bunq handles 97% of user support with Amazon Bedrock multi-agent AI system
bunq's traditional support systems could not keep up with round-the-clock multilingual demand from 20 million users, creating bottlenecks and straining internal resources. As bunq scaled its initial multi-agent architecture, a router-based design became a single point of failure due to routing complexity, overlapping agent capabilities, and a scalability bottleneck that required comprehensive testing for every new agent added.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits natural language request
Users interact with Finn through bunq's application and web interface using natural language for their requests.
Finn now handles 97% of bunq's user support activity with average response times of 47 seconds, was deployed in 3 months from concept, and supports 38 languages, positioning bunq as Europe's first AI-powered bank.
What failed first
The initial router-based multi-agent architecture became a single point of failure: routing logic grew too complex, agents required access to the same data sources, and every new specialized agent required comprehensive testing of all routing scenarios.
Results
Time saved47 seconds
Volume97%
Running sinceJanuary 2025 (concept start; production within 3 months)