WHY AURAONE · VS SCALE AI

A second source you can defend under audit.

Scale AI proved you can buy expert human data at volume. Then the largest data vendor was absorbed by one of the labs it served, and the labs it sold to started looking for somewhere else. AuraOne is the neutral alternative: founder-led, lab-independent, and built to deliver data your team can keep. Every datapoint carries who made it, who reviewed it, and what rights come with it.

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Scale AI delivers the match · AuraOne delivers the data record

Independence

No lab owns us

Founder-led and neutral. The data we build for you isn't routed through a lab that competes for the same model.

Provenance

August 2026

The EU AI Act high-risk training-data rules enforce in August 2026. 78% of teams can't validate their training data and 77% can't trace its origin. Ours ships traceable.

Coverage

Expert, not generalist

Coding and agent tasks, law, finance, medicine, and robotics — authored by people who do the work, with the rubric and golden answers attached.

Head to head

What Scale AI delivers. What you can prove afterward.

A fair comparison starts with what the other vendor already does well. The question that decides the deal is what survives an audit, and who owns the model in the end.

Managed annotation

Scale AI

Sells you a finished batch

Real strength: managed annotation and expert data at volume, with the operations to run large programs. The questions buyers now ask are who owns the result, whose pipeline it ran through, and what record ships with it.

Managed annotation

Sells you a finished batch

Vendor

Ownershipdelivered batchvendor-held

Independencelab-alignedsingle source

Chain of consentnot attacheduntraced

Handoff filenot includedmissing

Audit fileassembled laterafter the fact

AuraOne Human Data

AuraOne

Sells you data you own

The specialist, the reviewer, the rubric, and the usage terms stay on one record. You get a dataset with source details and review notes your team can keep. When a case fails, it becomes a test that should not ship again.

AuraOne Human Data

Sells you data you own

Live

Ownershipyou keep ityours

Independencelab-neutralsecond source

Chain of consentattachedclear

Handoff fileincludedyou keep

Audit filebuilt inexportable

Same expert work · proof you keep

Why teams switch

Three reasons, and the move makes itself.

The first signs the move worked. Procurement, engineering, and your model-risk team all see the same review record.

Best for

You need a neutral second source

The team buys expert data at volume but no longer wants a single pipeline aligned with a lab it competes with. AuraOne runs alongside what you have today and gives you an independent source you can stand behind in front of procurement.

Switch signal

Ownership and provenance start to matter more than volume

The move is working when the question stops being how many labels and becomes who owns the data, who reviewed it, and can you prove its origin before August 2026. That takes a clear consent trail, not a delivery summary.

Time to value

Weeks, and your existing data comes with you

Week one we scope one workflow and bring your existing labeled data in. Week two to three we run the review and return the output. Week four you hold an eval-ready dataset and an audit file procurement can read.

The bottom line

Scale AI delivers the batch. We deliver data you keep.

A clear consent chain means every datapoint carries who made it, who reviewed it, and under what rights. Choose the source that's independent of the labs and hands you the review record.

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Hard case intake

Use this path when you want a neutral second source, not a pipeline aligned with one lab.

Bring your existing labeled data in — the first migration is one workflow, not a rebuild.

Start beside your current vendor, then expand once the first reviewed dataset proves out.