Berlin AI Engineering Group with m|rig

Customer support teams are under constant pressure to reduce response times, control costs, and deliver consistent customer experiences across channels and languages.

Traditional support workflows, manual ticket handling, static FAQs, or rule-based bots — often struggle to scale. They lead to slow response times, inconsistent answers, and growing operational costs.
Recent advances in Large Language Models (LLMs) now make it possible to build intelligent customer service chatbots capable of answering complex questions, retrieving relevant documentation, and automating support workflows.
But building a reliable AI-driven customer service bot requires more than simply connecting an LLM to a knowledge base.

How do you design a chatbot that:

• Retrieves accurate information from company documentation,
• Handles multiple support scenarios,
• Escalates correctly to human agents,
• And avoids hallucinations or off-topic responses?

In this 60-minute interactive webinar, we will walk through the end-to-end design and implementation of an AI-powered customer service chatbot, focusing on scalable architectures using Retrieval-Augmented Generation (RAG) and agentic workflows.
The session will include a technical walkthrough of a production-ready chatbot architecture, as well as practical examples from real companies such as La Redoute and Zalando, which have successfully deployed AI assistants at scale.

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# You’ll learn

### 💼 Business Challenges of Customer Support Automation

Understand why many support systems struggle to scale:

• Why traditional support workflows break (cost, speed, consistency)
• How AI chatbots can improve support efficiency
• Common challenges when deploying chatbots across large support teams

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### 🤖 How AI Chatbots Create Business Value

Discover how organizations progressively introduce automation:

1. Answer simple questions using FAQ knowledge bases
2. Qualify and route requests using intent detection
3. Automate workflows such as refunds or invoice generation

This progressive approach helps companies safely move from basic automation to fully operational AI assistants.

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### 🏗️ Architecture of a Scalable Customer Service Bot

Learn how to design a production-ready architecture including:

• Retrieval-Augmented Generation (RAG)
• Document ingestion and vector retrieval
• Guardrails and off-topic detection
• API integration for operational tasks
• Modular orchestration layers

The session will show the reference architecture used to build enterprise-grade chatbots.

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### 🔧 Target Workflow for an AI Customer Service Agent

We will walk through the full chatbot pipeline:

1. User prompt and guardrail validation
2. Intent detection and query expansion
3. Document retrieval from knowledge base
4. RAG generation using retrieved context
5. Structured response returned to the user

This workflow enables reliable answers while minimizing hallucinations.

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### 🧠 Best Practices for Building Reliable RAG Systems

Key engineering principles covered in the webinar:

• Modular agentic architecture
• Role-based node specialization
• Schema-first validation
• Prompt engineering best practices
• Serialization of inputs and outputs for reproducibility

These practices help ensure maintainability, safety, and auditability of AI systems.

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### 📊 Real-World Case Studies

We will also review real deployments:
La Redoute

• AI chatbot handling 60% of mobile support conversations
• 65% faster response times
• Estimated 60–70× ROI

Zalando

• AI shopping assistant deployed across 25 markets
• Used by 2M+ customers
• Increased product engagement and reduced return rates

These examples illustrate how AI assistants are already transforming customer interactions at scale.

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📅 Duration: 60 minutes
🧠 Level: Beginner to Intermediate
🛠 Topics: LLMs, RAG, Agentic Workflows
URL: https://events.teams.microsoft.com/event/9edcf607-50c7-4763-93a5-6673d9a9557b@d94ea0cb-fd25-43ad-bf69-8d9e42e4d175