Adversarial QA for production AI
Model Testing
Adversarial testing, capability benchmarking, and failure-mode analysis before your model reaches production.
Overview
What is Model Testing?
Model testing stress-tests AI systems with adversarial inputs, edge cases, and capability benchmarks to surface failure modes before they reach users — the QA layer between training and production.
Why it matters: Models that pass standard benchmarks still fail in production on edge cases, adversarial prompts, and distribution shift. Systematic red-teaming finds these failures while they're still cheap to fix.
Adversarial Testing
Structured red-teaming for jailbreaks and prompt injection
Capability Benchmarks
Custom benchmarks aligned to your model's target skills
Failure Mode Analysis
Root-cause categorization of every discovered failure
Regression Retesting
Verify fixes with targeted retest cycles before ship
Workflow
How We Do It
01
Threat Modeling
We map the failure modes and adversarial surfaces relevant to your model's deployment.
02
Test Design
Adversarial prompt sets and capability benchmarks built to your risk profile.
03
Red-Team Execution
Trained red-teamers probe the model for jailbreaks, bias, and capability gaps.
04
Failure Cataloging
Every failure is logged, categorized, and severity-scored for triage.
05
Report & Retest
Detailed report delivered with retesting after fixes are applied.
Case Study
AI Safety Team
AI Safety Team
Red-team a customer-facing chatbot before public launch
Solution
Structured adversarial testing across 12 risk categories with severity-scored reporting
Results
4K+
Adversarial prompts tested
180
Failure modes found
3 wks
Turnaround
Ready to Get Started with Model Testing?
Tell us about your project and we'll scope a pilot within 48 hours.