Nussknacker has been deployed in various companies and domains. As a showcase, we present here two deployments that demonstrate the power of the solution in the case of the telecom company PLAY (part of the Iliad Group).
Case studies
case studies
Real Time Marketing
→ Transformed batch-based marketing campaigns into real-time interactions
→ Eliminated multiple-day delays in campaign execution
→ Addressed the challenge of managing complex business rules and campaign logic
→ Resolved the issue of sending irrelevant or outdated offers to customers
Fraud Detection Solution
→ Eliminated the delay in fraud detection from batch processing
→ Addressed complex SIM card fraud schemes that were hard to detect
→ Reduced financial losses from fraudulent activities
→ Solved the challenge of managing complex fraud detection rules
resources
ML models inference in fraud detection
How to simplify the integration of ML models into business applications, automate many of the technical complexities, and support advanced techniques like A/B testing and ensemble models. A fraud detection example
Next Best Action recommendation management simplified
Business scenarios can become increasingly complex, causing many problems for users and administrators. Read how we tackle the problem in the Request Response processing example
Real-Time Recommendations Using Machine Learning
Nussknacker simplifies the integration of machine learning models into streaming data processes. Software teams can now build intelligent recommendation systems using Nussknacker, Snowplow, and MLflow.
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blog
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