AI in the Newsroom: Machine Learning for the Paywall of the F.A.Z.

Client: F.A.Z.
Project: AI in the Newsroom – Machine Learning for the Paywall of the F.A.Z.

FAZ Paywall
FAZ Paywall CMS
  • The aim of F.A.Z. was to obtain a data-based decision-making basis whether an article should rather be used for the paywall or as a free article for online advertising.
  • jambit met the challenge with sophisticated natural language processing (NLP).
  • jambit had to face a situation in which historical data was only available in a limited and unedited form. For further processing, jambit structured the data.
  • Based on this historical data, jambit trained models using machine learning (ML).
  • These models now predict different quality metrics, such as the number of subscriptions for new articles.
  • An editor can now see from the recommendation in the CMS if the paywall for this article should be activated. It is calculated from the different quality metrics.
  • Used AI methods: transfer learning, feature engineering and gradient boosting
  • Used software technologies: BERT, PyTorch, scikit-learn, LightGBM, MLflow, Azure AutoML, Docker, Azure Functions


  • The F.A.Z. won an AI solution that provides the editors with a data-based decision-making basis on paywall or free articles.
  • The project achieved time and cost savings as well as quality due to an AI framework developed in-house by jambit F.A.Z. benefited from know-how transfer from other jambit projects, for example in the automotive and Industry 4.0 sectors.
  • AI experts, CMS developers and administrators from jambit worked directly together with an interdisciplinary team consisting of editorial staff, F.A.Z. Data Department and F.A.Z. Technical Project Management.
  • Cost savings were also achieved through jambit's own GPU computing infrastructure and optimal embedding in the existing Azure Cloud ecosystem.

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Robert Kowalski jambit Head of Business Division Media

Robert Kowalski

Head of Business Division Media

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