Experts find what your AI gets wrong. You get the corrections.
Evaluatewith qualified judgment
Explore Expert AI QualityThe work behind the result
Most systems save the result. AuraOne keeps the work that produced it.
What we make
Pick the problem you actually have.
- Expert AI Quality
- Experts grade your model against criteria you control. You get the corrections.Evaluation data and regression sets
- Voice AI
- Every voice your product will ever hear. Licensed, transcribed, reviewed.Licensed speech, transcripts, and metadata
- Physical AI
- Trained operators record the demonstrations your robot needs.Versioned robotics datasets
- Enterprise Intelligence
- Turn one repeated workflow into a governed AI system that improves with every accepted result.The work, the judgment, and the proof
The system remembers what worked.
Define the job once. Run it with control. Keep every accepted result and approved correction working for the next version.
01
Define
Agree on the input, output, systems, volume, and definition of a correct result.
Versioned workflow brief
02
Build
Version the prompts, models, context, tools, policies, review rules, and acceptance contract behind the work.
Immutable workflow version
03
Prove
Run it against real work and compare the result with the current process.
Baseline comparison
04
Deploy
Run it through a hosted worker with governed actions, durable human exceptions, and a delivered result.
Trace, receipts, review, and delivery
05
Improve
Turn rights-cleared failures and approved corrections into regression cases that every new version must pass.
Incident, replay, regression, and release history
Two connected pillars
Create proprietary AI data. Turn repeated work into intelligence.
Human Data produces the examples, evaluations, voice, and physical AI datasets. Enterprise Intelligence starts with one customer workflow and earns automation from accepted outcomes.
Human Data
Qualified experts create and grade the training data your model needs. You get the data and the reasoning behind it.
- Input
- Tell us what your model needs to learn.
- Output
- A reviewed dataset you can train on today.
Enterprise Intelligence
AuraOne works with design partners to define one recurring enterprise workflow and the evidence required before any production operation or model work is committed.
- Input
- Show us one recurring workflow and the result it needs to produce.
- Output
- A scoped workflow plan and evidence requirements. Only a signed agreement creates implementation or operating obligations.
What you actually get
Every engagement starts with a deliverable, an acceptance bar, and an owner.
| Outcome | Work | What you receive | Program fit |
|---|---|---|---|
| Your model fails and you cannot say why | Experts grade outputs against your criteria and settle the disagreements. | Reviewed labels, expert rationales, and reusable regression data. | Scoped to the agreed task, expertise, and acceptance criteria. |
| Your voice product misses real speech | Contributors record and reviewers evaluate speech under a program rights policy. | Licensed audio, transcripts, and reviewed failure examples. | Scoped to the agreed collection, rights, and delivery path. |
| Your robot has never seen the task | Trained operators perform the task while capture systems record it. | Reviewed episodes, manifests, and checksums. | Depends on hardware qualification, operators, and capture conditions. |
| Your team repeats the same expensive work | We build the AI for one recurring workflow, then run it for you or scope a connection to your systems. | A named workflow, baseline comparison, and managed or scoped task-model delivery path. | Enterprise Intelligence starts with a real workflow and a design partnership, not a prebuilt vertical catalog. |
Input → work → review → accepted outcome → memory
One accepted record, simplified.
This anonymized specimen follows the structure of AuraOne's agentic software-engineering JSONL sample.
Representative proof artifact
The result and the work stay together.
The sample stops at the accepted outcome and the memory that makes it reusable.
record SWE-REPRESENTATIVErepresentative specimen · accepted
- Input
- Scoped development repository and development database only. Identify the actual source of input-size-dependent database-query growth.
- Work
- The product-list queryset does not eagerly load relationships that the serializer accesses for every returned product, so query count scales with result size.
- Review
- Author revised the assertion before acceptance.
- Accepted outcome
- API response contract preserved. Query behavior bounded with respect to product count. Relevant tests pass.
- Memory
- SWE-AGENT-TASK-v1.0. SWE-AGENT-v2.2. Reviewer rationale and revision history. Production contributor rights and downstream model-use rights would be governed by the applicable agreements.
Derived from AuraOne_AgenticSWE_Sample.jsonl. synthetic_representative_sample. Identities redacted. Representative specimen only; not a claim of a delivered customer engineering dataset.
- 1
Accepted outcomeThe reviewed thing the customer receives.
- 2
MemoryWho did it, what was asked, what changed, why it passed, and the governing rights.
Feedback
When next-cycle feedback comes back → it shapes what we create next.
Feedback is what later acceptance, rejection, corrections, or failure signals tell AuraOne to improve next. It is a later program input, not a field invented on this sample.
The compounding asset
Production work makes the system better.
The inputs, corrections, tests, and accepted result stay connected.
- Failures become examples
- A wrong output and its approved correction become a concrete case the next version must handle.Production failure record
- Examples become tests
- The new case joins the regression set so the same mistake is visible across versions.Versioned regression record
- High-quality examples can improve the model
- Where rights, volume, and quality support it, approved examples can specialize the task system.Approved training or adaptation scope
- Deployment stays explicit
- Your team chooses AuraOne Managed or a scoped task-model deployment when the production boundary is ready.Deployment decision record
Start a program
Start with the AI problem in front of you.
Tell us what data your AI system needs or which recurring workflow your enterprise already performs. AuraOne will carry that context into the project brief.
1. Product2. Objective3. Brief