Human Data
The data your AI needs to do the work.
Experts create or review it, contributors capture it with consent, or source owners license records of real work on terms agreed per program.
Expert correctionAccepted
Model output
Product.objects.all()Corrected
Product.objects.select_related('category').prefetch_related('reviews')
Enterprise Intelligence
Send the work. Get it back finished and checked.
Bring one recurring workflow. Exceptions are reviewed against your quality standard.
How it connects
Different work. One connected production system.
We source or create the right inputs, prepare the work, apply the appropriate checks and deliver the agreed result. Where permissions allow, feedback informs what to improve next.
Source or create
Experts create or review the work, contributors capture it with consent, or a source owner licenses records on terms agreed per program.
Prepare and build
Our team prepares the material and builds it into the specified product. Privacy and use terms are agreed per program.
Check and deliver
The checks that fit the work are applied, and the accepted result is delivered with its review record.
Improve the next cycle
Where permissions allow, feedback informs what to create or check next. It never rewrites what was accepted.
What the work can become
- Task
- A clearly specified job with a defensible way to check the result.
- Demonstration
- A qualified person shows how to do the job and explains the important decisions.
- Private evaluation
- A versioned set of tasks used to measure a specified capability. Access is scoped per program.
- Environment
- A resettable setting in which an agent can act and be scored on the outcome.
Workflow walkthrough
From operating history to a data product
- SourceA software company licenses a slice of its resolved support tickets for AI training and evaluation.
- PrepareIdentifiers are removed under the agreed privacy policy; excluded material stays out.
- BuildResolved tickets become tasks, with the accepted resolution used as the check.
- DeliverThe prepared slice is delivered with its use terms after the source owner approves the release.
Inspect the work
See what the work becomes.
Explore the correction, the illustrated task, and the structured result.
Showing Expert AI Quality example.
One record underneath
Keep the correction. Improve the next attempt.
Most systems save the result. AuraOne keeps the work that produced it.
One reviewed correction, kept. The miss: Product.objects.all() loads each product's related records one query at a time. The correction: Product.objects.select_related('category').prefetch_related('reviews'). Under task specification SWE-AGENT-TASK-v1.0, an approved correction like this can become a regression case, and a conditional path carries it toward the next version.
Approved corrections are banked in your regression suite so future candidates are tested against every known failure. When you are ready to fine-tune or post-train, verified examples package cleanly with full provenance and your team controls every release.
An accepted result is the record. Next-cycle feedback comes later. It shapes what we create next and never rewrites what was accepted.
Proof
Inspect the evidence before you start.
- One reviewed sample in fullThe task, the correction, and the review behind it.
- How a correction becomes a testHow an approved correction can join a regression suite you configure.
- Security and data governanceHow customer data, access controls, and review environments are isolated.
- Case studiesHow teams run expert data, evaluation, and enterprise workflows with AuraOne.
Bring the work. Start with a clear scope.
Human Data
Expert, speech, physical-world and licensed data for your AI, with checks and use terms agreed per program.
Discuss a data programEnterprise Intelligence
One workflow your team repeats, sent to us and returned finished and checked.
Discuss an enterprise workflow
Have data to license? Not ready yet? Inspect the work or see how AuraOne connects it.