Know before your users do.
Structured evaluations and regression capture. Evidence travels with every release.
What the dashboard looks like.
Same tests. Every release.
Define once. Run forever. No drift in what gets checked.
Failures become permanent tests
Regression Bank turns real incidents into test cases. They re-run before any change ships.
Every unclear call sharpens the test.
Reviewer feedback tightens rubrics automatically. The system gets better every cycle.
Weeks earlier than your users.
Quality signals surface degradation before anyone notices. Not after.
Four steps. No gaps.
Define, run, review, export. No handoffs. No missing context.
- 1Step 1Define your suite
Pick a template, attach a rubric, and version the dataset and prompts together.
- 2Step 2Run before every release
Evaluate candidate models against your suite, including known regressions from past incidents.
- 3Step 3Review the exceptions
Uncertain or policy-sensitive cases escalate to human review with full context attached.
- 4Step 4Export the evidence
Generate signed reports and evidence packages for compliance, security, and release approvals.
Failures become your test suite.
Every incident feeds the next run. Versioned. Visible. Permanent.
Replay failures. Block repeats.
Illustrative: Regression Bank turns escapes into replayable tests. When configured, it can gate deployments on program-defined thresholds and publish evidence into your dashboards.
Evaluation that stays consistent.
Calibrate judges and reviewers against a shared rubric so scores are stable across time and teams. Capture confidence, route exceptions, and keep the evidence attached to each run.
Judges stay calibrated.
Confidence bands + human concordance stay attached to every run.
Bring your last failure. We'll turn it into a test.
Ship with gates that explain themselves.
Provider routing, bias checks, and evaluation harness loops stay attached to the same run timeline. The decision is visible. The receipts are exportable.
Provider gates & cost guards
Budget-aware routing with a paper trail.
provider_gates:
region: US
allow: [balanced, on-prem]
cost_guards:
budget_credits_per_day: 2200
max_p95_ms: 350
tokens_per_day_m: 3.2
decision:
provider: balanced
est_cost: 102
status: passPreview an evaluation workflow
Pick a scenario, inspect the workflow shape, and see what evidence gets attached. Values and prompts are examples.
AuraOne SDK
// Illustrative pseudo-code
await auraone.evaluations.run({
suite: "safety-redteam",
models: ["modelA", "modelB"],
gates: ["safety", "cost", "latency"],
});
// Measure:
// - refusal / policy adherence
// - cost envelope
// - latency budget
// Optionally: block promotion when a gate fails.Compare two models on a red-team suite and review evidence side-by-side.
Regression Bank
Guardrail replay (example)
Attribution Analysis
Token-level impact on classification
Domain Labs
Specialized evaluation environments
Model Performance
F1 Score vs Latency over 24h