AuraOne / Model Improvement

Your model, taught by your best people.

Catch the failure once. Then make every release prove it will not happen again.

A score tells you the model changed. It does not tell you what got worse.

Start a program See Expert AI Quality

Capabilities

Six ways to stop shipping the same mistake.

Every failure your experts find becomes a test. The test runs forever. That is the whole idea.

Benchmarks

A test set built from your work, not a public leaderboard.

Versioned dataset and scoring criteria

Failure taxonomies

Every failure gets a name. Named failures are the ones you can fix.

Labeled categories and source cases

Regression suites

Every release runs against every failure you have already seen.

Per-case results across versions

Blind comparison

Reviewers rank outputs without knowing which model produced them.

Blind labels, rankings, and rationales

Preference data

Which answer was better, and why. Written down by someone who knows.

Paired judgments with rationale

Specialization

A model trained on the outcomes your team already accepted.

Approved training records and baseline

From a failed answer to a test that never expires.

The failure, the correction, and the reason stay attached to each other.

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

Experts grade. We keep the record.

Qualified reviewers score against versioned criteria
Disagreements go to an adjudicator, not an average
Accepted failures become permanent regression cases

Receive tests, not opinions.

Benchmarks and evaluation datasets
Regression suites that run on every release
Preference and correction data you own

How a release gets checked

Each step has an owner, a record, and a reason it can block the release.

01
Inventory
Name the model, the version, the provider, and who owns it.
Model and version record
02
Evaluate
Lock the criteria, the data slice, and the candidate set.
Versioned evaluation definition
03
Compare
Read the deltas by criterion. Open the examples that failed.
Candidate comparison matrix
04
Improve
Route failures and preferences back into reviewed expert work.
Correction and training record
05
Release
Show the approvers, the checks, the blockers, and the rollback.
Approval chain and release manifest
06
Remember
Keep the regression. The next candidate has to pass it too.
Regression and audit history

What you receive

What arrives in a delivery

Your contract sets the exact scope. This is the shape of it.

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
Comparisons: Blind rankings, per-criterion deltas, and rationalesBlind comparison runs
Corrections: Preference pairs and corrected outputs with rationaleAdjudicated expert work
Release: Approval chain, blockers, and rollback pathRelease record

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.

Same checks

Candidate oneCandidate two

Select a criterion to inspect its source and decision effect.
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 work lets you decide

Each outcome depends on the scope your program agreed to.

What the work lets you decide. Each outcome depends on the scope your program agreed to.
Outcome	Work	What you receive	Program fit
Pick between two models	Run both against the same locked criteria and adjudicate the disagreements.	Per-criterion results, blind rankings, and final scorecard.	The comparison holds for the tested slice and criteria version.
Block a bad release	Run the regression suite. A failed case is a named blocker with an owner.	Failed cases, responsible owner, and required recovery.	Coverage is limited to the failures already in the suite.
Specialize a model	Train on the outcomes your own reviewers already accepted.	Approved training records, baseline, and measured acceptance.	Scoped separately once data rights, volume, and a baseline exist.
Explain a regression	Compare the failing version against the history the suite already holds.	Per-version pass and fail history with source examples.	Explanations reach as far back as the retained record.