Building an AI Customer Support System at Scale

When our client, a major e-commerce platform, approached us with the challenge of handling 10,000+ daily customer queries, we knew we needed to build something special. This is the story of how we designed, built, and deployed an AI-powered customer support system that reduced response times by 80% while maintaining high customer satisfaction.

The Challenge

The existing support system was struggling:

• Average response time: 4+ hours
• Customer satisfaction: 3.2/5 stars
• Support cost: $15 per ticket
• Agent burnout: High turnover rate

Our goal was to automate 70% of queries while improving the customer experience.

System Architecture

We designed a multi-tier architecture to handle different query complexities:

Tier 1: Instant Resolution

Simple queries like order status, return policies, and FAQs are handled instantly using a fine-tuned model with RAG.

Tier 2: AI-Assisted

More complex queries get AI-generated responses that human agents review before sending.

Tier 3: Human Escalation

Sensitive issues (refunds over $500, complaints) are routed directly to human agents with AI-provided context.

Key Technical Decisions

Why RAG Over Fine-Tuning Alone

We initially considered fine-tuning a model on historical support conversations. However, we found that:

1. Knowledge updates: Product info changes frequently
2. Accuracy: RAG provided better factual accuracy
3. Cost: Cheaper than constantly re-training

Our final approach combined both: a fine-tuned base model for tone and structure, with RAG for factual information.

class SupportRetriever:
    def __init__(self):
        self.product_index = VectorStore("products")
        self.policy_index = VectorStore("policies")
        self.faq_index = VectorStore("faqs")
 
    def retrieve(self, query: str, category: str) -> List[Document]:
        # Select appropriate index based on query category
        index = self._select_index(category)
 
        # Hybrid search: semantic + keyword
        semantic_results = index.similarity_search(query, k=5)
        keyword_results = index.keyword_search(query, k=3)
 
        # Rerank and deduplicate
        return self.reranker.rerank(
            query,
            semantic_results + keyword_results,
            top_k=3
        )

Intent Classification

Before generating responses, we classify the customer's intent:

Results After 6 Months

The impact exceeded our expectations:

Query Resolution Breakdown

After deployment, we tracked where queries were being handled:

Lessons Learned

What Worked Well

1. Gradual rollout: Started with 10% of traffic, scaled up over 8 weeks
2. Human-in-the-loop: Agents reviewed AI responses initially, providing feedback
3. Continuous learning: Weekly model updates based on new patterns
4. Clear escalation paths: Customers could always reach a human

What We'd Do Differently

1. Earlier investment in monitoring: We underestimated the importance of real-time quality metrics
2. More diverse training data: Initial model struggled with non-English queries
3. Better handoff experience: The transition from AI to human could be smoother

Technical Implementation Details

Response Generation Pipeline

async def generate_response(query: CustomerQuery) -> Response:
    # 1. Classify intent
    intent = await classifier.predict(query.text)
 
    # 2. Check if escalation needed
    if should_escalate(intent, query):
        return escalate_to_human(query)
 
    # 3. Retrieve relevant context
    context = await retriever.retrieve(
        query.text,
        category=intent.category
    )
 
    # 4. Generate response
    response = await llm.generate(
        prompt=build_prompt(query, context, intent),
        max_tokens=500,
        temperature=0.3  # Lower for consistency
    )
 
    # 5. Safety checks
    if not safety_filter.is_safe(response):
        return escalate_to_human(query)
 
    # 6. Confidence check
    if response.confidence < 0.85:
        return Response(
            text=response.text,
            needs_review=True
        )
 
    return response

Future Improvements

We're currently working on:

1. Voice support: Extending to phone calls
2. Proactive outreach: Anticipating issues before customers complain
3. Personalization: Tailoring responses based on customer history
4. Multi-modal: Handling images (damaged products, receipts)

Conclusion

Building an AI customer support system is as much about process as it is about technology. The key to our success was:

• Starting small and iterating
• Keeping humans in the loop
• Measuring everything
• Listening to customer and agent feedback

If you're considering a similar project, feel free to reach out. We're happy to share more details about our approach.