About
I’m building Verdic Guard to address a common problem in production AI: LLMs perform well in demos but often drift when used across long, real-world workflows. Most teams rely on prompt tuning, retries, or post-hoc monitoring. These help, but they don’t clearly define or enforce what an AI system is actually allowed to do. Verdic Guard takes a different approach—treating AI output reliability as an engineering and validation problem. It focuses on defining intent and constraints upfront, then validating outputs before they reach users or critical systems. The goal is to help teams build AI systems that are predictable, auditable, and trustworthy, especially in high-risk or regulated environments.
Links
Badges
