Streaming Data for Real-Time AI Applications

Chapter 1. Why Real-Time AI Needs Streaming Data

At its core, real-time AI is about immediacy; that is, delivering insights, making decisions, and adapting to new information as it emerges. This demand for immediacy stems from the fundamental nature of intelligence itself, which is the ability to respond to the world as it changes. Human cognition does not process events in fixed intervals; rather, people react to them as they happen. Similarly, real-time AI reacts instantly, drawing on extensive pretraining while adapting continuously to live conditions. In these systems, freshly retrieved information brings immediate relevance into the context window, either through mechanisms like retrieval-augmented generation (RAG) or by leveraging recent context. This fresh data enables timely, informed responses grounded in the most relevant available knowledge.

Unlike batch processing, which relies on delayed inputs, streaming data provides a continuous flow of up-to-the-moment information. This empowers organizations to act in real time, rather than waiting for insights that may already be outdated. Consider the following use case that involves a customer calling into a call center:

1. The customer begins a conversation with a support agent through live chat, or a phone call is transcribed into text in real time.

2. As the conversation unfolds, the system captures the most recent portion of the dialogue to maintain an up-to-date context window.

3. This context is transformed into a structured ...