AI Recommendation Engine

A recommendation engine uses machine learning to predict which products, content, or actions are most relevant to each individual customer. Modern recommendation systems go beyond collaborative filtering to incorporate purchase history, browsing behavior, contextual signals, and customer attributes for truly personalized suggestions.

Building a recommendation engine in-house is one of the most complex ML projects a company can undertake. It requires specialized infrastructure for candidate generation, ranking, real-time serving, and continuous A/B testing. Most teams either rely on basic 'customers also bought' rules or expensive standalone recommendation platforms.

EkamFlow includes recommendations as part of its unified prediction model. The same API call that returns churn risk and LTV also returns personalized product recommendations — trained on your catalog data and customer behavior. No separate recommendation infrastructure to build or maintain.