Bridging AI and Law: A Scalable Multi-Agent Platform for Quantitative Legal Analytics Across Millions of Documents

Lukasz Augustyniak, Kamil Tagowski, Adrian Szymczak, Jakub Binkowski, Albert Sawczyn, Michał Skibiński, Denis Janiak, Mateusz Bystroński, Grzegorz Piotrowski, Michal Bernaczyk, Krzysztof Kamiński, Tomasz Jan Kajdanowicz

Keywords: Legal AI, Quantitative Legal Analytics, Multi-Agent Systems, Information Extraction, Semantic Search, Explainable AI, Human-AI Collaboration, Legal NLP

Paper Type: Demo papers

TL;DR: A production platform enabling quantitative legal analytics across 3M+ documents through a five-stage multi-agent pipeline and lawyer-AI collaborative workflow, overcoming RAG limitations in scale and interpretability.

Abstract: We present a production-scale platform that bridges artificial intelligence and legal practice, currently indexing over \textbf{3 million legal documents} and \textbf{300 million semantic vectors} across multiple jurisdictions. While retrieval-augmented generation (RAG) systems have advanced legal information retrieval, they remain limited in processing scale, quantitative aggregation, and interpretability---capabilities crucial for trustworthy AI in law. Our \textit{Quantitative Legal Agent (QLA)} architecture enables systematic analysis across massive document collections through a unified data model supporting Polish court judgments (3M+), UK rulings (6K), and tax interpretations, with an extensible ingestion pipeline for additional jurisdictions and document types. The platform introduces a novel \textit{lawyer-AI specialist collaborative workflow}: legal experts define search criteria, curate example documents into collections, and specify extraction goals, while AI specialists expand document retrieval and refine extraction schemas---enabling rigorous quantitative analysis with validated aggregation. This workflow has already produced published legal analytics studies. We demonstrate the system's capabilities in bias detection, precedent mapping, and trend analysis, showing how QLA advances responsible, transparent AI for high-stakes legal applications.

Submission Number: 49