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Real time systems generate a constant stream of events, yet most AI models are still designed to run offline and produce reports rather than actions. This gap between prediction and decision is where many production systems fail. Developers often have accurate models but no safe, reliable way to use them in live environments. This talk focuses on how to design AI driven decision systems that act on live events with low latency and high reliability. It explains how models connect to event streams, APIs, and rule engines, and how decisions flow through real software systems. The emphasis is on architecture and system behavior rather than algorithms or math. Using real examples from large scale retail and marketing platforms, the session highlights common failure modes such as delayed signals, noisy data, unstable actions, and lack of explainability. It then shows practical design patterns like guardrails, decision thresholds, rollback strategies, and continuous monitoring that allow AI systems to operate safely in production. The ideas and patterns discussed translate directly to WebRTC, telephony, and real time communication systems where decisions such as routing, prioritization, or optimization must happen quickly and predictably. Attendees will leave with a clear reference architecture and practical guidelines they can apply to their own real time systems.
AI is automating cognitive work faster than most organisations realise, but the gap between what the technology can theoretically do and what it's actually doing in practice is still enormous. This talk looks at where that gap sits today, what current labour market research says about how it's closing, and which parts of cognitive work are genuinely at risk versus which parts remain distinctly human. Grounded in real deployment experience and recent research, not forecasts.
We put real-time transcription and a language model into a live call's media path to assist agents. This talk covers the architecture — media forking, streaming speech-to-text, model-driven suggestions — the latency war that decides whether any of it is usable, and the honest list of things that didn't work.