A deployed investigative AI search engine for combating human trafficking at web scale

Abstract

Online human trafficking investigations generate vast amounts of noisy, heterogeneous, and deliberately obfuscated data, making traditional search and analytics tools ineffective for supporting law enforcement. This paper discusses the deployment of the Domain-Specific Insight Graphs (DIG) system, an AI-powered investigative search engine that was operationally used by over 200 U.S. law enforcement agencies for more than five years in the pre-COVID period. The system integrates advanced research conducted over the years in information extraction, knowledge graph construction, and entity-centric search to enable investigators to formulate queries without technical background, aggregate evidence, and uncover latent relationships among entities such as phone numbers, emails, and locations. Beyond technical innovation, the deployment required sustained attention to usability, explainability, and policy compliance, ensuring trust in high-stakes legal contexts. We report measurable benefits in investigative efficiency, case initiation, and prosecutorial support, as well as lessons learned from long-term maintenance and adaptation to evolving online platforms. Since 2020, work conducted in this domain has also had significant policy and advocacy ramifications. The system's generalized design has also allowed it to be prototyped for adjacent illicit domains, including securities fraud and illegal firearm sales, demonstrating the broader applicability of AI-driven investigative tools. We contribute a rare case study of an AI system that has transitioned from research to sustained real-world impact in a socially critical domain.