I'm building Quantlix, a runtime control plane for AI systems.
Most tooling focuses on training, fine-tuning, or deployment. In practice we've found many failures happen at runtime when requests reach the model.
Quantlix sits inline in the request path and evaluates requests before execution. It can enforce:
• schema contracts • policy rules • budget limits • retry amplification controls
Every decision produces a structured enforcement log.
I'm currently looking for feedback from people running models in production.
Curious what people think.