Guardian: 360° Testing & Monitoring for Generative AI Systems

Test for accuracy, safety, bias, privacy, and robustness across your development and production environments.

Testing for Bias of Large Language Models in Clinical Applications

Data Science Research Director at Children’s Hospital of Orange County

You can’t assume fairness. You have to test for it — by swapping genders, names, or cultural cues and tracking how the model’s response shifts.

Louis Ehwerhemuepha, Data Science Research Director at Children’s Hospital of Orange County

Meet Your Guardian Agent

Guard your models with 360° testing for accuracy, performance, robustness, fairness, safety, and ethics

Detect, debug, and mitigate risks before they reach your users

Monitor for drift in production systems across all safety aspects

Comprehensive, Real-World Testing for Generative & Agentic AI

Define multiple test suites per AI system.
Reuse dozens off-the-shelf test datasets and benchmarks, or upload your own.
Integrate directly in CI/CD pipelines.
Publish results & metrics in your system’s model card

Compare outcomes. Detect drift. Strengthen trust

Run and compare tests against multiple LLM endpoints and configurations.
Visually drill down to debug why results differ across runs.
Configure pass/fair rules for CI/CD builds or production monitoring alerts.

Monitor, Measure, Mitigate:
Master your AI risks.

Monitor continuously Comply confidently with Real-time monitoring that turns compliance into continuous assurance.
From deployment to diligence: oversight enables trust Detect drift, bias, and safety issues early to maintain regulatory and clinical integrity.
Schedule your monitoring jobs With Pacific AI Guardian Configure live testing & red teaming rate to minimize inference cost & latency in production.

Identifying and Mitigating Bias in AI Models for Recruiting

Katie Bakewell

Data Science Solutions Architect at NLP Logix

Jason Safley

Chief Technology Officer at Opptly

We apply LangTest in two stages: during training, and every time we generate a match list in production. It gives us real-time fairness validation.

Katie Bakewell, Data Science Solutions Architect at NLP Logix

The Brains: Combining Three LLM
Evaluation Engines

LangTest: Automated Evaluation of Custom
Language Models

LangTest, built by Pacific AI, can automatically generate and run 100+ test types, focused on evaluating the fairness and robustness of large language models. It supports testing common tasks like question answering, summarization, and classification across all major LLM models and APIs.

MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks

MedHELM, built by Stanford’s Center for Research on Foundation Models, is a framework for assessing LLM performance for medical tasks, comprising a taxonomy with 5 categories, 22 subcategories, and 121 distinct real-world clinical tasks as well as 35 distinct benchmarks (14 private, 7 gated-access, and 14 public). Pacific AI is the first commercial implementation of MedHELM, making it easily usable by a larger audience.

Red Teaming: Ensuring General & Medical Safety

The Red Teaming engine performs adversarial safety testing across 50+ categories and subcategories. It covers general-purpose safety risks (jailbreaking, prompt injection, illegal activity, …), healthcare-specific safety risks (consent, patient privacy, conflicts of interest, …), and medical cognitive biases (anchoring, availability bias, confirmation bias, …).

With Pacific AI, we embedded policies, guardrails, human-in-the-loop patterns, and benchmarks directly into our pipeline. That’s what allows us to keep innovating safely.

Tal Amitay, VP of Engineering, Brook Health

We’ll make sure you’re successful. Pacific AI’s Governor includes a Kickstart Project to help you deploy privately on AWS or Azure, onboard your first AI system and vendor with risk assessment, and train your team for self-sufficient use.