LLMs are becoming part of real products now. They answer customers, summarize documents, write code, search internal knowledge bases, and make decisions inside workflows. But most LLM apps still have a quiet problem: We usually find the failure after the user has already seen it. A hallucinated answer gets reported by a customer. A prompt injection is discovered after logs are reviewed. A model starts drifting after a deployment, but the team notices only when the experience already feels unreliable. I built Failure Intelligence Engine , or FIE , to move that detection earlier. FIE is an open source system for real-time LLM failure detection. It can run as a lightweight Python SDK with no server, or as a full monitoring platform with shadow-model verification, ground truth checks, auto-correction, analytics, email alerts, and a dashboard. The goal is simple: Treat LLM failures as observable, diagnosable, and fixable runtime events.…