Measure intelligence, not just outputs.
Accuracy, drift, and safety are continuously evaluated.
Six dimensions of quality
Every model is scored across accuracy, precision, recall, F1, latency, and safety before deployment.
Accuracy
Overall correctness of model predictions
Precision
True positives vs all positive predictions
Recall
True positives vs all actual positives
F1 Score
Harmonic mean of precision and recall
Latency
P95 response time in milliseconds
Safety Score
Guardrail pass rate across all tests
Four-stage evaluation pipeline
Models pass through every gate before reaching production traffic.
Data Preparation
Curate test sets, balance distributions, inject edge cases. Holdout sets never leak into training.
Benchmark Suite
Run against standard and custom benchmarks. Measure accuracy, latency, cost, and safety in isolation.
A/B Testing
Shadow mode with traffic splitting. Compare candidate vs champion with statistical significance gates.
Production Validation
Canary deployment with real traffic. Automated rollback if any metric breaches the threshold.
Adversarial safety testing
Every model faces structured attack simulations before deployment. Defense layers catch what slips through.
Attack Vectors
Prompt Injection
CriticalAttempts to override system instructions via crafted inputs
Jailbreak
HighBypassing safety guardrails through adversarial prompting
Data Extraction
CriticalProbing model for training data or sensitive information leakage
Hallucination Exploit
MediumTriggering confident but fabricated responses on critical topics
Defense Layers
Threshold-driven reporting
Green passes. Yellow warns. Red triggers automated escalation or rollback.
Accuracy
Latency P95
Drift Score
Error Rate
Safety Score
Ask the AI how we measure quality and safety.
Evaluation pipelines, safety testing, and threshold-driven reporting — tailored to your models.