HUMAN + AI Interaction Science Lab jhb

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Most businesses still rely on a single model trained on population data to predict customer behavior. While this may appear personalized, it often produces generalized outcomes. In this session, we explore the model-per-customer approach and how it enables truly contextual predictions by training models on individual interaction histories.

We’ll unpack the technical realities of operating millions of models, including real-time learning, cold start challenges, and scalable infrastructure required to support this shift in machine learning systems.

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Workers sit at the core of the ecosystem.Ai Prediction Platform, handling inputs, responses, and the flow of every interaction. In this session, we break down how worker architecture is structured and how its components fit together. From the underlying technology stack to integration, customization, and journey management, we’ll walk through how each layer works in practice. Expect a clear, practical view of how to design, manage, and scale workers effectively across real use cases.

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Generic marketing campaigns often fail to re-engage customers or drive meaningful action. In this session, we explore how behavioral insights and predictive models can identify loyalty members at risk of churn and deliver targeted messaging that encourages customers to return.

We’ll discuss how behaviorally informed SMS campaigns can be deployed through the ecosystem.Ai Prediction Platform to test messaging effectiveness, improve customer engagement, and generate measurable uplift.

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This webinar will be a hands-on, demo-led discussion on what it actually takes to generate a full-stack application that is deployable (without modification) across multiple infrastructure targets from a single architectural specification. We will cover environment parity, secrets management, containerisation strategy, cloud provider abstraction, and GitHub integration as architectural concerns rather than DevOps afterthoughts.