AuraOne AI Labs

The Brain Above the Workforce.

Quality Control for the entire platform.
Ship with certainty.

1,818
evals / minute
307ms
avg response
99.98%
success rate
10K+
regressions blocked
The Regression Bank

Guardrails ready before the next shift.

If a regression sneaks through, AuraOne replays it, verifies the fix, and drops the guardrail in The Regression Bank—no late-night scramble required.

10,847
guardrails active
0.5%
escape rate (industry 12%)
342
failures prevented this week
The Regression Bank

Generative shield. Ripple feedback. Zero escape.

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.

10,847 failures blockedGuard strength 82%Replay P95 15m
Active
Failures archived
10,847
Escape rate
0.5%
Replay P95
15m
Ripple threshold
82%
Ripple feedback activeSeverity spikes auto-blocked

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.

Evaluation infrastructure
that judges itself.

Six tools. One platform. Complete confidence.

RLAIF Validators

The teacher who never sleeps. Grades every conversation, 24/7.

Red-Team Generator

Friendly hackers. Finding weaknesses before anyone else does.

Agent Tool Sandbox

Safe playground. Agents run tools in isolation.

Anti-Overfit Harness

Drift detection. Know if your model is memorizing instead of learning.

RLHF + Active Learning Loops

Continuous improvement. New data feeds training automatically.

Feature Store

Centralized definitions. Everyone uses the same ingredients.

SHAP/LIME Explainability

Explainability. Know exactly why the AI said that.

AI Labs Upgrades

Evaluation that stays honest.

Multi-turn agent harnesses. Provider cost and latency governors. Calibrated judges with confidence bands. Bias-by-default, attached to every run.

Calibrated Judges
Production replay

Judges stay calibrated.

Confidence bands + human concordance stay attached to every run.

Judge confidence
91%
Human concordance
89%
Provider gates
$0.0008 / call
Bias sentinel
normal
Gates can block deploys and attach evidence automatically.
Judge confidence band
Score 92%
run telemetry
harness
multi-turn tool traces
judge
confidence + concordance
gates
cost + bias + SLO
API surface
POST /api/v1/labs/agent-evals
GET /api/v1/labs/agent-evals/:runId

AuraStoryline

Scroll the evaluation journey.

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.

Phase 01capture

Capture production reality

Regression Bank mirrors every production edge case, replaying traffic through deterministic harnesses before it reaches end users.

0/min

signals streaming

+68% coverage vs baseline

  • Autonomous log + eval ingestion across orgs
  • Deterministic replay harness synced to prod
  • Anomaly fingerprints stored in Glass Vault
Phase 02judge

Judge with synthetic certainty

RLAIF validators, red-team agents, and golden sets combine into a blended score that blocks regressions automatically.

0.00%

pass threshold

+2.4% RLAIF lift

  • Red-team automation across 10 attack families
  • Bias + toxicity scoring fused with INP
  • Golden set drift alarms streamed live
Phase 03escalate

Escalate only the unknown

TrustScore™ routes unresolved failures to certified reviewers, enriching training queues without flooding teams.

0 cases

triaged daily

0 unresolved escalations

  • Intent-aware inbox prioritises severity
  • Telemetry-linked dispute resolution
  • Label guilds auto-provisioned per domain
Phase 04redeploy

Redeploy with manufactured confidence

Safe builds roll forward automatically while shielded routes isolate risk. Everything stays observable in under 300 ms.

0.000 s

avg response

-41% latency drift

  • Progressive deploys with live rollback gates
  • GPU + CPU budgets linked to motion tiers
  • Storyline telemetry synced to dashboards
0

Regressions blocked

0%

Faster RLHF iterations

0

Verified workflows

Continuous improvement

Collect → Train → Redeploy without lifting a finger.

RLHF preference runs, active learning queues, and Regression Bank guardrails trade signals in real time— keeping your models honest long after the first launch.

RLHF Refresh

Cleo drops preference job. TrustScore™ verifies IAA.

92% IAA

Active Learning

Uncertainty sampling refills queues automatically.

Live Refill

Regression Bank

Escaped failures become new training data.

Zero Regression
The Regression Tax

Every regression costs
$200K to $3M annually.

Apple suspended AI News. CNET published fake articles. Replit wiped databases.
Prevention costs less than remediation. Always.

Industry Standard
12%
Regression escape rate
Without systematic prevention
With AuraOne
0.5%
Regression escape rate
24x better than industry
Cost Per Incident
$50K-$500K
Emergency patch cost
Engineering + downtime + trust erosion

The Economics of Prevention

Without AuraOne

12% escape rate
1 in 8 deployments causes issues
$200K-$500K per incident
Emergency patches + downtime
Trust erosion unmeasured
Customer churn compounds quarterly

With AuraOne

0.5% escape rate
10,847 regressions blocked automatically
$2M+ annual savings
Prevention vs remediation cost delta
Ship with confidence
Regression Bank blocks bad deploys before they happen

From commit to deploy.
Automatically protected.

Every deployment gates on evaluation. Zero manual intervention. No regressions escape.

1

Code Push

Developer merges to main branch

2

Auto-Eval Trigger

AI Labs runs regression suites automatically

3

Safety Gates

RLAIF, anti-overfit, red-team checks execute

4

Deploy or Block

Ship with confidence or prevent disaster

GitHub Actions Integration

name: Deploy with AI Labs
on: [push]
 
jobs:
eval-and-deploy:
runs-on: ubuntu-latest
steps:
- name: Run AI Labs eval
run: |
# Regression bank check blocks deployment
curl -X POST "$AURA_API/v1/labs/evals" \
-d '{"gates":{"noRegression":true}}'
- name: Deploy if safe
run: ./deploy.sh

Five minutes from
idea to evaluation.

eval.ts
import { AuraOne } from '@auraone/sdk';
 
const labs = new AuraOne.Labs();
 
// Your first evaluation
const result = await labs.evaluate({
model: 'production-v2',
suite: 'regression-bank',
confidence: 0.98
});
 
if (result.safe) {
await labs.deploy();
}
 
// ✓ You’re protected

Simple API. Powerful results.

TypeScript SDK. Python SDK. GraphQL native.

Ready to manufacture
confidence?

Join the teams shipping AI systems with certainty.

AI Labs · Policy Console

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.

exports: signed bundlesSee exports

Provider gates & cost guards

Budget-aware routing with a paper trail.

ready
Policy
credits
RegionUS
Daily budget2,200 credits
Estimated burn: 102 credits · 3.2M tokens/day
Latency governor350ms p95
Token volume3.2M
Providers
click to select
Decision
Policy satisfied.
Policy snapshot
See deploy 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: pass
AI Labs Playground

Run a full evaluation in seconds

Pick a scenario, preview the SDK call, and review the expected impact. Every run is optimized for performance and respects motion preferences.

AuraOne SDK

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.
suitesafety-redteam
modelsanthropic.claude-3-opus, openai.gpt-5.1
modeblocking
alertsslack://ai-incidents
Expected Outcome

Compare Claude 3 Opus against GPT-4.1 on safety red-team prompts.

Pass rate
94%+6%
Budget burn
$27-18%
Eval speed
00:58-35%

Regression Bank

Live guardrail replay

Prompt Injection #402
PASSED
PII Leakage #11
PASSED
Hallucination #89
FAILED
Tone Deviation #7
PASSED
Run Full Suite (10,482)

Attribution Analysis

Token-level impact on classification

ThepatientshowssignsofseverecardiacdistressdespitenormalBP
Positive Drivers
Negative Drivers

Domain Labs

Specialized evaluation environments

Select a lab to initialize

Model Performance

F1 Score vs Latency over 24h

+12.4%
F1: 0.94