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Recomendations Engine


Recommendation System

Recommendation systems are built using artificial intelligence algorithms. AI has the access to user’s past data, for example, user’s likings, interests, choices, and preferences. On the basis of this data artificial intelligence systems suggests products or items that are recommended for the user. Online Recommendation Systems have an ability to customized the content, based on the past behavior, which brings customer delight and improves the traffic on the website as a customer would like to revisit the website.


There are three main classes of online recommendation system algorithm based on Machine learning and Natural language Processing :

  • Content-Management-System-CMS-for-Static-Content
    Content-Based Filtering Systems

    Content-based recommendation engine generates recommendations based on items and attributes and their similarities. The item refers to content whose attributes are used in recommendation models.

    Collaborative filtering System 

    Collaborative based filtering System generates recommendations based upon on crowd-sourced input. This strategy recommends user behavior and similarity between users.

  • Hybrid-ticket-stream
    Hybrid recommendations Systems

    Hybrid recommendations System is a combination of content based recommendation engine and collaborative approaches. They help us for improving recommendations that are derived from the sparse dataset.

    Demoraphic Based Recommendation Systems

    Demographic area based recommendation system recommends the products that are available in that particular geographical location. Artificial intelligence system .

  • Phone-activity-reports
    Activity Recommendation System

    The system is responsible to analyze past activities of a person like what a person orders mostly to eat or drink, types of places one visits mostly.

  • Product-Management-For-Seller
    Product Recommendation

    This system prefers a new product to any customer based on their previous search. It extracts the required information from customers previous activities or choices from the database.

  • HD-video-and-audio
    Movie / Video/ Song Recommendation Systems

    Any person who uses to view online movies, songs or video are preferred with similar items. This is due to recommendation system.

  • health-monitor
    Health Recommendation System 

    Any person who uses to view online movies, songs or video are preferred with similar items. This is due to the recommendation system.

  • Customer-Address-Management
    Customer Services Recommendation System

    The engine will analyze the behavior of customer data reviews or comments and recommend the products according to the previous purchase history.

  • Sounds
    Automatic Music recommendation

    Using machine learning algorithms we made a system which can automatically predict genre of any song along with its instrumentation.

  • Color-Tagging
    Automatic Tagging and Recommending

    Tagging on Social media websites has become so popular. It means connecting any song, video or person within a particular stuff.

Benefits to bank on

Our clients are able to increase end-user productivity without sacrificing usability.

  • One Touch Enablement

    No need to scratch your head thinking how to integrate the recommendation engine. All you need to do is enable it right from the “Manage Content” section of the CMS. Let the power of artificial intelligence unfold.

  • No Background Building

    Our powerful recommendation engine does not want any manual insertion of data from your side. Your users get content recommendation basing on their past viewing history.

  • Activity-based Recommendation

    Our recommendation engine uses machine learning which tracks your users’ time of login to sign out and showcase their favorite content which they cannot resist to click and stream.

  • Choice-based Recommendation

    Unleash the brilliance of our recommendation engine that is the brainchild of a bunch of data scientists who have rich experience of studying and analyzing netizens’ behavior across leading OTT platforms.

Stop wasting time and money on technology. Let’s get started


Transform your technology by focusing on 3 key areas

  • Increase-Efficiency

    Increase Conversion Rate

    Accelerate “Data-to-Insight-to-Action” cycle, by consuming offerings like Data-as-a-Service and Reporting-as-a-Service.

  • Min-Customer-Efforts

    Increase customer Loyalty

    Drive key business outcomes, using the full service digital stack – Mosaic, Digital Jedis, and human-centered design experience.


    Increase customer Retention

    Adopt a collaborative approach to innovation, by leveraging innovation labs, future financial ecosystems, alliances & partners.

Customers stories