Deploying large language models (LLMs) in healthcare requires more than high initial accuracy – it demands ongoing testing and monitoring to ensure safety, fairness, and compliance over time.
Pacific AI provides a comprehensive governance platform that supports both development and production needs. During development, test suites can be integrated into CI/CD pipelines so every model version is validated before release. Once live, continuous monitoring detects drift, performance degradation, or safety issues in production systems, helping organizations maintain trust throughout the full lifecycle of their AI.
To achieve this, Pacific AI combines three specialized test engines:
- MedHELM provides benchmarks designed by medical experts, grounded in real-world healthcare needs, and validated on real-world data. It focuses on whether LLMs deliver accurate, clinically useful answers when applied in practice.
- LangTest generates systematic variations of datasets to test dozens of bias and robustness dimensions. This ensures that models produce consistent and fair outputs across patient populations, edge cases, and wording changes.
- Red Teaming executes adversarial safety tests, covering 120+ categories of unsafe or undesirable behaviors. Using both semantic matching and LLM-as-a-judge techniques, it probes whether models comply with safety, policy, and compliance requirements.
Together, these engines provide comprehensive coverage of accuracy, robustness, and safety risks — supported by audit trails, role-based access, and versioned test suites.
Join us to see how Pacific AI helps organizations deploy and operate LLMs responsibly, with continuous assurance that models remain accurate, safe, and compliant.
