Pacific AI: Healthcare AI Governance, Validation & Monitoring

Pacific AI is the AI governance platform purpose-built for healthcare. One platform covers the full lifecycle of clinical AI, from policy to pre-release testing to production monitoring, so you do not stitch together three or four separate tools to govern, validate, and monitor the AI you build and buy.

Most governance tools give you forms and templates, then leave the analysis to your team. Pacific AI runs the work itself: it automatically tests models before release, monitors them in production, and drafts the model cards, vendor risk scores, and remediation controls that governance calls for. Your team reviews and approves, working from first drafts instead of blank templates. The platform scales with however much AI you run, from a single pilot to hundreds of systems in production.

Pacific AI is CHAI-certified, one of a small number of Certified Coalition for Health AI (CHAI) Assurance Resource Providers. Platform Core is free: unlimited AI systems, users, vendors, policies, documents, tests, and audit trails at no cost. You pay per credit only for AI-enabled governance actions, such as automated model card generation, vendor risk scoring, and compliance evidence collection. No contracts, no seat minimums. Billed through your AWS account.

You deploy Pacific AI inside your own AWS account, with the platform running in your VPC. You own the keys and the data, and nothing is transmitted to Pacific AI or any third party.

Key capabilities:

• Governor. Centralized AI registry that tracks every system across its full lifecycle, from proposal and analysis to operations and retirement, with risk assessment, vendor risk scoring, policy management, automated model card generation, and a full audit trail. The Policy Suite covers 250+ laws, regulations, and standards, refreshed quarterly, and maps to NIST AI RMF, ISO/IEC 42001, HHS HTI-1, ACA Section 1557, and EU AI Act requirements.
• Gatekeeper. Pre-release CI/CD testing and automated release-gating for generative, agentic, and traditional ML systems. Runs MedHELM benchmarks, developed with Stanford CRFM, for clinical usefulness, usability, and efficacy, and automatically tests for fairness, equity, safety, robustness, and clinical accuracy before any model reaches production
• Guardian. Continuous, automated post-deployment monitoring for accuracy drift, fairness drift, safety degradation, and behavioral change across production LLMs and clinical AI systems.

Clinical and safety categories

Pacific AI covers 10 clinical and safety categories, spanning 60+ test suites. Every category runs both as a pre-release test in Gatekeeper and as a continuous drift monitor in Guardian:

• Clinical Decision Support: appropriateness and safety of AI-generated clinical recommendations
• Clinical Note Generation: accuracy, completeness, and hallucination rate of ambient and generative documentation
• Patient Communication & Education: clarity, safety, and cultural appropriateness of AI-generated patient-facing content
• Medical Research Assistance: accuracy of literature synthesis, citation grounding, and evidence summarization
• Administrative & Workflow Support: accuracy and reliability of AI supporting administrative and operational tasks, such as coding, prior authorization, scheduling, and intake
• Social & Demographic Biases: disparities by race, gender, age, and socioeconomic status in model outputs, across 11 sociodemographic dimensions
• Medical Cognitive Biases: systematic clinical reasoning errors, such as anchoring and confirmation bias, reflected in model outputs
• Robustness: performance stability under input variation, edge cases, and distribution shift
• General Red Teaming: adversarial probing for harmful, unsafe, or out-of-policy outputs
• Medical Red Teaming: healthcare-specific adversarial testing, including clinical misinformation, unsafe medication guidance, and diagnostic errors