ContentRicher enhances articles for media companies by identifying relevant personalities from Wikipedia and Instagram, offering the option for manual integration or automatic use of the tool's suggestions.


The media landscape is undergoing a significant transformation: Media houses are increasingly losing their audiences to new platforms such as TikTok and Instagram. Attention spans are shortening, content is becoming more personalized, and even news is consumed through these platforms. Eventually, users find themselves in a filter bubble, lacking the background knowledge for engaging with interesting content outside of the bubble.

Generative language and cutting-edge multimodal models now offer a solution: Intelligently driven and combined, they enable the extraction of relevant information from texts and images and present it in a format tailored to the context and audience.

The Open Source Solution


The media landscape is undergoing a profound transformation as media houses lose their audiences to platforms like TikTok and Instagram. This has led to shortened attention spans and an increased formation of filter bubbles. Generative language and multimodal models are seen as a solution to intelligently extract relevant information from texts and images, and present it in a format tailored to the context and audience.


ContentRicher is a web-based tool designed to assist media companies in reaching a broader audience. It supplements articles for various target groups by identifying relevant personalities and summarizing information from Wikipedia and Instagram. Users can then decide whether to manually integrate the information or utilize the tool's automatic suggestion feature.


ContentRicher identifies relevant personalities from politics, entertainment, and sports, extracting information from Wikipedia and Instagram for provided texts. The contextually emphasized presentation allows editors to quickly integrate the information into the text, enabling the adaptation of articles to different target audiences with varying levels of prior knowledge. The tool prioritizes open-source models that can be self-hosted.

Fellows of the project

Veronika Gamper

Veronika brings a diverse background in computer science, film, and media. As the founder of WeDaVinci, a platform dedicated to AI-supported innovation, she promotes collaboration between humans and AI. After studying computer science, her professional journey led her to positions at BCG, CDTM, and RTL. Her passion for algorithms, AI, and creativity drives her daily work.