Why AI Misses Business Context & How Teams Fix It

What this post added

This post elaborates on the challenges of providing accurate business context to AI models, emphasizing the critical role of the surrounding infrastructure (the context layer) over the AI model itself. It details common failure modes such as stale data, noise, contradictory sources, and context window decay. The post defines the key functions of a context layer: separating systems of record from query layers, retrieving relevant material (not just more), and serving it quickly. It then explains how Redis, and specifically Redis Iris (comprising Redis Context Retriever, Agent Memory, Data Integration, LangCache, and Search), addresses these challenges by providing a real-time context engine that enables fast, low-latency access to continuously updated business state, supporting hybrid retrieval and semantic caching for AI workloads.