An AI4Justice User Journey

A prototype of a prototyping tool.
26 January 2026
Dan Ryan (djjrjr@gmail.com)

NOTE: This is version 0.0 of an oversimplified and naive version of the journey a legal innovator might experience using a platform like AI For Justice.

This artifact is more to prototype the storyboard as a prototyping tool than to be a fullblown attempt at a rigorous storyboarding of the platform itself. If this looks promising, I'll sketch out something more realistic next.

Abel N is director of a legal aid NGO in Ghana. Abel learns about AI4J at a regional justice reform conference. He sees a demo of a legal bot that can reduce intake time from two hours to fifteen minutes.

His agencies serve 2,000 cases per year and turn away 10,000 more.

Abel fantasizes about the development of "LegalMate Ghana" that can help people solve basic problems and act as front end to the work of the paralegals at the legal aid organization.

What if there were a way to serve twice as many people per year, he muses.

When Abel returns to Kumasi the reality of 15 paralegals in cramped offices makes "LegalMate Ghana" seem like a bit of magical thinking.

Undeterred, he goes online and opens up a project with AI4J.

The platform requires more than just registration. It turns out that IT uses an AI bot to handle intake. The AI4J bot leads Abel through an almost hour long initial assessment. What's the idea? What is happening now? What kind of an organization? And on and on.

Abel iterates with the AI4J bot and exchanges emails with AI4J staff. The bot wants to know a lot about their process, their successes and failures, staff capacity, bottlenecks and hurdles they encounter in their work. It asks Abel to describe the app he has in mind and offers pushback that helps him refine the concept.

The AI4J bot synthesizes a work plan and advises Abel about the kinds of permissions and such that he'll need to secure in order to get the project started.

The bot suggests that version 0 of the app will use these plug and play AI4J modules

APPFrame
UserReg
ListenLearn
Report2Case
Language (Asante Twi)

These will arrive with basic customization for language and branding. Bot generates concept materials for feedback

Abel runs workshop with concept materials and records feedback from his team. The feedback is uploaded so the bot can make a few last minute adjustments before releasing version 0.0 and creating a build and test plan and schedule.

Desired Outcome: Legal aid doubles level of service by reducing case intake to about 25% current, better triage, and case support for paralegals. 

Build Goal: an app that serves as front end, case management, document generator, and followup CRM. 

AI4J bot recommends phase I just case intake.

Step one of the build is to upload Ghanaian legal codes and local rules and document standards and legal aid eligibility requirements.

Since this is the first AI4J project in Ghana Abel uses the GetNationalLaw module to carry out the process.

He scans the document their office uses on local rules.

Version 0.1 is ready. It speaks the local language, knows Ghanaian Law, and is familiar with local courts and government agency document and form rules.

It's time for evaluation.

The FormFillerOuter eval generates narratives based on intake forms created by paralegals and feeds these to our intake bot and scores it on how well it reproduces originals.

The AI4J platform facilitates Abel's testing of version 0.1 with his coworkers.

Abel and his team spend a lot of time filling out ethics paperwork to allow for testing of the new app with actual legal aid users. The AI4J bot is available for consultation during this process. It's been through this many times before in other jurisdictions.