Not every problem is a chatbot. Many are decisions: which product to show, which price to offer, which action to take next. adBrain’s machine-learning expertise builds systems that make those decisions in production and prove they worked. Our arc here is Predict · Decide · Prove.
Why this expertise is high-value
While generative AI takes the headlines, machine learning that moves revenue and margin remains in steady, high-value demand: recommendation, ranking, pricing, forecasting, and, increasingly, the causal measurement that separates genuine impact from correlation. Buyers have grown wary of models that score well offline but never move a real metric; the premium skill is delivering decisions into production and measuring their true lift. This is a mature, research-grade practice, expressed in commercial terms.
What we can do
- Recommendation and ranking that put the right product or option in front of the right person at the moment of intent, online and offline.
- Price and discount optimisation: balancing win probability against retained margin, so pricing is set by a model rather than by habit.
- Predictive modelling across propensity, next-best-action, and forecasting to estimate what is likely and prioritise accordingly.
- Causal measurement through incrementality and lift, so budget follows what genuinely worked rather than what merely correlated.
- Experimentation and adaptive optimisation: A/B and geo experiments and contextual bandits that keep decisions improving as conditions change.
How it shows up in a solution
When you discount by gut feel and leave margin on the table, this expertise puts a model behind the decision. When shoppers can’t find the product they’d have bought, it surfaces the right one at the moment of intent. See it applied in Solutions and proven in our price optimisation for a printing house and online product recommendation projects.
In depth
- Recommendation, ranking & pricing covers the core decision models: recommendation, ranking, next-best-action, and price/discount optimisation.
- Predictive & causal modelling: propensity and forecasting, plus causal inference and incrementality.
- Real-time event processing turns live signal into features and serves it in milliseconds.
- Experimentation & optimisation: A/B and geo experiments, incrementality testing, and contextual bandits.
The stack we work in
Modern gradient-boosting and deep-learning model families; feature engineering and serving; A/B, geo and incrementality testing; contextual-bandit frameworks; and the pipelines and monitoring that keep models accurate in production, all deployed in your environment and handed over.
Related expertise
Runs on the data-and-platform-engineering foundation, and combines with Generative & Agentic AI in Compound AI, where a language interface reads unstructured evidence and a calibrated model supplies the disciplined decision. See the practices overview.