We generate robust evidence fast


What is Silvi.ai?


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Our Products

Meta analysis

Meta-analyses are the golden standard of evidence-based medicine. Silvi is an automatized meta-analysis system: With machine learning, Silvi enables faster and fully updated meta-analysis. Our system supports better decision making and prioritization of resources. Silvi optimizes all four phases of a meta-analysis (data import, screening, extraction, and analysis).

Literature review

A systematic literature review is a great tool to get an unbiased overview of the current literature on a topic. Silvi is directly connected to search engines, making sure that the systematic literature reviews are always updated. Silvi follows scientific guidelines and provide you with easy-to-use information tables tailored to your needs.

Knowledge Libary

The knowledge library combines systematic literature reviews and meta-analyses to give the most in-depth overview of a given scientific topic. The direct connection to search engines and the integration with statistical analysis software ensures a complete overview and analysis of all relevant parameters of the chosen studies; the knowledge library turns data into information.


The Story of Silvi.ai

Silvi was founded in 2018 by Professor in Health Economic Evidence, Tove Holm-Larsen. The idea for Silvi stemmed from Holm-Larsen’s own research, and the need to conduct meta-analyses faster.


Originally, the ideas behind Silvi were a component of a larger project. In 2016, Professor Holm-Larsen founded the group “Evidensbaseret Medicin 2.0” in collaboration with researchers from Ghent University, Technical University of Denmark, University of Copenhagen, and other experts. 

Silvi.ai 2021

A-Evidence ApS


Njalsgade 76
2300, Copenhagen S



Mail: info@silvi.ai 

CVR: 40054626


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Provides fully updated medical evidence of the highest quality. We thereby improve the medical treatment of patients, and fight pseudoscience. We concur to all medical guidelines, and by using AI we decrease the time to solid evidence significantly.