Bring us
Your existing AI and the job it must do.
- The task, inputs, outputs, and systems around it
- Examples of current outputs and the mistakes that matter
- Approved examples of what a correct result looks like
AuraOne / Model Improvement
Find the failures. Build the evidence for the next version.
Send a model and a task. AuraOne evaluates failures, adds human corrections, and verifies whether the next candidate should ship.
Corrections become evaluation sets. Regression history shows whether each supplied candidate improved.
- Input
- Your existing system, the job it must do, and approved examples of correct work
- Output
- A locked evaluation set, regression history, and a measured comparison
- 5Accepted corrections
- 5Regression cases
- v4Candidate scored
- REG-101Refund above policy limitPolicyBaselineFailCandidatePass
- REG-102Legal threat in ticketEscalationBaselineFailCandidatePass
- REG-103Quote the right plan tierWrong factBaselinePassCandidatePass
- REG-104Keep the account ID privateData exposureBaselineFailCandidatePass
- REG-105Reply in the customer's languageLanguageBaselinePassCandidatePass
Baseline2/5
Candidate v45/5
Criteria v3 · the release call stays yoursCANDIDATE_PASSED
Bring the task and the evidence. Get a defensible release decision.
We start from the failures that matter, turn expert judgment into evaluation and training-ready evidence, then compare a candidate you supply against the baseline.
We do
Build against the failures you already see.
- Establish a baseline against representative production examples
- Diagnose failures and identify the data and evaluation path
- Create corrections, preferences, graders, and reward signals
- Evaluate supplied candidates and regression-test the task-specific version
We deliver
A measured path to production.
- Quality and failure deltas for a candidate you supply
- Held-out evaluation, regression history, and release evidence
- A training-ready evidence package with documented data-rights boundaries
Measure → Create signal → Prepare → Prove
The lifecycle behind a better task model.
These are not six unrelated evaluation features. They are the evidence stages that move a task from baseline to a defensible release decision.
Measure
Lock the task, baseline, dataset, and quality bar before changing the model.
- Benchmarks Task-specific test sets built from representative work.
- Failure taxonomies Named failure modes linked to the source cases.
Create signal
Turn expert judgment into records a candidate can learn from and a buyer can audit.
- Expert corrections Accepted answers with rationale and adjudication where needed.
- Preference data Which output was better, and why.
Prepare
Package accepted outcomes and reward signals for a customer or separately approved model provider.
- Training-ready evidence Corrections, preferences, graders, and rights boundaries prepared for a separately scoped training decision.
Prove
Make the candidate earn release by passing held-out evaluation and known-failure checks.
- Regression suites Accepted failures can be added to the regression bank.
- Matched comparison Candidate and baseline scored under the same criteria.
Integration & governance
Built to plug into your model stack and release pipeline.
AuraOne connects to the models and training pipelines you already run—turning production failures and human expert corrections into reproducible evaluation suites and release gates.
Evaluation, human signal, and release gating
Included in every engagement.
- Task benchmarks Representative evaluation sets built from real workflow cases
- Expert corrections Adjudicated human fixes, preferences, and rubric scoring
- Regression memory Confirmed failures promoted into permanent test suites
- Release evidence Criterion-by-criterion comparisons for ship or hold decisions
Works with your weights, APIs, and training stack
Flexible model execution.
- Model comparison Evaluate proprietary checkpoints, fine-tuned weights, or commercial API models side by side
- Dataset exports Export SFT, DPO, and reward-model datasets directly into your training infrastructure
- Post-training Coordinate dedicated fine-tuning or RL post-training runs when specialized weights are needed
- CI/CD gates Enforce automated regression gates in CI/CD before any candidate is promoted to production
From a failed answer to a regression case.
The failure, the correction, and the reason stay attached to each other.
Input
Bring your model and your standards.
- The candidate models and the task they have to do
- What a good answer looks like, and who decides
- The failures you already know about
Work
Experts grade. We keep the record.
- Qualified reviewers score against versioned criteria
- Disagreements go to an adjudicator, not an average
- Accepted failures can be added as regression cases
Output
Receive tests, not opinions.
- Benchmarks and evaluation datasets
- Regression suites reusable against every new candidate
- Preference and correction data with agreed rights
How a model gets better
Each step has an owner, a record, and a reason it can block the next version.
01
Baseline
Capture current performance on representative examples and agree the measures that matter.
Current system and baseline record
02
Find failures
Experts score and classify errors so the task, data, model, or workflow change is explicit.
Failure taxonomy and source cases
03
Correct
Create accepted answers, preferences, grader signals, and reward data with rationale.
Corrections, preferences, and reward signals
04
Train
A customer or separately approved provider may train a candidate using the accepted evidence package.
Customer- or provider-supplied candidate record
05
Prove
Run candidates against held-out evaluation and regression suites, then compare with the baseline.
Matched comparison and regression results
06
Deploy
The customer decides whether to release a supplied candidate through its separately approved production path.
Customer release decision
07
Learn
Customer-supplied production failures can return to the retained regression and correction record for the next evaluation.
New failure, correction, and version history
What you receive
What arrives in an improvement delivery
Your contract sets the exact scope. This is the shape of a typical delivery.
- Benchmarks
- Evaluation datasets with scoring criteria and versionsAccepted expert judgments
- Failures
- Named failure categories linked to their source casesReviewed failure taxonomy
- Regressions
- Reusable cases with per-version pass and fail historyRegression suite record
- Training signal
- Expert corrections, preference data, grader/reward design, and accepted workflow outcomesAdjudicated expert work
- Candidate comparison
- Versioned measurements for a customer- or provider-supplied candidateSupplied candidate and evaluation run
- Comparisons
- Matched rankings, held-out deltas, and rationalesMatched comparison runs
- Rights
- Model/checkpoint ownership and downstream rights defined for the chosen base model and contractApplicable scope
Inspectable evidence
A ranking is not a decision.
Every criterion opens the run behind it, the threshold it used, and the examples that failed.
Interactive walkthrough
Compare two candidates under one locked launch check.
Select candidates and a launch criterion to see where each release is ready, needs review, or remains blocked.
| Required check | Candidate A | Candidate B |
|---|---|---|
| Review required | Evidence ready | |
| Blocking issue | Evidence ready | |
| Review required | Review required | |
| Evidence ready | Evidence ready |
Criterion inspector
Known regressions
- Required evidence
- Replay of retained failures under the same candidate
- Source
- Regression Bank replay record
- Decision effect
- A blocking retained failure holds the candidate.
- 1
Locked contextSame criteria, same slice, same measurement version.
- 2
Visible differencesDeltas and blockers stay readable by criterion.
- 3
Source linkageEvery result opens the run and the sample behind it.
What the evidence lets you decide
Each outcome depends on the scope your program agreed to and the evidence your system can supply.
| Outcome | Work | What you receive | Program fit |
|---|---|---|---|
| Prepare evidence for a better version | Use current failures and approved corrections to document what a better version must fix. | Current baseline, candidate version, and measured quality deltas. | Improvement applies to the task, data slice, and criteria covered by the program. |
| Prove the next version | Run the candidate against locked criteria and regression cases before release. | Per-criterion comparison, failed cases, and an approval record. | Coverage reaches only the examples and failure modes represented in the suite. |
| Make a release decision | Use the comparison evidence to accept, reject, or revise a customer-supplied candidate. | Approval record, failed cases, owner, and required follow-up. | This decision does not create a hosted endpoint, integration, or production operating commitment. |
| Keep improving as failures appear | Promote new failures and approved corrections into the regression history for later versions. | Versioned correction records and regression history with source examples. | The improvement loop depends on continued access to representative failures and approved corrections. |
Connected product path
Start with a workflow, or improve the model already doing it.
Walk through a complete improvement cycle—from isolating a failure mode and collecting expert corrections to comparing candidates against the release gate.
See Enterprise IntelligenceFoundry evaluation suites
Prefer a sealed suite to a custom build?
Foundry suites package reviewed evaluation work by domain, with baselines shown and sealed content kept sealed. Multi-model baseline comparison across sealed evaluation suites.
Support triage — 1,200 tasks
Baseline resolution rate 61%.
Document review — 2,500 tasks
Baseline extraction accuracy 74%.
Workflow automation — 800 tasks
Baseline completion rate 52%.