Automatic Call Routing Leveraging Deep Learning

Case Studies

Automatic Call Routing Leveraging Deep Learning

A case study using voice to text and NLP

Business Challenge

The customer was looking to seamlessly and effectively manage public safety with next generation control centers and dispatchers.  They required:

  • Lower call center routing latency and lower Abandonment Rate
  • Improve customer experience through customer/data-360 integration
  • Automatically route call to correct agent


Technical Challenge

  • Run deep learning models on transactional data in real time
  • Continuous training models are built and deployed with no downtime
  • Automatically trigger transactional workflows to route call to correct agent based on prediction criteria and scoring
  • Leverage deep learning without GPU



With GigaSpaces’ InsightEdge, integrated with Intel’s BigDL and Xeon processors, customers can run real-time Deep Learning models on the incoming calls to automatically route them to best agent according to their need.

  • The user speaks using web interface
  • Browser converts speech to text and sends to controller
  • Controller writes data to a Kafka topic
  • Spark job listens on Kafka topic  and using BigDL model, creates prediction
  • BiGDL Prediction to InsightEdge
  • InsightEdge event processor listens for Prediction data  and routes call session




  • Increase of ~30% performance over previous processors.
  • Simplified ETL, reducing component and cluster sprawl for optimal performance and TCO.
  • Classify calls, retrieve customer data and route to relevant agent in ~50 ms