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We analyze your historical user interaction logs, search queries, and catalog metadata to identify recommendation patterns.
We construct automated pipelines on Apache Spark to clean and process user clicks, views, and transaction histories.
We train collaborative filtering, matrix factorization, and deep learning ranking models using TensorFlow Recommenders.
We store precomputed recommendation vectors in Redis caches, ensuring suggestions are retrieved within milliseconds.
We wrap the recommendation models into secure REST APIs, connecting them to your front-end apps.
We deploy live A/B tests to monitor click-through rates, optimizing model hyperparameters to maximize overall conversion.
We believe in radical transparency. You'll always know where your project stands and what comes next.
Progress reports every week
Communicate with your team
Clear deliverable checkpoints
Complete technical handoff
Let's begin with a conversation about your project goals.