The Legal Innovation & Technology Lab (LIT Lab) at Suffolk University Law School is an experiential program that combines the vision of the Legal Innovation & Technology Center with the pedagogy and legal services mission of our clinical programs.

In the Lab students work as part of a research and development (R&D) consultancy focused on legal tech and data science. Active areas of research include:

  • Expert systems/guided interviews
  • Codification of tacit knowledge
  • Artificial intelligence

Lab students use technology and data to develop solutions for legal aid organizations, courts, nonprofits, and law firms to help them improve efficiency and effectiveness.

William Gibson

The LIT Lab torch

Suffolk’s Legal Innovation & Technology Lab

Providing a legal time machine to those in need.

Support the LIT Lab

Become an affiliate!

The LIT Lab connects affiliates with legal innovators and law students working with emerging legal technologies.

Suffolk’s LIT Lab is the nation’s no. 1-ranked legal technology program. The LIT Lab enables:

  • Public access to legal services, information, and justice
  • Law firms, courts, legal services providers, and corporate legal departments to become more efficient and effective

The LIT Lab develops cutting-edge innovations while educating future lawyers, integrating pedagogy with the service-oriented mission of Suffolk’s clinical programs, which are regularly ranked among the top 15 clinical programs in the U.S. Learn how to become a LIT Lab affiliate.

Featured affiliate

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Other ways to support the LIT Lab

Featured projects

The Document Assembly Line

Document Assembly Line logo

Open-source tools for court forms, guided interviews, and e-filing.

Spot, an AI issue spotter

Give Spot a plain-language description of a situation and it returns a list of relevant legal issues. Developers can use Spot to guide people in need of legal assistance to available resources.

Law lab directory

A directory of legal innovation and technology labs—academic programs for experiential and experimental learning.