jev-open — local classifier for intake at law firms and clinics
A local specialist for training and classifying intake cases at law firms and clinics, with a CPU service.
jev-open is a research and education project for building a local classifier for service messages. It runs on the user’s machine and answers questions defined in a task sheet with “yes,” “no,” or “uncertain.” The model observes the messages; rules written in code determine queues, deadlines, amounts, and priorities. If there is not enough evidence, the answer is not turned into “yes,” and uncertain cases go to a person.
The system can help organize triage messages in law offices and clinic reception areas by ordering the queue and flagging urgent cases. Every message still reaches a person. In legal offices, its scope is administrative: it does not provide legal opinions, and case updates require identity verification. At clinics, health data is sensitive, so the service runs on the clinic’s machine. The project does not claim parity with Jev or readiness for production use.
To get started, prepare the environment and write a task sheet with the questions, labels, and actions for each queue. Someone from the relevant field should review the examples, and real messages need to be anonymized. Training requires a GPU; the service can run on a CPU. Before any real use, professionals should validate the keyword lists and fixed texts. The project says results that depend on real data or a real VPS have not yet been measured.
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