Certify Your Entire AI Lifecycle
Governance
Does your organization have policies and controls in place to manage risk across the AI system lifecycle?
learn howTesting
Is your model tested for safety? Accuracy? fairness? Can you show test results for each version?
learn howMonitoring
Is your deployment safe, stable, unbiased, and delivering results for its user base?
learn how
Pacific AI
There’s growing regulation on how AI systems should behave, but few tools that can prove they do. Pacific AI packs truly novel technology for testing Generative AI in an easy-to-use, fast-to-market package.
AI Governance, Risk, and Compliance
The Challenge
You’re looking to get to market fast and scale your AI initiatives quickly.
Doing it legally isn’t easy: 26 US States have AI laws. There are 9 competing standards for Responsible AI. Regulators keep publishing industry-specific guidance. Things change every week.
You need a solution that keeps track of all of this for you, maps all the upcoming laws to one list of requirements, updates your policies, and provides you with one risk management and governance process.
Let Us Do It For You
Track AI Legislation
Update a Unified Set of Controls
Train or Run Your AI Governance Team
Track Regulator Guidelines
Write and Update Your AI Policies
Provide Templates for Audit & Governance Artifacts
Track AI Governance Frameworks & Standards
Annual Training For You
Provide Tools to Risk Manage an AI Portfolio
Certifying an Organization
AI System Testing
The Challenge
You need to prove to customers, regulators, and investors that your AI system is safe.
Proving means having a large test suite that validates everything from accuracy and fairness to factuality and privacy. Tests need to be created quickly and run automatically on every new version.
They also need to be human-readable – so that you can share them with regulators without exposing your trade secrets.
Prove Your Model is Production Ready
Accuracy
Bias & Fairness
Safety
Factuality
Robustness
Ideology
Representation
Privacy
Toxicity
Certifying a Model
AI System Monitoring
The Challenge
You need to prove that your system is safe as used in production.
A “good” model can still cause harm in practice – being unfair due to automation bias, unsafe due to outliers or selection bias, or inaccurate due to drift.
You’re liable to make sure that doesn’t happen. Just like AI model testing, this requires tools and controls beyond what traditional software systems need.
Prove that your system keeps working
Measure Outcomes
Usage & Engagement
Automation Bias
Concept & Data Drift
Outlier Detection
Error Rates
Selection Bias
Feedback Quality
Outcome Bias
The Production System Certification
We weren’t only looking for a third-party AI audit because it’s legally required. We were looking for a partner who would help us build our service the right way, and help our team stay compliant next year.
Get Certified