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Paid per review
A published rate per completed review — the same whether you accept, correct, reject or escalate. Reviews are timed and scoped, so you know what you are taking before you take it. Pay is never contingent on approving anything.
Most legal AI treats attorneys as the thing being disrupted. We treat them as the thing being compounded. The judgment you apply to a review is measured, it is paid — and where it becomes knowledge the system reuses, you share in what it earns for as long as it keeps working.
You accept work from queues scoped by jurisdiction and practice area, on standards you can read before you accept anything. It is not a referral scheme, and it is not piecework dressed up as one.
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A published rate per completed review — the same whether you accept, correct, reject or escalate. Reviews are timed and scoped, so you know what you are taking before you take it. Pay is never contingent on approving anything.
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Contribute a playbook, a position, a clause pattern. When endpoints use it, you share the revenue it generates — for as long as it keeps working, not as a one-time payment for a document. Contribution stays attributed.
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Corrections, rankings, redlines and escalation calls are the RLLF signal behind OpenLegalLM. The standards you mark to are written and versioned, and the application side cannot override a flag you raise.
Matter work is separate and real: when a customer’s matter goes Yellow or Red, a conflict-checked attorney is engaged by the customer, on a stated scope, with the file already built — the record, the documents, the history and the issue list, assembled before the first call. The two modes of review
It arrives with provenance, consent and a permitted use, and it stays attributed. You’re paid per review, and you share in the revenue the knowledge you contributed generates, for as long as it keeps working. Your reviews train the model; your pay is never contingent on approving anything.
No — that is the point of the pay design. The rate per completed review is identical for an accept, a correction, a rejection and an escalation, and there is no throughput bonus tied to outcomes. Revenue share lives in a separate ledger attached to contributed knowledge, not to reviews. A reviewer who flags heavily earns exactly what one who flags rarely earns for the same volume.
Clean-Room-anonymized artifacts: the document structure, jurisdiction, legal posture and clause language intact, with the company, the people, the counterparties and identifying amounts removed. You never learn whose work it is, and the terms bar you from trying to find out. A live customer matter is different — you see it only when that customer has engaged you on it.
Continuous review does not — you are reviewing anonymized artifacts under contributor terms, not advising a client. A matter engagement does, and it runs between you and the customer directly, conflict-checked, on a stated scope. Which obligations attach in your jurisdiction is exactly the kind of question to raise with us before you accept a queue — the standards and terms spell out the structure, and we would rather you probe it now than discover it later.
The review standards for the queues that fit your practice, and the reviewer terms — in writing, before you commit to anything. We reply within two business days.