Built from Real-World AI Failure Analysis

FailureModes.ai was built from a simple observation: the hardest AI failures are not always visible in benchmarks, demos, or generic eval scores. They appear when models are connected to real users, enterprise data, tools, workflows, and production constraints.

The founders have spent years working across hyperscaler-scale AI infrastructure, enterprise AI systems, and frontier model deployment workflows. That experience shaped a practical view of AI reliability: teams need to understand not only whether a model performs well, but how it fails, where those failures appear, and what controls are needed before deployment.

Modern AI systems fail in recurring patterns. They hallucinate when evidence is missing. They misuse tools when workflow boundaries are unclear. They drift across long contexts. They expose data when permissions and retrieval are not aligned. They regress after model upgrades. They pass narrow evals while failing in production.

FailureModes.ai exists to help teams make those patterns visible. We help enterprise AI teams detect, classify, monitor, and mitigate failure modes in LLMs and agents. The goal is to turn hard-won operational knowledge into a system that improves reliability, safety, and trust.

FailureModes.ai is the continuous diagnosis, critique, and improvement layer for enterprise Agentic AI — built by people who have shipped, debugged, and improved AI systems at the frontier.