Why Custom Recommender Systems

Custom recommender systems or engines provide a filter that can predict user ratings or preferences.

It is used to innovate, increase profits and drive more revenue, and improve efficiencies across organizations. There are a variety of applications for machine learning given massive volumes of data, company inefficiencies, and competitive pressures.

Typical problems

Recommender systems take decision-making to the next level.

A company may be looking to connect the right product to a buyer, get critical information from an employee, customer, or partner, or improve online shopping experiences. Often decision-makers, having identified AI as a path to innovation, perceive custom systems as beyond their reach, available only to cutting-edge companies with seemingly infinite tech budgets.

How we help

Synaptiq works with clients to devise strategies for how to apply the recommender systems that will best suit their needs.

If we determine a custom recommender system is optimal, we build and integrate an engine that integrates with existing workflows or systems. We work with each client to ensure the new system works to solve their unique challenge in an efficient and repeatable way. 

Who benefits

Recommender systems are particularly valuable for

    • High tech companies
    • Digital products
    • Customer Service
    • Financial Services
    • Healthcare

Custom Recommender Systems for Improving Customer Experience

Read about how we used Custom Recommender Systems to help the Chief Technology Officer and product team at a corporate training solutions provider. Our client wanted to improve the way customer success representatives recommended content to customers. We created models in a Python notebook to identify patterns in data and help personalize customer experiences and…

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About Us

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