalate│ │Response │ │ API │ │ Human │ └────┬────┘ └────┬────┘ └────┬────┘ │ │ │ └───────────────┴───────────────┘ │ ▼ ┌─────────────────────────┐ │ Customer Response │ └─────────────────────────┘ `

Three Response Modes Pure RAG Response: Factual questions whose answer is in the knowledge base. API Action: Requests requiring a system action (order tracking, account modification). Human Escalation: Complex cases, complaints, emotional situations.

Building the Support Knowledge Base

Sources to Index

`python SUPPORT_SOURCES = { "faq": { "path": "/docs/faq", "priority": "high", "update_frequency": "daily" }, "procedures": { "path": "/docs/procedures", "priority": "high", "update_frequency": "weekly" }, "product_docs": { "path": "/docs/products", "priority": "medium", "update_frequency": "on_change" }, "past_tickets": { "path": "/tickets/resolved", "priority": "medium", "update_frequency": "daily", "filter": "satisfaction >= 4" }, "policies": { "path": "/docs/policies", "priority": "high", "update_frequency": "on_change" } } `

Optimal FAQ Article Structure

`markdown How do I return a product?

Short answer You have 30 days to return an unused product in its original packaging. Log in to your account > My Orders > Request a Return.

Conditions • Unused product in original packaging • Deadline: 30 days after receipt • Exceptions: personalized products, hygiene, food items

Detailed procedure Log in to your account Go to "My Orders" Select the relevant order Click "Request a Return" Select items to return Choose the reason Print the prepaid label

Refund timeline • Return validation: 2-3 business days after receipt • Refund: 5-7 business days to original payment method

Related questions • How do I track my return? • Who pays return shipping? • Can I exchange instead of return?

--- Tags: return, refund, exchange, send back Last updated: 2024-01-15 `

This structure allows RAG to quickly find the short answer while having access to details if needed.

Intent Detection and Routing

Classifying Requests

`python from enum import Enum from pydantic import BaseModel

class IntentType(Enum): INFORMATION = "information" RAG response TRANSACTION = "transaction" API action COMPLAINT = "complaint" Priority escalation TECHNICAL = "technical" Technical support URGENT = "urgent" Immediate escalation

class IntentClassifier: def __init__(self, llm): self.llm = llm

async def classify(self, message: str, context: dict = None) -> dict: prompt = f""" Analyze this customer message and determine: Main intent (information, transaction, complaint, technical, urgent) Priority level (low, medium, high, critical) Detected emotion (neutral, frustrated, angry, satisfied) Extracted entities (order number, product, date, etc.)

Message: {message} Customer context: {context}

Respond in JSON. """

response = await self.llm.generate(prompt, temperature=0) return self._parse_classification(response)

def _parse_classification(self, response: str) -> dict: Parse and validate JSON response import json try: return json.loads(response) except: return { "intent": IntentType.INFORMATION, "priority": "medium", "emotion": "neutral", "entities": {} } `

Intelligent Routing

`python class SupportRouter: def __init__(self, rag_service, action_service, escalation_service): self.rag = rag_service self.actions = action_service self.escalation = escalation_service

async def route(self, message: str, classification: dict) -> dict: intent = classification["intent"] priority = classification["priority"] emotion = classification["emotion"]

Immediate escalation for critical cases if priority == "critical" or emotion == "angry": return await self.escalation.create_ticket( message=message, classification=classification, priority="high" )

Complaints: always escalate if intent == IntentType.COMPLAINT: return await self.escalation.create_ticket( message=message, classification=classification, priority="medium" )

Transactions: check if action is possible if intent == IntentType.TRANSACTION: action_result = await self.actions.try_action( message=message, entities=classification.get("entities", {}) ) if action_result["success"]: return action_result Otherwise, fallback to RAG

Default: RAG response return await self.rag.answer( query=message, context=classification ) `

Response Personalization

Adapting to Customer Profile

`python class PersonalizedResponder: def __init__(self, llm, customer_service): self.llm = llm self.customers = customer_service

async def generate_response( self, query: str, rag_context: str, customer_id: str ) -> str: Get customer profile profile = await self.customers.get_profile(customer_id)

Adapt tone and detail level tone = self._determine_tone(profile)

prompt = f""" You are a support assistant for [Company].

CUSTOMER PROFILE: • Tenure: {profile.get('tenure', 'new')} • Value: {profile.get('value_segment', 'standard')} • History: {profile.get('ticket_count', 0)} previous tickets • Preferred language: {profile.get('language', 'en')}

TONE TO ADOPT: {tone}

RELEVANT DOCUMENTATION: {rag_context}

CUSTOMER QUESTION: {query}

Generate a response: • Direct and actionable • Adapted to customer profile • With useful links if relevant • Maximum 3 paragraphs """

return await self.llm.generate(prompt, temperature=0.3)

def _determine_tone(self, profile: dict) -> str: if profile.get('value_segment') == 'vip': return "Premium and attentive, offer proactive solutions" elif profile.get('ticket_count', 0) > 10: return "Efficient and direct, avoid basic explanations" elif profile.get('tenure') == 'new': return "Welcoming and educational, explain steps in detail" return "Professional and warm" `

Conversation Context Management

`python class ConversationManager: def __init__(self, redis_client): self.redis = redis_client self.max_history = 10

async def add_message(self, conversation_id: str, role: str, content: str): key = f"conversation:{conversation_id}"

message = { "role": role, "content": content, "timestamp": datetime.now().isoformat() }

await self.redis.rpush(key, json.dumps(message)) await self.redis.ltrim(key, -self.max_history, -1) await self.redis.expire(key, 3600 24) 24h TTL

async def get_context(self, conversation_id: str) -> list: key = f"conversation:{conversation_id}" messages = await self.redis.lrange(key, 0, -1) return [json.loads(m) for m in messages]

async def build_prompt_context(self, conversation_id: str) -> str: history = await self.get_context(conversation_id)

context_parts = [] for msg in history[-5:]: Last 5 messages role = "Customer" if msg["role"] == "user" else "Assistant" context_parts.append(f"{role}: {msg['content']}")

return "\n".join(context_parts) `

Metrics and Continuous Improvement

Essential KPIs

`python class SupportMetrics: def __init__(self, db): self.db = db

def calculate_metrics(self, period_days: int = 30) -> dict: return { Efficiency "deflection_rate": self._deflection_rate(period_days), "first_contact_resolution": self._fcr_rate(period_days), "avg_response_time": self._avg_response_time(period_days),

Quality "csat_score": self._csat_score(period_days), "escalation_rate": self._escalation_rate(period_days), "reopen_rate": self._reopen_rate(period_days),

Volume "total_conversations": self._total_conversations(period_days), "automated_responses": self._automated_count(period_days), "human_interventions": self._human_count(period_days),

Content "top_intents": self._top_intents(period_days), "unanswered_queries": self._unanswered_queries(period_days), "knowledge_gaps": self._identify_gaps(period_days) }

def _deflection_rate(self, days: int) -> float: """Rate of tickets avoided thanks to AI""" query = """ SELECT COUNT(CASE WHEN escalated = false THEN 1 END)::float / COUNT()::float 100 as deflection_rate FROM support_conversations WHERE created_at > NOW() - INTERVAL '%s days' """ result = self.db.execute(query, [days]) return round(result[0]['deflection_rate'], 1)

def _identify_gaps(self, days: int) -> list: """Identify questions without good answers""" query = """ SELECT query, COUNT() as count, AVG(satisfaction) as avg_sat FROM support_conversations WHERE created_at > NOW() - INTERVAL '%s days' AND (satisfaction < 3 OR escalated = true) GROUP BY query HAVING COUNT(*) >= 3 ORDER BY count DESC LIMIT 20 """ return self.db.execute(query, [days]) `

Improvement Loop

`python class KnowledgeImprover: def __init__(self, metrics, kb_service, llm): self.metrics = metrics self.kb = kb_service self.llm = llm

async def weekly_improvement(self): Identify gaps gaps = self.metrics._identify_gaps(days=7) For each significant gap for gap in gaps[:10]: Analyze failed conversations failed_convs = await self._get_failed_conversations(gap['query'])

Generate article suggestion suggestion = await self._generate_article_suggestion( query=gap['query'], conversations=failed_convs )

Create ticket for content team await self._create_content_ticket(suggestion) Identify outdated articles outdated = await self._find_outdated_articles() for article in outdated: await self._create_review_ticket(article)

async def _generate_article_suggestion( self, query: str, conversations: list ) -> dict: prompt = f""" Analyze these failed support conversations:

Recurring question: {query}

Conversations: {json.dumps(conversations, indent=2)}

Generate an FAQ article suggestion that would answer this question. Include: title, short answer, detailed answer, tags. """

return await self.llm.generate(prompt, temperature=0.5) `

Integration with Existing Tools

CRM Connection (Salesforce, Hubspot)

`python class CRMIntegration: def __init__(self, crm_client): self.crm = crm_client

async def enrich_context(self, customer_email: str) -> dict: """Enrich context with CRM data""" customer = await self.crm.get_contact(email=customer_email)

if not customer: return {}

return { "customer_name": customer.get("name"), "company": customer.get("company"), "segment": customer.get("segment"), "lifetime_value": customer.get("ltv"), "open_opportunities": customer.get("open_opps"), "recent_purchases": customer.get("recent_orders", [])[:5], "support_history": customer.get("ticket_count"), "nps_score": customer.get("nps") }

async def log_interaction( self, customer_email: str, interaction: dict ): """Log interaction in CRM""" await self.crm.create_activity( contact_email=customer_email, type="support_chat", subject=interaction.get("intent"), description=interaction.get("summary"), outcome=interaction.get("resolution") ) `

Escalation Webhook

`python from fastapi import FastAPI, HTTPException from pydantic import BaseModel

app = FastAPI()

class EscalationRequest(BaseModel): conversation_id: str customer_email: str priority: str summary: str transcript: list

@app.post("/webhooks/escalate") async def escalate_to_helpdesk(request: EscalationRequest): """Create ticket in helpdesk (Zendesk, Freshdesk, etc.)"""

ticket = { "subject": f"AI Escalation - {request.summary[:50]}", "description": format_transcript(request.transcript), "priority": map_priority(request.priority), "requester": {"email": request.customer_email}, "custom_fields": { "ai_conversation_id": request.conversation_id, "ai_escalation_reason": request.summary } }

result = await helpdesk_client.create_ticket(ticket)

return { "ticket_id": result["id"], "ticket_url": result["url"] } ``

Use Cases by Industry

E-commerce

| Question Type | Automation Rate | Time Saved/Ticket | |---------------|-----------------|-------------------| | Order tracking | 95% | 8 min | | Return policy | 90% | 6 min | | Product availability | 85% | 5 min | | Order modification | 60% | 10 min |

SaaS / B2B

| Question Type | Automation Rate | Time Saved/Ticket | |---------------|-----------------|-------------------| | Product features | 85% | 12 min | | Pricing / Plans | 80% | 8 min | | L1 technical issues | 70% | 15 min | | API integration | 65% | 20 min |

Financial Services

| Question Type | Automation Rate | Time Saved/Ticket | |---------------|-----------------|-------------------| | Balance / Statements | 90% | 5 min | | Standard procedures | 85% | 10 min | | Fee explanations | 75% | 8 min | | Disputes | 20% (escalation) | N/A |

Best Practices AI Transparency

Always indicate when the customer is talking to an AI and offer the option to speak with a human. Graceful Escalation

Never leave a customer in a dead end. If the AI cannot help, transfer immediately. Feedback Loop

Systematically collect feedback to improve responses. Continuous Updates

Policies change, products evolve. Update the KB continuously. Test Before Deployment

Test each new article with real questions before putting it into production.

Learn More • Intelligent Knowledge Base - Centralize knowledge • Retrieval Fundamentals - Optimize search • GDPR and Chatbots - Customer data compliance

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Automate Your Support with Ailog

Deploying performant RAG customer support requires robust infrastructure and specialized expertise. With Ailog, benefit from a turnkey solution: • Embeddable widget in 3 minutes on your site • Native connectors to your sources (FAQ, docs, resolved tickets) • Intent detection and automatic routing • Seamless escalation to your existing helpdesk • Real-time analytics to measure deflection rate • European hosting GDPR compliant

Try Ailog for free and reduce your tier-1 tickets by 70%.