AuraOne / Enterprise Intelligence / Improve existing AI
Make the AI you already run measurably better.
Bring an existing model or agent and the task where it underperforms. AuraOne builds the evaluation first, then works failures into candidates you can verify.
Design-partner program. Improvement is a scoped engagement measured against an evaluation suite — not a promise to retrain or fine-tune anything.
- You bring
- The existing system, the task it misses, and evidence of the failures
- AuraOne builds
- The evaluation suite and the regression record that proves or rejects each candidate
- You decide
- What ships — every ship, hold, or reject call keeps its evidence
The improvement cycle
Evaluation first. Then candidates. Then your call.
Improvement items name the failure. Evaluations define the bar. Regression candidates carry each proposed fix; regression suites make sure a fix for one failure does not reopen three others.
From named failure to a verified candidate
01
Name what is failing
You bring the model or agent, the task where it underperforms, and the evidence that shows it. We open improvement items for each named failure.
Improvement item record
02
Build the evaluation
Before anything changes, the task gets an evaluation suite built from your acceptance bar — historical, edge, adversarial, tool, human, cost, and latency evidence.
Evaluation suite
03
Produce candidates
Each improvement produces a candidate: a prompt revision, a tool-binding change, a context package, or a model variant — judged against the suite, not anecdotes.
Regression candidate
04
You decide what ships
A candidate that clears the suite becomes a regression case the next version must pass. The ship, hold, or reject decision and its evidence stay attached to the record.
Ship / hold / reject decision
The compounding data loop
The system remembers what worked.
Accepted results become proof. Approved corrections become tests. The next version begins where the last one finished.
Design-partner program.
- 01
Customer workflow
The repeated job, inputs, rules, examples, and acceptance bar.
- 02
Governed execution
The approved workflow version runs with permissioned context, models, tools, budgets, and action policies.
- 03
Human exception
Uncertain or sensitive cases pause durably for qualified review, QA, or adjudication before the same run resumes.
- 04
Delivered outcome
The result ships with its manifest, trace summary, exceptions, rights scope, and acceptance standard.
- 05
Acceptance + economics
Accept, reject, or request rework. Contract-defined acceptance can create usage, invoicing, and expert earnings exactly once.
- 06
Incident + replay
Failures and rejections retain the evidence needed to replay the run without repeating external side effects.
- 07
Regression memory
Rights-cleared corrections and production failures become permanent cases the next version must pass.
- 08
Release gate
Historical, edge, adversarial, tool, human, cost, and latency evidence produces a ship, hold, reject, or rollback decision.
Program boundary
What model improvement is today
Design-partner program. No claim here says we continuously train or fine-tune your model; each improvement is a scoped, evidence-gated decision.
- Improvement items
- Each named failure or target becomes a tracked item with owner, evidence, and statusImprovement item record
- Evaluations
- Suites built from your acceptance bar judge every candidate before a decisionEvaluation record
- Regression
- Accepted fixes become permanent regression cases the next version must passRegression suite
- Candidate types
- Prompt, tool-binding, context, or model-variant changes — each proposed with its evidenceCandidate record
- Deployment
- Where a task-specific model is in scope, its endpoint and rollout plan are separately decidedScoped endpoint decision
- Your model
- AuraOne does not take ownership of, retrain, or fine-tune your model without a signed scope that says soWritten boundary