Our studio actively investigates the boundaries of verifiable AI, focusing on problems where current methods fail. We formulate hypotheses, design empirical tests, and publish our findings, including unresolved challenges. This iterative process allows us to continuously refine our understanding of consequence-based risk classification and continuous control probing in production environments.
We document tested hypotheses and open vectors, detailing current blockers. This iterative process allows us to continuously refine our understanding of consequence-based risk classification and continuous control probing in production environments. Our research underpins the reliability of our deployed infrastructure.
Redacted Engineering Documents
Excerpts from our failure-mode registers and control matrices provide concrete examples of our engineering discipline. These documents are living records, continuously updated with new findings from our verification pipelines.
Failure Mode Register
Control Matrix
Operational Telemetry
Maps critical controls to specific system components, specifying continuous probing schedules and verification metrics.
Categorizes system failure modes by consequence, detailing detection methods and mitigation strategies.
Visualizes real-time performance data, including p95 latency and incident counts, from deployed systems.
Propose a Technical Research Engagement
Our team is available for discussions on specific engineering challenges or research collaborations.