A decision is only as fresh as the signal behind it. adBrain builds the real-time event layer that lets models decide from what a customer or system is doing right now (the search they just typed, the page they are on, the sequence of actions in this session) rather than a nightly batch that is already out of date.
What “real-time” actually means here
- Event capture: search terms, browsing context, clicks, and prior behaviour read as a live intent signal at the moment of decision.
- Streaming features: those events turned into model-ready features in-flight, so the model sees current context, not a stale snapshot.
- Low-latency serving: recommendations, rankings, and next-best-actions returned inside the request, fast enough to change what the customer sees before they move on.
- Feedback capture: the outcome of each decision (converted, ignored, clicked) captured immediately, so the system can learn from it.
Why it matters commercially
Relevance at the moment of intent is the highest-leverage point in the funnel. A recommendation shown while a customer is actively searching converts far better than one shown after they have left. The same holds for pricing, fraud, and operational decisions: the value of a decision decays quickly, so the signal has to be current.
This is the layer that makes the rest of the loop live. It feeds current context to predictive & causal modelling, lets machine learning decisioning act in the moment, and gives experimentation & optimisation the fast feedback that adaptive policies need to learn.
Built for production, in your environment
Like everything adBrain builds, the event pipeline is delivered into your own infrastructure: Docker-based services, your cloud or on-premises, with the source code handed over. See Deployment & Ownership.
You can see this capability at work in our online product recommendation project, where the model reads a customer’s live search signal and surfaces the right product in the moment.