Imagine a giant puzzle where pieces are scattered across different countries and languages. This tool acts like a smart assistant that automatically finds those pieces and fits them together to show the big picture of criminal activity. It helps investigators stop talking past each other by putting everyone on the same digital page.
Law enforcement agencies use fragmented tools that cannot talk to each other, making it hard to track criminals moving across borders. This creates blind spots in fighting terrorism, drug trafficking, and cybercrime.
An integrated platform featuring LLM-based evidence collection, data mediation for database interconnection, and analytics visualization reports.
If you are a threat intelligence provider dealing with fragmented data on cybercrime—this project developed a platform that uses Large Language Models to automate evidence collection. This allows you to track high-risk networks across borders more efficiently.
If you are a software vendor dealing with non-interoperable police databases—this project developed a system for data alignment and mediation. This enables your clients to exchange information using common standards across 6 different countries.
If you are an AML firm dealing with complex transnational illegal activities—this project developed analytics and visualization reports. This helps you identify links between legitimate business and high-risk criminal networks.
Based on available project data, the EU contribution is EUR 1,499,800, but specific commercial pricing for the resulting platform is not listed.
Yes, the project aims to establish a broad ecosystem and benchmark its tools with Europol’s Innovation Lab to ensure wide adoption by practitioners.
Based on available project data, specific licensing terms are not provided, though the project involves 5 SMEs and 6 industry partners.
The platform uses common standards, data alignment, and mediation to interconnect with national and international databases.
The project runs from 2024-10-01 to 2026-09-30.
The consortium is heavily industry-driven with an 86% industry ratio, consisting of 6 industry partners and 1 other entity. With 5 SMEs involved across 6 countries, the project is structured for commercial viability rather than academic research, focusing on practical deployment for law enforcement.
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