Creating safe AI at the
speed of innovation

We curate diverse, high-quality datasets that reduce bias and improve model precision for reliable AI outputs.

Expert annotators guide AI behavior through reinforcement learning and continuous feedback to align with human values.

Red teams stress-test AI systems with adversarial prompts and edge cases to identify vulnerabilities before launch and test how models behave under pressure and unexpected conditions.

We design escalation paths, monitoring workflows and human-in-the-loop (HITL) systems to catch and correct harmful or unintended actions.

We identify model vulnerabilities early and embed safeguards to prevent misuse, bias and downstream risks throughout the model lifecycle.

We implement safeguards for autonomous AI agents, ensuring reliable decision-making with appropriate human oversight.