Shared infrastructure for the access to justice sector — so legal aid organizations, courts, and technology partners can build better AI together, instead of reinventing the wheel alone.
The Legal Help Commons organizes shared resources around three complementary pillars — discovery, implementation, and community.
An R&D discovery platform cataloging AI projects, a formal task taxonomy, datasets, benchmarks, guides, and orientation resources for anyone entering the justice AI landscape.
Visit JusticeBench →Reference architectures, implementation playbooks, proven code and classifiers, RFP templates, cost models, evaluation rubrics, API connections, and design pattern libraries.
See what's inside →Working groups around specific workflows, public interest tech courses and groups, justice professional networks, and peer-learning programs connecting those building, stewarding, evaluating, and scaling new solutions.
Join a Working Group →Resources to help your team go from "what should we build?" to a deployed, evaluated, and maintained AI tool — without starting from zero.
Technical blueprints for specific workflows — technology stacks, data flows, integration points, prompt strategies, and decision logic.
Step-by-step guides covering planning, staffing, procurement, data prep, testing, launch, and maintenance for specific AI workflows.
Standardized protocols for measuring accuracy, jurisdiction sensitivity, equity, safety, and ongoing performance of justice AI tools.
Reusable classifiers, prompt libraries, data pipelines, and integration components that organizations can adopt or adapt.
Procurement language, budget frameworks, and staffing models so organizations can plan and fund AI projects realistically.
Tested UI/UX patterns for common justice AI interactions — intake flows, document explanation, multi-step navigation, and more.
Without coordination, the justice sector's AI investments fragment rather than compound.
Dozens of organizations are building overlapping AI tools in parallel — each solving the same OCR, classification, and accuracy problems from scratch.
There are no widely adopted benchmarks for whether a legal AI tool gives accurate, safe, jurisdiction-correct answers. The field is flying without instruments.
When a team in Illinois discovers that a certain prompt strategy fails for debt collection intake, that lesson doesn't travel to the team in Texas facing the same problem.
Well-resourced states and organizations build capable tools. Others can't keep up. Shared infrastructure levels the playing field.
The Legal Help Commons is in active development. Be in touch to share your work with us or discuss new resources, working group opportunities, and platform launches.