Case Study

BSH Hausgeräte

We built an AI-powered message processing system that helps BSH handle millions of customer inquiries across 30+ countries.

The Company

BSH is a multinational home appliance company with well-known brands like Bosch, Siemens, and Gaggenau. They operate in more than 50 countries, employ over 57,000 people, and handle a massive volume of customer communication daily. Efficient, high-quality customer service is central to their brand.

The Challenge

BSH’s customer service spans multiple countries, languages, brands, and product lines. Each market has specialized teams handling diverse request types: warranty claims, product inquiries, technical support, and more.

  • Volume: Millions of customer messages per year, with seasonal peaks
  • Complexity: Messages in many languages, covering dozens of product categories and request types
  • Routing: Each message needs to reach the right specialist, a task that was largely manual
  • Quality: Maintaining consistent response quality across all markets and agents
  • Scale: Growing volumes without proportionally growing the team

What We've Built

We developed an AI-powered message processing system that mirrors BSH‘s organizational structure, workflows, and quality requirements. The system sits at the center of their customer service operation.

Automated Classification

We built AI pipelines combining language models and task-specific classifiers that analyze every incoming message, identifying language, customer intent, product category, request type, sometimes sentiment, and urgency. These models were trained on BSH's actual historical data, not generic datasets.

Intelligent Routing

Based on classification, messages are routed to the appropriate team or queue. Urgent issues are flagged and escalated. The routing logic mirrors BSH's real organizational structure across countries and brands.

Response Automation

For well-defined request types, the system generates responses or handles requests end-to-end. The level of automation is configurable per request type and per market.

Monitoring Dashboard

Real-time visibility into message volumes, classification accuracy, automation rates, and response times. The system's performance is transparent and continuously tracked.

Technology Used

  • Language model pipeline for multi-language text analysis
  • Classification models trained on BSH’s historical data
  • Cross application event handling
  • Integration with BSH’s email and CRM/ERP infrastructure
  • Real-time monitoring dashboards
  • Automated reports and detailed analysis
  • Deployed on BSH’s infrastructure

Results

  • Millions of customer messages processed annually
  • Multi-country deployment across 30+ markets
  • Significant reduction in average response time
  • Improved processes across all markets and languages
  • Scalable architecture that grows with message volume

What We Learned

Every large-scale customer service deployment teaches us something. BSH reinforced several principles we bring to every project:

  • Multi-language AI requires training on real client data, generic models aren’t enough
  • Organizational complexity (many teams, many countries) is the hard part, not the AI itself
  • Gradual automation works better than big-bang rollouts
  • Monitoring and transparency build trust with operations

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