Expert training data for AI. Finished work for enterprises.

Models are abundant. Trusted work is not.

Expert training data for AI labs. Finished, checked work for enterprises. Every result carries its record.

Programs run as named design partnerships.

Explore Human Data Bring us a workflow

Expert data programs

Lawyers, clinicians and analysts answer hard questions and grade AI answers. A second expert checks every point.

Explore Expert AI Quality

Human Data

The examples your model cannot scrape.

Specialists create the examples your AI needs.

Action: stylized hand carrying the mug mid-move above the tray — Three separate kinds of work — expert judgment, speech, and physical tasks. An overview, not one dataset.

Explore Human Data

Enterprise IntelligencePilot

Start with the work your team repeats.

Bring one workflow. We define the quality bar and agree a scope together.

Supplier photo: unbranded matte stoneware bowl, illustrative — Illustrative example — not a customer result.

Illustrative example — not a customer result.

Explore Enterprise Intelligence

Inspect the work

See what the work becomes.

Explore the correction, the illustrated task, and the structured result.

One correction, reviewed.

One reviewed fix: scattered lookups become one bounded batch.

Task Fix the /api/products/ list-performance regression

Before N+1/N+M pattern

After Bounded set of queries

Reviews relationConcept illustration. Shapes are not measured query counts.

Corrected queryset

Product.objects.select_related('category').prefetch_related('reviews')

Fix is correct and the loading strategy is right for each relation type.
Reviewer conclusion

Created to demonstrate the delivery schema; not an expert-authored production record. Representative specimen only; not a claim of a delivered customer engineering dataset.

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.

Representative specimen. Code from the reviewed sample in the work explorer.

An approved correction can become a regression case under the agreed scope, so later versions have to pass it. Using approved examples to improve a model is a separate decision. It needs the rights, volume, and quality to support it, it is agreed in scope, and your team decides when anything ships.

An accepted result is the record. Next-cycle feedback comes later. It shapes what we create next and never rewrites what was accepted.

See the sample Read the full review

Proof

Inspect the evidence before you start.

Bring the work. Start with a clear scope.

Human Data
Expert-created examples for your model, with review and rights connected.
Start a Human Data brief
Enterprise Intelligence
One workflow your team repeats, scoped into a program.
Bring an Enterprise workflow

Not ready yet? Inspect the work or see how AuraOne connects it.