AI-based Recommendation Systems
Personalised AI recommendation engines that increase revenue, engagement, and retention by surfacing the most relevant products, content, or actions for each user.
Overview
Generic experiences drive generic results. Personalisation drives revenue. We build AI recommendation systems trained on your user behaviour, purchase history, and content interactions — powering product recommendations, content feeds, next-best-action suggestions, and personalised email content that feels curated by hand for each user.
Core Capabilities
Collaborative Filtering
Recommends based on what similar users liked — surfaces products or content your customer hasn't found but will love.
Content-Based Matching
Matches users to items based on attribute similarity — ideal for catalogues with sparse interaction data.
Real-time Personalisation
Recommendations update based on current session behaviour — what users do right now influences what they see next.
A/B Testing Engine
Built-in experimentation framework to test recommendation algorithms against each other with statistical rigour.
Real-World Use Cases
Product catalogue of 8,000 SKUs — customers seeing the same bestsellers on every visit, low discovery.
Collaborative filtering recommendation engine trained on browse and purchase history, serving personalised homepage and email recommendations.
Learning platform with 500 courses — students not knowing what to take next after completing a course.
Course recommendation model based on completed courses, quiz performance, and peer pathways.
News platform losing subscribers who couldn't find relevant articles in a feed of 200 daily stories.
Personalised content feed based on reading history, time-on-article signals, and topic affinity model.
Our Process
Data Audit
We assess your interaction data volume, quality, and user/item attributes to select the best algorithm.
Train
Recommendation model trained and evaluated on held-out data before any production exposure.
Integrate
Model served via API — plugged into your website, app, and email platform with minimal engineering work.
Experiment
A/B tests running continuously to measure lift and improve model accuracy over time.
Technologies We Use
Ready to Get Started?
Let's build your ai-based recommendation systems solution together.