Making safe, reliable AI systems possible
Reliable and safe AI starts with system behavior that can be understood, measured, and continuously improved.
Quantiles supports the teams building applied AI systems. We provide the infrastructure to evaluate, monitor, and understand system behavior, so organizations can make evidence-based decisions about how AI is built, improved, and used.
Infrastructure for evaluations
Evaluation is a core layer of the AI development lifecycle, turning measured behavior into evidence teams use to build, improve, and deploy systems.
Shared evaluation foundations
We help teams measure AI behavior with transparent, reusable open-source infrastructure they can inspect, adapt, and extend.
Built for applied AI systems
Quantiles is designed for applied AI systems, where evaluation reflects the tasks, data, and deployment context behind production use.
The Team
We’re a team of engineers, researchers, and technical builders with experience across data, infrastructure, and applied AI. We’re working together to build evaluation tools that make AI systems easier to measure, understand, and improve.
Built by AI and technology experts from
Reliable AI systems depend on evaluation infrastructure that makes every model, prompt, and workflow change measurable, inspectable, and regression-aware.
Run an eval
Run lightweight checks or full-scale evals locally with the open-source Quantiles CLI, manually and with coding agents.