Bridging AI and Law: A Scalable Multi-Agent Platform for Quantitative Legal Analytics Across Millions of Documents
Ł. Augustyniak, K. Tagowski, A. Szymczak, J. Binkowski, A. Sawczyn, M. Skibiński, D. Janiak, M. Bystroński, G. Piotrowski, M. Bernaczyk, K. Kamiński, T. Kajdanowicz
Quantitative legal research needs more than a chatbot over case law. This paper describes a production platform that indexes millions of legal documents and lets agents aggregate, compare, and explain patterns across them, built with lawyers rather than for them.
@inproceedings{augustyniak2026bridging,
title = {Bridging AI and Law: A Scalable Multi-Agent Platform for Quantitative Legal Analytics Across Millions of Documents},
author = {Augustyniak, {\L}ukasz and Tagowski, Kamil and Szymczak, Adrian and Binkowski, Jakub and Sawczyn, Albert and Skibi\'nski, Micha{\l} and Janiak, Denis and Bystro\'nski, Mateusz and Piotrowski, Grzegorz and Bernaczyk, Micha{\l} and Kami\'nski, Krzysztof and Kajdanowicz, Tomasz},
booktitle = {Bridge between Artificial Intelligence and Law (AILaw)},
year = {2026},
url = {https://openreview.net/forum?id=hWjsyTSWrY}
}
In brief
The paper presents a production-scale, multi-agent platform that bridges AI research and legal practice. It indexes more than 3 million legal and tax documents and over 300 million semantic vectors, and it exposes them to a Quantitative Legal Agent architecture that runs aggregation queries and produces interpretable analyses across jurisdictions, rather than answering one question about one document at a time.
Production legal-analytics platforms of this kind combine large-scale document retrieval, expert-defined criteria, and structured extraction to support quantitative studies. That combination is the foundation that later work such as Schematize builds on when it automates the schema-design step.
Highlights
- Scale. Over 3M documents and 300M+ semantic vectors indexed, with retrieval and extraction designed to run across the whole corpus rather than a sample.
- Quantitative Legal Agent. An agent architecture for aggregation and interpretable analysis: counting, comparing, and explaining outcomes across many rulings, with the reasoning exposed to the legal user.
- Cross-jurisdiction. The same pipeline is applied to multiple document collections and legal systems.
- Built in production. The platform is the research backbone behind the AI-Tax work at Wrocław Tech, where the same stack serves working legal and tax professionals.
Presented at the Bridge between Artificial Intelligence and Law (AILaw) workshop, 2026. This page summarizes the paper from the published abstract and the platform it describes. The full text is on OpenReview.