AI safety at TeraSystemsAI is treated as a governance discipline, not a research aspiration. Every system we deploy is designed for human authority, independent review, and documented accountability.
Four commitments that shape how we design, evaluate, and deploy every system.
Our systems are designed with multiple independent safety layers so that no single point of failure can compromise the system. If one layer is bypassed, the remaining layers are designed to contain the failure. This architecture is documented, reviewable, and tested prior to deployment.
Every system we deploy is designed to preserve human decision authority. AI provides recommendations and surfaces information. Final decisions, especially in high-stakes contexts, remain with qualified human operators. Override pathways are built into every workflow.
Before any system reaches production, it undergoes structured adversarial testing designed to identify failure modes, edge cases, and unexpected behaviors. Results are documented and reviewed by an independent team before deployment approval is granted.
Every decision an AI system contributes to is logged, traceable, and reviewable. We publish model documentation, maintain complete audit trails, and support third-party review. When incidents occur, we document root causes and corrective actions.
Structured input filtering and adversarial input detection
Documented operational boundaries and refusal conditions
Content classification, fact-checking, and consistency verification
Escalation triggers, uncertainty thresholds, decision audit trails
Anomaly detection, performance drift alerts, controlled shutdown
Our safety architecture is designed so that if any single layer is bypassed, the remaining layers are intended to contain the failure. No system relies on a single control to prevent harm.
We evaluate systems for known failure modes before they reach production. Results are documented and subject to independent review.
Structured attempts to bypass safety controls through adversarial inputs, unexpected formats, and edge cases. Results documented and reviewed before deployment.
Evaluating whether system confidence scores accurately reflect actual reliability. Systems that overstate confidence are flagged for recalibration before deployment.
Evaluating outputs across demographic categories for disparate treatment. We use documented benchmarks and report known limitations alongside evaluation results.
Testing resistance to unintended data leakage, training data memorization, and privacy-sensitive information disclosure across various query strategies.
Evaluating how systems perform when inputs differ from training conditions. Systems that degrade unpredictably are constrained to documented operating conditions.
Known failure modes, edge cases, and operational limitations are documented and communicated to deployment teams. No system ships without a known-limitations report.
These are documented operational principles that govern deployment decisions. They are maintained as policy, not aspirations.
"AI must never be the last responsible actor."
The Accountability Invariant, TeraSystemsAIWhether you are deploying AI in regulated industries or evaluating vendor safety practices, we welcome the conversation.