Blind expert grading
Reviewers apply your criteria without knowing which model produced what.
Task, rubric version, and expert rationaleAuraOne / Human Data / Expert AI Quality / Design partner
Experts find what your AI gets wrong.
Qualified reviewers grade your model against criteria you control. You get the corrections.
You know the score dropped. You do not know which answers got worse, or why.
- Input
- Model outputs, test cases, criteria, and expertise requirements
- Output
- Reviewed judgments, rationales, scorecards, and evaluation data
Evaluation program
A score is not an answer.
Start from the decision you need to make. Then find the people qualified to make it.
Independent quality review
A second reviewer approves the work, returns it, or rejects it.
Reviewer, disposition, and lifecycle historyAdjudicated disagreement
When two experts disagree, a named adjudicator decides. No averaging it away.
Competing judgments, adjudication rationale, and final decisionReusable quality data
Accepted judgments become datasets, scorecards, and permanent regression cases.
Accepted records, source links, and delivery definitionFrom a bad answer to a permanent test.
The output, the criteria, and the judgment stay connected.
Input
Define what good means.
- Model outputs, tasks, conversations, or test cases
- Required expertise, blind conditions, and evaluation criteria
- Quantity, deadline, and delivery format
Work
Grade it. Then check the grading.
- Qualified experts grade against a versioned rubric
- A separate reviewer checks quality and routes rework
- Adjudicators resolve disputed or conflicting judgments
Output
Get corrections you can train on.
- Criterion-level labels and expert rationales
- Adjudicated disagreements and failure classifications
- Scoped JSONL or CSV datasets, scorecards, and regression cases
How an expert evaluation program operates
Four steps. Each one has an owner and a record you can inspect.
01
Brief
Define the task, the rubric, and the expertise it takes to judge. Acceptance criteria and delivery are agreed up front.
Customer-approved program specification
02
Grade
Assign qualified experts and collect criterion-level judgments with rationales.
Worker assignment, rubric version, grade, and rationale
03
Review
Run independent QA, request rework, and adjudicate disagreements under named ownership.
QA disposition, adjudication, and audit history
04
Deliver
Package accepted data and evidence in the format and scope agreed with the customer.
Stored artifact, manifest, acceptance, and billing receipt
Delivery boundary
What an evaluation delivery can contain
Your contract sets the exact scope. This is the shape of it.
- Judgments
- Criterion-level labels, rankings, scores, and rationalesAccepted expert grades
- Quality
- QA dispositions, rework, disagreement, and adjudicationVersioned review history
- Analysis
- Scorecards, failure categories, and blind comparisonsApproved program aggregation
- Data
- Scoped JSONL or CSV records and reusable regression casesCustomer delivery specification
What the work lets you decide
No dataset, payout, model improvement, or production availability is implied until a program completes its contracted path.
| Outcome | Work | What you receive | Program fit |
|---|---|---|---|
| Compare model candidates | Run blinded criteria against the same task set and adjudicate material disagreements. | Per-criterion results, rationales, and the final scorecard. | The comparison applies to the scoped test set and rubric version. |
| Build a failure dataset | Classify accepted failures and retain hard cases for later evaluation runs. | Failure labels, source cases, and regression membership. | Reuse depends on the customer's data rights and retention policy. |
| Prepare post-training data | Select accepted judgments and rationales that meet the program's delivery criteria. | Approved records, stated exclusions, and the delivery manifest. | Training, fine-tuning, and model-performance gains are separate scopes. |