Adjuda routes important AI decisions to the right reviewer, holds action until your team reviews, and creates a complete audit trail. As precedent builds, routine work clears automatically under your controls.
Models can make useful calls, but your business still owns the outcome. The hard part is showing the right person saw the risky decisions, made the call before action, and left a clear audit trail.
The people who know the edge conditions see the mistakes late, or not at all.
Risk thresholds, exceptions, and reviewer precedent live inside the business, not inside the model.
If sign-off and reasoning are scattered, the audit story depends on screenshots and memory.
Start in the Adjuda review console, embed review in internal tooling, or send lightweight approvals through Slack, Teams, and Discord. The original request, AI finding, and required action stay together, so reviewers are not sent to another queue.
Adjuda sits between an AI recommendation and the action your business will take. Risky, novel, or low-confidence decisions stop for review; routine work can move only when your rules and precedent allow it.
Your AI makes a call. Adjuda checks risk, policy, and precedent, then sends the right decisions to the right reviewer.
The reviewer approves, corrects, or rejects it in context, the way they work today. No prompts, no new tool.
The downstream action waits until the required sign-off is complete.
The audit trail keeps who signed off, what changed, which rule applied, and why.
Automation earns its way forward. Adjuda clears routine decisions only when they match reviewed precedent, active rules, and the confidence thresholds you set.
A decision can auto-clear only when it matches patterns your reviewers already approved.
Novel, high-risk, low-confidence, or disputed decisions go back to a person before action.
Override spikes, rule changes, or drift can pause automation and return a flow to review.
Who was asked, who signed off, what changed, which rule applied, and why, in one audit trail for every governed decision. Filter by decision, reviewer, date, or flow.
Human review, AI finding, system hold, and manual override stay separate.
Each audit trail keeps the reviewer’s reason and the policy active at the time.
Hand over a hash-chained audit trail without rebuilding the story after the fact.
Every reviewer decision becomes precedent your business owns. As the pattern proves itself, Adjuda can move from audit to assist to automate, while override rates, rule changes, and novelty keep risky decisions in human review.
Same total volume, always governed. Reviewers teach the system by approving and correcting real decisions. Adjuda applies that precedent to similar future work, and drops back automatically when the evidence stops holding.
Adjuda does not need to own your model, prompts, or weights. It sits at the control point where an AI decision becomes a business action, applies your approval rules, and creates the audit trail.
Use it across vendors, internal models, agents, or deterministic systems.
Work in flight keeps the approval policy it started under.
Support maker-checker, four-eyes, senior reviewer, and escalation paths.
Control what Adjuda stores, for how long, and who can access the approval history.