Companies, like all economic players, are leaving behind digital traces of their activities to an increasing extent. The Digital Layer approach leverages these data sources and provides an alternative method for analyzing company networks. The integration of the Digital Layer approach into webAI provides our customers with a new tool for capturing and analyzing enterprise networks.
As language assistants in the living room, in search engines on the Internet or as systems for autonomous driving in cars: Artificial Intelligence (AI) is already being used in many different ways and has long been part of our everyday lives.
Web-based AI indicators offer a comprehensive, granular, up-to-date and cost-effective alternative to traditional data collection methods. We at are pioneers in the field of web-based indicator technology. With webAI, we are developing an artificial intelligence that provides our customers with market and company information in real-time.

Press articles


Kinne J, Lenz D (2021) Predicting innovative firms using web mining and deep learning. PLOS ONE 16(4): e0249071.
Kinne, Jan und Janna Axenbeck (2020), Web Mining for Innovation Ecosystem Mapping: A Framework and a Large-scale Pilot Study, Scientometrics

Dania Eugenidis, David Lenz, Christoph Leser, Frauke Schleer-van Gellecom  und Peter Winker (2020), Text-mining basierte Analyse der Kapitalmarktreaktionen auf Ad-hoc-Mitteilungen, CORPORATE FINANCE, 09-10.

Kinne, Jan und David Lenz (2019), Predicting Innovative Firms Using Web Mining and Deep Learning, ZEW Discussion Paper No. 19-001, Mannheim.

Kinne, Jan und Resch Bernd (2018), Generating Big Spatial Data on Firm Innovation Activity from Text- Mined Firm Websites, GI_Forum 1, 8289.

Krüger, Miriam, Jan Kinne, David Lenz und Bernd Resch (2020), The Digital Layer: How Innovative Firms Relate on the Web, ZEW Discussion Paper No. 20-003, Mannheim.

Mirtsch, Mona, Jan Kinne und Knut Blind (2020), Exploring the Adoption of the International Information Security Management System Standard ISO/IEC 27001: A Web Mining-Based Analysis, IEEE Transactions on Engineering Management.

Rammer, Christian, Jan Kinne und Knut Blind (2019), Knowledge Proximity and Firm Innovation: A Microgeographic Analysis for Berlin, Urban Studies.

D. Lenz, C. Schulze, M. Guckert (2018),”Real-time Session-Based Recommendations using LSTM with neural Embeddings”Artificial Neural Networks and Machine Learning – ICANN 2018 | SpringerLink.

D. Lenz, P. Winker (2020), “Measuring the Diffusion of Innovations with Paragraph Vector Topic Models” PLOS ONE. 2020;15(1):1-18

Kinne, Jan, Miriam Krüger, David Lenz, Georg Licht und Peter Winker (2020), Corona-Pandemie betrifft Unternehmen unterschiedlich, Tagesaktuelle Webseiten-Analyse zur Reaktion von Unternehmen auf die Corona-Pandemie in Deutschland, ZEWKurzexpertise Nr. 20-05, Mannheim.

Kinne, Jan und Bernd Resch (2018), Analyzing and Predicting MicroLocation Patterns of Software Firms, ISPRS International Journal of GeoInformation 7, 1.

Kinne, Jan und Janna Axenbeck (2018), Web Mining of Firm Websites: A Framework for Web Scraping and a Pilot Study for Germany, ZEW Discussion Paper No. 18-033, Mannheim.