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$122m

Series D
Total Funding000k
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More about Blue J Legal
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Founded in 2015, Blue J is a legal technology company that originated from the collaboration of University of Toronto law professors Benjamin Alarie, Anthony Niblett, and Albert Yoon, along with veteran software engineer Brett Janssen. The founders' academic and professional roots in law and economics are central to the company's mission. Alarie, who serves as the CEO, is a law professor and the Osler Chair in Business Law at the University of Toronto, with a background in tax law and the application of artificial intelligence to legal reasoning. This expertise has shaped the firm's direction, aiming to address the complexities and ambiguities inherent in tax law.

Blue J operates in the legal technology market, providing AI-powered research and analysis platforms specifically designed for tax and legal professionals. The company's primary clients include accounting firms of all sizes, such as the Big Four, law firms, corporations, and government agencies like the Canada Revenue Agency and the Department of Justice. Revenue is generated through a subscription-based model for its software-as-a-service (SaaS) platforms. The company has demonstrated significant growth, more than doubling its revenue and customer base in the first half of 2025 and securing a $122 million USD Series D funding round to fuel further expansion.

The core of Blue J's offering is a suite of tools that use machine learning and generative AI to analyze vast amounts of legal data and predict court outcomes with high accuracy. Its flagship product, "Ask Blue J," is a generative AI platform that allows tax professionals to ask complex questions in natural language and receive verifiable answers with citations in seconds. This contrasts with traditional keyword-based legal research, offering significant time savings. The platform covers U.S. federal, state, and local tax, as well as Canadian and UK tax law. In addition to tax, the company has developed products for employment law, addressing issues like wrongful dismissal and employee classification. Features include "Classifiers" to predict outcomes for specific fact scenarios, "Case Finders" to locate relevant cases based on facts rather than keywords, and "Case Analytics" to visualize how various factors influence legal outcomes.

Keywords: legal technology, tax law, AI legal research, generative AI, case outcome prediction, legal analytics, tax analysis software, employment law software, legal SaaS, Benjamin Alarie, University of Toronto, legal research platform, AI for lawyers, tax compliance, regulatory technology, legal data analysis, Blue J, tax professionals, accounting firms, corporate legal departments

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