If a regression sneaks through, AuraOne replays it, verifies the fix, and drops the guardrail in The Regression Bank—no late-night scramble required.
Production failures trigger adaptive shields that expand, ripple, and permanently block recurrence. The bank updates in real time—deployments gate on shield state without human intervention while Grafana stays in sync.
After each replay, AuraOne slips the guardrail into production with zero manual work.
Ship knowing the failure is archived as protection, not folklore.
The Regression Bank turns history into protection.
Six tools. One platform. Complete confidence.
The teacher who never sleeps. Grades every conversation, 24/7.
Friendly hackers. Finding weaknesses before anyone else does.
Safe playground. Agents run tools in isolation.
Drift detection. Know if your model is memorizing instead of learning.
Continuous improvement. New data feeds training automatically.
Centralized definitions. Everyone uses the same ingredients.
Explainability. Know exactly why the AI said that.
Multi-turn agent harnesses. Provider cost and latency governors. Calibrated judges with confidence bands. Bias-by-default, attached to every run.
Confidence bands + human concordance stay attached to every run.
AuraStoryline
Each stage reacts to live telemetry, adapting gradients, motion, and metrics as AI Labs validates your models in real time. Reduced-motion preferences gracefully shift to static storytelling.
Regression Bank mirrors every production edge case, replaying traffic through deterministic harnesses before it reaches end users.
signals streaming
+68% coverage vs baseline
RLAIF validators, red-team agents, and golden sets combine into a blended score that blocks regressions automatically.
pass threshold
+2.4% RLAIF lift
TrustScore™ routes unresolved failures to certified reviewers, enriching training queues without flooding teams.
triaged daily
0 unresolved escalations
Safe builds roll forward automatically while shielded routes isolate risk. Everything stays observable in under 300 ms.
avg response
-41% latency drift
Regressions blocked
Faster RLHF iterations
Verified workflows
Continuous improvement
RLHF preference runs, active learning queues, and Regression Bank guardrails trade signals in real time— keeping your models honest long after the first launch.
Cleo drops preference job. TrustScore™ verifies IAA.
Uncertainty sampling refills queues automatically.
Escaped failures become new training data.
Apple suspended AI News. CNET published fake articles. Replit wiped databases.
Prevention costs less than remediation. Always.
Every deployment gates on evaluation. Zero manual intervention. No regressions escape.
Developer merges to main branch
AI Labs runs regression suites automatically
RLAIF, anti-overfit, red-team checks execute
Ship with confidence or prevent disaster
Plus evaluation templates for specialized domains:
Simple API. Powerful results.
TypeScript SDK. Python SDK. GraphQL native.
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
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: passPick a scenario, preview the SDK call, and review the expected impact. Every run is optimized for performance and respects motion preferences.
Use AuraOne.evaluations.run to execute the "safety-redteam" suite against anthropic.claude-3-opus and openai.gpt-5.1. Measure: - Harmful completion score - Latency - Cost per 1K tokens Enable auto-blocking when harmful completion exceeds threshold.
Compare Claude 3 Opus against GPT-4.1 on safety red-team prompts.
Live guardrail replay
Token-level impact on classification
Specialized evaluation environments
F1 Score vs Latency over 24h