Doubt gets a human. Immediately.
When confidence drops or policy requires a second look, the right reviewer is already queued — with the run, rubric, and history attached.
Only the right people review the hardest cases.
Calibration scores, domain tags, and availability determine who gets the task. No guesswork.
Automation handles the easy calls.
When confidence is high, it moves. When it isn't, a human does.
See who acted. When. Why.
Escalations are logged. Ownership is visible. Timelines are clear.
Every review leaves a record.
Evidence follows the task automatically. No separate logging.
Platform preview
See who is assigned, and why.
Reviewer assignment is driven by calibration scores, domain tags, and availability. The panel below is illustrative.
Illustrative UI. Actual reviewer pools, scores, and coverage depend on your program configuration.
How it works
How it decides who reviews what.
Policies decide when humans lead. Calibration decides which humans. Evidence stays attached either way.
See how AuraOne chooses the right solver.
Hover through scenarios to see how automation and human expertise work together.
Automation handles routine requests
Automation escalates to a reviewer
Human review by default
When confidence drops or the task is high-stakes, AuraOne routes the work to a calibrated reviewer with full context and evidence attached.
Truth-first note
The UI above is illustrative. Routing behavior and metrics depend on your policy, data, and review design.
- Step 1Define routing policy
Set thresholds and categories: what can run autonomously, what must be reviewed, and what always escalates.
- Step 2Calibrate the workforce
Use golden sets, peer review, and feedback loops so reviewer quality stays measurable over time.
- Step 3Route with context
When a task escalates, the reviewer sees the run, rationale, rubric, and history in one place.
- Step 4Tighten the loop
Outcomes feed back into rubrics and gates, so the next release ships with stronger protection.
Teams
Domain specialist teams
Organize reviewers by domain, calibrate standards, and route work with clarity.
Communities built for precision.
Experts grouped by domain, calibrated over time, and routed into work when confidence drops or policy requires a second set of eyes.
Medical AI Alliance
Curated roster
Clinical annotation, safety review, documentation workflows (as applicable)
Code & Infrastructure
Bench-ready capacity
Autonomous debugging, infrastructure monitors
Creative AI Collective
Calibration loops
Synthetic data, multimodal review, tone alignment
Research & Ethics
Policy-aware review
Evaluation design, bias review, policy
Advancement Tiers