We develop AI systems designed to remain beneficial even under adversarial conditions, distribution shift, and edge cases. Safety is not a feature, it's an architectural principle.
"The question is not whether AI will be powerful, but whether it will be controllable. We build for the latter."
TeraSystemsAI Safety CharterFour pillars that guide every system we build, every model we deploy, and every decision we make.
Multiple independent safety layers ensure that no single point of failure can compromise the system. We assume adversarial conditions and design accordingly.
Our systems are trained to understand and respect human intentions, not just optimize narrow metrics. We use constitutional AI and RLHF to maintain alignment under pressure.
Every model undergoes rigorous red-teaming and adversarial testing. We actively search for failure modes before deployment, not after incidents occur.
We publish model cards, safety evaluations, and incident reports. When systems fail, we share what went wrong so the entire field can learn and improve.
Adversarial input detection, prompt injection defense
Hard-coded behavioral boundaries and refusal patterns
Multi-stage content classification and fact-checking
Escalation triggers, uncertainty thresholds, audit trails
Real-time anomaly detection, kill-switch capability
Our five-layer safety framework ensures that even if one defense fails, multiple independent systems prevent harmful outputs. No single point of failure.
We actively try to break our own systems before deploying them. Our red team operates with full adversarial mindset.
Systematic attempts to bypass safety filters through adversarial prompts, jailbreaks, and context manipulation.
Testing for confabulation under pressure, edge cases, and adversarial queries designed to elicit false confidence.
Probing for demographic biases, stereotyping, and differential treatment across protected categories.
Attempting to unlock hidden capabilities through multi-turn manipulation and context engineering.
Testing resistance to training data memorization and privacy-violating information retrieval.
Evaluating robustness under out-of-distribution inputs, novel domains, and temporal drift.
These are not aspirations, they are binding operational principles that govern every deployment decision we make.
"AI must never be the last responsible actor."
The Accountability Invariant, TeraSystemsAIWhether you're deploying AI in healthcare, finance, or critical infrastructure, we can help you build systems that stay safe under pressure.