AuraOne / Human Data

Qualified people create, review, and improve the data AI systems use.

Qualified people create, review, and improve the data AI systems use, with training, rights, quality, and delivery evidence attached.

AI teams that need specialist-created labels, rankings, evaluations, speech, demonstrations, or reviewed examples.

Brief a Human Data program See Workforce

Input: A defined task, required expertise, data rights, and quality rules.
Output: A scoped dataset or evaluation record with quality and delivery evidence.

Capabilities

Enter through a concrete job and decision.

Cleo

Find and screen specialists for a defined program.

Open Cleo

Workforce

Build and operate a qualified specialist bench.

Open Workforce

Annotation

Create, rank, correct, and review training examples.

Open Annotation

Voice

Collect consented, license-scoped speech and voice data.

Open Voice

Robotics

Collect, clean, structure, and deliver human demonstrations for robotics.

Open Robotics

Aura Capture

Join smartphone and wearable human-demonstration capture programs.

Open Aura Capture

From input to finished Human Data output.

Bring the work and requirements.

A defined task, required expertise, data rights, and quality rules.

AuraOne runs the workflow with clear ownership.

Recruit, qualify, train, assign, review, adjudicate, and document.

Receive a usable result and complete handoff.

A scoped dataset or evaluation record with quality and delivery evidence.

Operating flow

Each checkpoint moves the work forward with a clear owner, review, and next step.

01
Define the work
State the task, eligible expertise, source material, intended use, rights, and acceptance criteria.
Program brief and rights scope
02
Qualify people
Match specialists to the work and retain qualification, identity, consent, and training records.
Qualification and consent record
03
Create or review
Assign work with visible instructions, rubric, compensation terms, and escalation paths.
Task, rubric, work product, and timing
04
Check quality
Review examples, adjudicate disagreement, record feedback, and route rework without hiding failures.
Quality review and adjudication
05
Deliver
Package accepted work with source, rights, quality, version, and handoff records.
Delivery manifest and acceptance

What you receive

Human Data program deliverables

Your program keeps source material, criteria, decisions, approvals, and final outputs together.

Program: Purpose, task, owner, expertise, intended use, and scopeApproved program brief
People: Qualification, training, identity, consent, and roleContributor and reviewer records
Work: Source, instructions, rubric, output, timing, and escalationTask and work-item history
Quality: Checks, disagreement, adjudication, feedback, and reworkVersioned review record
Delivery: Accepted files, rights scope, version, checksums, and ownerDelivery manifest

What your team can do next

Connect the completed work to the decision, handoff, or release it supports.

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
Specialist work is fit for review	Qualified people complete a defined task under visible instructions and terms.	Qualification, task assignment, rubric version, work product, and escalation history.	Best fit: programs with explicit expertise, task, and review requirements.
Data is accepted for the agreed use	Reviewers apply quality criteria, adjudicate disagreement, and record rework.	Review results, adjudication, rights scope, acceptance, and delivery manifest.	Delivery includes the agreed dataset, use, quality criteria, and acceptance details.
Program history is retained	People, work, rights, quality, and delivery remain separate but linked records.	Immutable events, versions, source references, owners, and final handoff.	Retention is configured around the customer's data policy and program needs.