Legal aid technologist
The builder who has been working alone
She built an internal AI tool at her organization. It works pretty well, but she has never formally evaluated it and nobody else knows about it. She finds the Commons through recommendations from others in her network.
The Commons lets her evaluate her tool against published standards. She can also have her project documented as reference architecture, packaged so other organizations can consider and possibly adopt it. She goes from isolated builder to active partner whose work benefits the whole field.
Statewide Legal AIid funder
The funder who is unsure of what projects to support
He has $400K for AI-related legal aid technology grants. He has received vendor proposals and grantee requests and cannot tell which ones are good. He finds the Commons conformance standards through his professional network.
He adds the standards to his next grant RFP. He can evaluate proposals against a real, clear quality bar instead of guessing which ones will be feasible and safe. The conformance levels tell him what to require and what to look for. He can ask key followup questions to vendors during demos and set up technology standards into the agreements.
Civic tech engineer
The engineer looking for a real problem
She spent years building government technology. She is exploring legal help but does not know the law, does not know any legal aid attorneys, and does not know where to start.
She finds a Commons workflow page that has the functional agenda, the conformance standards, the test suite, and the reference architecture. She starts building within a week. The spec tells her what the tool should do. The test suite tells her whether it is working. The reference architecture shows her one way to build it.
Court administrator
The court admin who needs it to just work
Her presiding judge told her to "look into AI." She has a two-person IT team and no AI experience. She reads a Commons implementation case study from another court her size.
She sees the vendor-independent integration path works with her existing case management system. She follows the playbook with her small team, and reaching out to other groups who have also built out this system. She has a working system deployed within months, without having to figure everything out from scratch.
Community health worker
The frontline worker who encounters legal problems
She does home visits with patients. They mention eviction threats, wage garnishment, court letters they do not understand. She has nothing to offer except the recommendation to "call legal aid."
She finds a Commons tool designed for non-lawyer operators. She shares it with a legal aid group locally, who works with their internal IT staff to build a local version of this tool for her and other justice partners to use. She can do a first-level legal screening during a home visit and connect her patient to help that afternoon. The tool tells her what to ask and what the answers mean.
Skeptical legal aid director
The director who thinks AI is hype
He has seen too many technology demos that do not hold up on real cases. He reads a Commons housing workflow page and sees the evaluation data: honest about what the tool can and cannot do.
He tries it on anonymized cases from his office. It catches defenses his intake team missed. He decides to greenlight a local implementation of this tool, because the trial run and evaluation data convinced him, not a sales pitch.
Frontier lab engineer
The engineer on a sprint
She is assigned to a four-week sprint on an eviction defense tool. The functional agenda tells her what the tool checks. The conformance standards tell her what "better" means.
She rebuilds the prompt architecture. Jurisdiction precision improves significantly. She produces a reference architecture document and performance data. The Commons gets a better blueprint. She gets a measurable contribution to the field.
Law student
The student running an evaluation audit
She is enrolled in an AI audits course. Her project: audit a Commons tool using the published evaluation methodology. She runs test scenarios, scores the outputs, and writes a full audit report.
The Commons gets independent evaluation data. She gets a career-defining project and a publishable piece of work. The tool gets better because someone tested it who did not build it.
MBA student
The student who wants to build something that matters
He finds a Commons project brief through his business school's social entrepreneurship center. The brief describes the tool, the market, and the open question: who pays for this?
He spends a summer building the business model. He might start a company. The Commons gave him a real problem with real constraints instead of a hypothetical case study.
