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Legal AI Tools

Fail-closed tools for legal work.

In legal automation, a silent guess is worse than a refusal. PLI's tooling principle is simple: when the evidence is weak, the system should stop, record why, and wait for human review.

The design posture

Legal tools should be built around auditability and restraint. A document-filing system should not move a scan because a model guessed a client name. A redaction tool should preserve review trails. A triage tool should separate confidence from convenience.

What fail-closed means

Fail-closed systems treat automation as conditional. They move forward only when independent evidence agrees, and they leave work in a reviewable queue when facts are ambiguous, sources conflict, or a model endpoint is unavailable. That is slower than pretending every answer is reliable, but it is much safer for legal operations.

Public GitHub work

The current public PLI Labs tools page states the principles for legal AI tooling. Related earlier public repositories show the same direction in narrower form: local legal redaction, local document and email triage, and an archived legal workflow experiment. Those repositories are useful context for the kind of tools PLI builds, even where newer PLI product code has not yet been published.

Law-practice standard: automation should create better work queues and stronger drafts. It should not create unreviewed legal decisions.

Source links

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