adBrain is a machine-learning company in the operational sense: where the right answer is a recommendation, a price, a ranking, or an action, we build and deploy the models that make that decision, and we make it in production, on live signal, online and offline.
This is a deliberate part of our positioning. We pick the right technique for the problem instead of forcing everything through a large language model, and we treat modelling as one part of a decision loop, not an end in itself.
What we deliver
Recommendation & ranking
We build recommenders that put the right product in front of the right customer at the right moment, including on-site recommendation that displays a frame with the correct product while a customer is searching online. The goal is relevance at the point of intent: better conversion, larger baskets, and a better customer experience. See it in practice in our online product recommendation project.
Next-best-action
Prediction only pays off when it drives a decision. We rank candidate actions (offers, messages, interventions, resource allocations) by propensity to achieve the outcome you care about. The system goes beyond scoring each customer to choosing what to do about them.
Price & discount optimisation
We model demand, elasticity, and margin to recommend optimal prices and discounts, online and at the point of sale. Our work for a printing house in Poland recommends discounts across offline and online channels to balance win probability against retained margin, so one of the few levers that moves conversion and margin is set by a model rather than by habit.
Demand, conversion & forecasting models
Forecasting, churn and conversion propensity, segmentation, and anomaly detection: the classic data-science toolkit, delivered into production rather than left in a notebook.
Data engineering for decisions
Most ML projects fail on the data, not the model. We build the pipelines, feature stores, and serving infrastructure that make models reliable in production, and that keep them accurate as your data changes.
How we approach a decisioning engagement
| Phase | What happens |
|---|---|
| 1. Frame | Turn the business question into a measurable objective and a success metric: revenue, margin, conversion, or accuracy. |
| 2. Data | Assess and prepare the data, and be honest about what it can and cannot support. |
| 3. Model | Build, validate, and compare models against a clear baseline. |
| 4. Decide | Wire the model into a decision (a recommendation, a price, a next-best-action) with guardrails and human override where it matters. |
| 5. Deploy | Put it into production in your environment, with monitoring and a retraining plan. |
| 6. Measure | Track the real-world metric through live experiments rather than offline scores, and iterate. |
One practice, online and offline
The same models that personalise an online storefront can recommend a discount at a print counter. adBrain works across both worlds (digital channels and physical operations), so the recommendation a customer sees online and the price an employee offers offline come from the same, consistent intelligence.
This capability is the connective tissue of adBrain’s decision loop. It draws on predictive & causal modelling for the estimates, real-time event processing for the live signal, and experimentation & optimisation to keep improving.