Expert AI Quality
Experts find what your model gets wrong. You get the fixes.
Open Expert AI QualityAuraOne / Human Data
The data your model can't find on the internet.
Qualified experts create and grade the data your model needs.
The best models now require data that cannot be scraped from the internet.
- Input
- Tell us what your model needs to learn.
- Output
- A reviewed dataset you can train on today.
The quality system
We build around the work.
We don't just find specialists. We build the quality system around their work. The data ships. The history stays useful.
When the next model cycle exposes a new failure, the record tells you what worked before and what to create next.
Every delivery carries the review history and the rights governing how the data can be used.
Capabilities
Enter through a concrete job and decision.
Voice AI
Every voice your product will ever hear.
Open Voice AIPhysical AI
Teach machines how the world actually moves.
Open Physical AIData Partnerships
License the data you already have. Capture the data nobody has.
Open Data PartnershipsSynthetic Research
Design-partner programTurn expert research into a market you can keep asking.
Open Synthetic ResearchFrom input to finished Human Data output.
Input
Bring the work and requirements.
- Tell us what your model needs to learn.
Work
AuraOne runs the workflow with clear ownership.
- Experts create the examples. Reviewers check the work and settle disagreements.
Output
Receive a usable result and complete handoff.
- A reviewed dataset you can train on today.
Operating flow
Each checkpoint moves the work forward with a clear owner, review, and next step.
01
Define the work
Tell us what needs judging and who is qualified to judge it.
Program brief and rights scope
02
Qualify people
We find the specialists and keep the record of why they qualify.
Qualification and consent record
03
Create or review
Experts do the work. They can see the rubric and what they are paid.
Task, rubric, work product, and timing
04
Check quality
A second reviewer checks the work. Disagreements go to an adjudicator.
Quality review and adjudication
05
Deliver
Accepted work ships with review history and the program rights that govern its use.
Delivery manifest, review history, and acceptance
What you receive
What your team receives
The completed work, the information needed to review it, and a clear next step stay together.
- Program
- The task, the owner, and what counts as doneApproved program brief
- People
- Who did the work and why they qualifyContributor and reviewer records
- Work
- The instructions, the rubric, and the work itselfTask and work-item history
- Quality
- Every check, disagreement, and correctionVersioned review record
- Delivery
- The files, the review history, and the program rights governing their useDelivery manifest and governing agreement
What your team can do next
Connect the completed work to the decision, handoff, or release it supports.
| Outcome | Work | What you receive | Program fit |
|---|---|---|---|
| The work is ready to review | Qualified people complete a defined task under instructions they can see. | Qualification, task assignment, and the work product itself. | Best fit: programs with explicit expertise, task, and review requirements. |
| The data is accepted | Reviewers apply your criteria and settle every disagreement on the record. | Review results, adjudication, and the delivery manifest. | Delivery covers the agreed dataset, its permitted use, and the acceptance criteria. |
| The history survives | The people, the work, and the delivery stay linked as separate records. | Immutable events, versions, and the final handoff. | Retention is configured around the customer's data policy and program needs. |