MA / RESEARCH 01
Beyond benchmarks: evaluating AI in the real world
Why static benchmarks miss the failures that matter in enterprise deployments.
8 min read
INDEPENDENT AI SAFETY & EVALUATION
Stress-test yourenterprise AI.
Your AI is confident. Is it reliable?
Automated red-teaming that uncovers hallucinations,
vulnerabilities, and risks—before they reach production.
RESEARCH-DRIVEN. MODEL-AGNOSTIC. BUILT FOR TRUST.
A RIGOROUS APPROACH TO
ENTERPRISE AI SAFETY Adversarial testing Hallucination detection Model interpretability Safety by design
ENTERPRISE AI SAFETY Adversarial testing Hallucination detection Model interpretability Safety by design
+ MEASURE WHAT MATTERS
Make the invisible measurable.
Find the failure modes before your users do.
Turn adversarial testing into actionable evidence.
Baseline model With ModelAuditFAILURE RATE (%)
ADVERSARIAL EVALUATION ROUNDSILLUSTRATIVE DATA · NOT A LIVE BENCHMARK
Think like an adversary.
Systematically probe your AI with attacks that evolve beyond static test sets.
01 / AUTOMATED RED-TEAMINGUnderstand every failure.
Go beyond pass or fail. Get clear, reproducible evidence of where your model breaks.
02 / DEEP EVALUATIONDeploy with confidence.
Build a repeatable safety standard for every model, update, and deployment.
03 / CONTINUOUS ASSURANCE+ FROM THE LAB
Research, not assumptions.
MA / RESEARCH 02
The anatomy of a prompt injection
A closer look at attack surfaces, failure patterns, and practical defenses.
6 min read
MA / RESEARCH 03
Measuring what your model doesn’t know
A practical framework for hallucination detection and calibrated uncertainty.
10 min read
EDITORIAL DRAFTS · Proposed research topics, not published study findings.
Better questions. Safer AI.
Let’s put your model to the test.