Gatekeeper: Automated LLM, ML, and Agentic AI Testing

Run test suites and build CI/CD release gates on real-world medical AI tasks, social and cognitive bias, red teaming, and regulatory compliance.

Testing for Bias of Large Language Models in Clinical Applications

Louis Ehwerhemuepha

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

Holistic Safety for Healthcare AI

Clinical Task Performance

Real-world benchmarks for clinical decision support, note generation, patient communication, and workflow administration.

Robustness & Bias

Detecting demographic bias and robustness against clinical data perturbations.

Continuous Red Teaming

Real-time adversarial loops for ethical violations, HIPAA breaches, and jailbreaking.

Medical Cognitive Biases

Identifying reasoning flaws like anchoring, confirmation, and availability bias.

Regulatory Hardening

Enforcing 2026 legal standards (e.g., California AB 489) for emergency escalation and preventing AI impersonation of licensed professionals

System Specific Goals

Build custom test suites and judging panels to match your specific clinical and business goals.

Benchmark results across versions, models, and environments with precision.

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

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.

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.

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, …).

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

Partnership for the AI Era

Whether you are evaluating your first high-impact AI system or scaling AI governance across the enterprise, Pacific AI provides the infrastructure, oversight, and expertise to move forward with confidence.

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