Nussknacker has been successfully 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
Nussknacker can be used where business teams want to define and run real-time decision algorithms, like in marketing, monitoring, Internet of Things, next best action, fraud detection, and more
customer story
Real time marketing for a Telecom Service Provider
The Client needed both very high throughput and the ability to analyse events in time windows. After the competitive evaluation, the real-time marketing decisioning algorithms were implemented with Nussknacker.
Fraud Detection Solution for a Telecoms Provider
In Nussknacker it is easy to set up hidden detection thresholds to prevent easy reverse engineering by fraudsters. Nussknacker uses the same expression language to formulate conditions for sensitivity thresholds, ranging from very simple to very complex.
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
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blog
Nussknacker Team
Nov 26, 2024
1.18 release
With the latest 1.18 release we have added new Activity Panels to replace Versions, Comments and Attachments panels. ☑️Now you can browse all scenario activities in a single chronological list.
Arek Burdach
Sep 27, 2024
Using Nussknacker with Apache Iceberg: Periodical report example
Nussknacker now supports Flink catalogs. This means you can use it with Apache Iceberg for tasks like data ingestion, transformation, aggregation, enrichment, and creating business logic. This blog post will show you how to use Nussknacker and Apache Iceberg together for a real-world example
Łukasz Jędrzejewski
Sep 6, 2024
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