Keep attorneys accountable for material conclusions and exceptions.
AI governance for consequential legal work
Move from AI policy to proof.
Give firm leadership a defensible control layer for AI-assisted work: verification, attorney oversight, and an evidence trail for every consequential decision.
Evidence verification Human review gates Durable audit history
MATTER 2026-041CONTROLLED
AI-ASSISTED WORK PRODUCTContract risk memorandum
92/ 100
OUTPUTASSURANCE STATUSApproved with oversight
Material findings traced to approved evidence.
Evidence groundingPassed
Policy controlsPassed
Partner reviewRecorded
Built for the questions leadership is already asking
Who approved this work?What evidence supports it?Can we prove our controls worked?
01 / THE LEADERSHIP CASE
AI adoption is an accountability decision.
Policy explains what should happen. OutputAssurance records what actually happened at the point where evidence, automation, and human judgment meet.
Show how AI-assisted work was checked before it left the firm.
Turn one-off review practices into a repeatable firm control.
Build evidence that helps the firm understand and transfer emerging risk.
02 / THE CONTROL MODEL
A governed path from AI output to accountable action.
OutputAssurance sits between generation and consequential use. It does not replace expert judgment. It makes that judgment visible, consistent, and reviewable.
01
Ground work in the record
Trace material claims, citations, and conclusions to approved source evidence.
02
Route by consequence
Escalate exceptions and high-impact work to the appropriate attorney reviewer.
03
Preserve the decision file
Record versions, checks, approvals, and the exact work product released.
AI outputVerificationRisk bandExpert reviewEvidence record
What changes for the organization
Control without freezing innovation.
Give teams room to adopt useful tools inside a leadership-approved framework.
A consistent control standard across practices
Visible attorney accountability for exceptions
Evidence ready for clients, insurers, and auditors
A measured path from experimentation to adoption
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The goal is not to certify that AI is infallible. It is to prove that the firm exercised disciplined judgment before relying on it.
THE OUTPUTASSURANCE PRINCIPLE
COMMON STARTING POINTS
Contract analysisLegal researchDue diligenceClient-facing memoranda
03 / A PRACTICAL START
Begin with one workflow.
Prove the control.
A focused showing can map OutputAssurance to a selected workflow, its evidence, and the decisions that require accountable human review.
1 Select a consequential, repeatable use case2 Define evidence and review thresholds3 See the complete decision record in OutputAssurance
Request a showing