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AuraOne / Model Improvement

Your model, taught by your best people.

Find the failures. Turn expert corrections into training signal. Train the next candidate. Make every release prove it got better without reopening failures you already fixed.

Bring a model you already have, or a workflow that needs one. AuraOne builds the evaluation data, training signal, task-specific candidate, and regression suite required to improve it.

Improve a modelBuild a task model
Input
Your task, current AI system, production failures, and approved examples of correct results
Output
An improved task system, measured comparison, and a production deployment path scoped to your requirements

Start here

Two ways to start.

Bring the AI system you already operate, or bring a workflow that needs a model. AuraOne coordinates the orchestration from evidence through a scoped candidate and deployment decision.

Path A / Existing system

Improve an existing model

Bring the existing model or API, task, known failures, criteria, and examples of correct results. The outcome is a demonstrably stronger candidate against the agreed evaluation set.

  • Evaluate the current model against the task and known failures
  • Create expert corrections, preferences, and training signal
  • Train a candidate and compare it with the baseline under locked criteria
  • Retain regressions and support a scoped release decision
Improve a model

Path B / Defined task

Build a model for a defined task

Bring a workflow, accepted outcomes, failure cases, and a measurable quality bar. The outcome is a versioned model specialized for one defined job.

  • Define the input/output boundary and accepted result
  • Prepare the task dataset, grader, reward signals, and held-out evaluation set
  • Train or fine-tune a task-specific candidate where the evidence qualifies
  • Regression-test, version, and deploy through an approved path
Build a task model

Bring the task and the evidence. Get the next candidate.

We start from the failures that matter, turn expert judgment into training signal, train or fine-tune where the task qualifies, then prove the next version against the baseline.

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

We do

Build against the failures you already see.

  • Establish a baseline against representative production examples
  • Diagnose failures and choose the right data, model, and training path
  • Create corrections, preferences, graders, and reward signals
  • Train candidates, evaluate them, and regression-test the task-specific version

We deliver

A measured path to production.

  • A candidate model version with quality and failure deltas against the baseline
  • Held-out evaluation, regression history, and release evidence
  • A scoped endpoint or approved integration path when deployment requirements are met

Measure → Create signal → Train → Prove

The lifecycle behind a better task model.

These are not six unrelated evaluation features. They are the evidence and training 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.

  • BenchmarksTask-specific test sets built from representative work.
  • Failure taxonomiesNamed 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 correctionsAccepted answers with rationale and adjudication where needed.
  • Preference dataWhich output was better, and why.

Train

Use the accepted outcomes and reward signals to specialize a candidate for the task.

  • SpecializationFine-tune or train a task-specific candidate where data rights and scope support it.

Prove

Make the candidate earn release by passing held-out evaluation and known-failure checks.

  • Regression suitesEvery known failure remains a permanent test.
  • Blind comparisonCandidate versus baseline under the same locked criteria.

Task-model product

A dedicated model for one job.

AuraOne starts with a bounded task and its accepted result, not a promise to create a general-purpose enterprise model. These are representative workflow patterns, not customer case studies.

Menu structuring

Input
PDF / image / source menu
Output
Accepted structured menu object
Model learns
Items, modifiers, categories, prices, and exceptions

Support resolution

Input
Customer case + policy + account context
Output
Approved resolution / next action
Model learns
Policy application, escalation, and exception handling

Document review

Input
Document + review criteria
Output
Structured findings / decision record
Model learns
Task-specific classification, extraction, and judgment patterns

Code task

Input
Repository context + issue
Output
Validated change
Model learns
Task execution under repository-specific acceptance criteria

Scope and boundaries

You bring the task. AuraOne runs the improvement program.

You do not need to choose an RL algorithm, write a training loop, manage accelerators, select adapter ranks, or operate checkpoints yourself. AuraOne determines the data, grader, training method, evaluation, and deployment path required for the scoped task.

Supported methods are selected during scope.

Training, fine-tuning, and reinforcement learning are options only when the task, baseline, data volume, rights, and quality bar make them appropriate.

Qualification required

Rights follow the model and contract.

Model weights or a checkpoint, derived artifacts, and downstream rights are defined in the applicable scope; customer ownership is not assumed universally.

Terms defined in scope

Deployment is a scoped operating decision.

A candidate can be served behind a scoped endpoint or approved integration only after security, systems, and production requirements are established.

Approved path required

From a failed answer to a test that never expires.

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 become permanent regression cases

Output

Receive tests, not opinions.

  • Benchmarks and evaluation datasets
  • Regression suites that run on every release
  • 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.

  1. 01

    Baseline

    Capture current performance on representative examples and agree the measures that matter.

    Current system and baseline record

  2. 02

    Find failures

    Experts score and classify errors so the task, data, model, or workflow change is explicit.

    Failure taxonomy and source cases

  3. 03

    Correct

    Create accepted answers, preferences, grader signals, and reward data with rationale.

    Corrections, preferences, and reward signals

  4. 04

    Train

    Train or fine-tune one or more task-specific candidates where rights, volume, and method support it.

    Training run and candidate model

  5. 05

    Prove

    Run candidates against held-out evaluation and regression suites, then compare with the baseline.

    Blind comparison and regression results

  6. 06

    Deploy

    Approve a version and serve it through the scoped endpoint or approved enterprise integration.

    Model version and deployment record

  7. 07

    Learn

    Production failures return to the retained regression and training record for the next version.

    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
Versioned task-specific candidate with base-model selection and training run recordScoped training run
Comparisons
Blind rankings, held-out deltas, and rationalesBlind comparison runs
Deployment
Scoped endpoint or approved integration, release manifest, and rollback pathDeployment record
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.

Same checks
Select a criterion to inspect its source and decision effect.
Required checkCandidate ACandidate B
Review requiredEvidence ready
Blocking issueEvidence ready
Review requiredReview required
Evidence readyEvidence 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 blocking itemCandidate A: Known regressions
Launch review summaryCandidates, checks, sources, blockers, approvals, and rollback
  1. 1

    Locked contextSame criteria, same slice, same measurement version.

  2. 2

    Visible differencesDeltas and blockers stay readable by criterion.

  3. 3

    Source linkageEvery result opens the run and the sample behind it.

What the work lets you put into production

Each outcome depends on the scope your program agreed to and the evidence your system can supply.

What the work lets you put into production. Each outcome depends on the scope your program agreed to and the evidence your system can supply.
OutcomeWorkWhat you receiveProgram fit
Improve a production taskUse current failures and approved corrections to build a better version for the specific job.Current baseline, candidate version, and measured quality deltas.Improvement applies to the task, data slice, and criteria covered by the program.
Prove the next versionRun 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.
Choose a deployment pathPrepare the improved system for AuraOne Managed, scoped task-model, or private operation as scoped.Deployment plan, release manifest, owner, and rollback path.Production availability depends on integration, security, data rights, and deployment requirements.
Keep improving as failures appearPromote 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.

Enterprise Intelligence runs and captures one recurring workflow. Model Improvement turns the accepted outcomes, corrections, and failures into a stronger task-specific candidate when the task qualifies.

See Enterprise Intelligence