Recommendation engine

❝ The propensity to immediately incorporate and analyze real time data based on browsing patterns can help firms recommend meaningful and timely offers to customers. We help you to create this rich and interactive environment. ❞

How can we help you?

Contact us at the consulting office nearest to you or submit a business inquiry online.

Recommendation engines reduces complexity of a decision to just a few recommendations. Big data has opened up an opportunity for that we did not see before. Our recommendation engine filters big data to decision cenetric data by suggesting few important data points out of a large pool of data. It outdoes bench-marking and helps your organization to unlock unplanned benefits.

Increase customer engagement by Relevant, Effective, and Timely recommendations

Optimize performance of your applications & platforms


Arrange more dynamic and complementary landing products/services to engage customers and give them what they need to drive much required conversions.


Provide your customers more personalized offers/suggestions on what are their needs, wants and likes for quick conversions.

Product based

Offer your customers relevant and complementary products based on their browsing history to enhance sales and improve engagement.

abandonment solutions

Engage customers personally and reduce customers exit by non-distracting ads and reminder mails.


Monitor and analyze behavior of every visitor on your websites and make your website more personalized to increase customer engagement and satisfaction.

Popup based

Enable popup feature in intervals or when user is inactive on your website to increase customer engagement for rapid conversions.

Set of recommendation engines and algorithms.

Similarity based recommendations

Using the available data, our similarity engine uses sophisticated algorithms to calculate the similarity between the available library of content and reference assets. Real estate enterprises increase their sales by over 55% leveraging our recommendations.

Collaborative recommendations

Our collaborative recommendation engine analyzes aggregated viewing patterns and preferences. This engine brings in a social advantage to the video content providers and televisions. Media industries based on this recommendations increase customer engagements by over 55% and subscribers by over 40%.

Preference based recommendations

Our preference engine self learns the user’s priorities by collecting relevant inputs and segregates them into individual profiles. The sophisticated algorithms used in our engine result in preference learning up to 7x faster than competitive solutions. BFSI organizations benefit and increase their customer satisfaction by over 33% driving more revenues.

Social recommendations

The social engine filters the viewer’s social network profile and recent postings to generate recommendations. Highly personalized and sophisticated algorithms are used to filter and prioritize profile information relevant to their interests and liking. Leading educational institutions integrate our engines to revive their syllabus content enabling more student interaction and interests.

Our broadly classified engines have considerable ROI that provides benefit to industries.

Our engine uses information extracted from various relevant data sources to predict trend and behavior patterns.

Our engine analyzes the past performance, users’ behavioral pattern to understand the true need of a customer and generates meaningful recommendations.

We use advanced machine learning techniques and business rules through which the engine creates and recommends relevant options for your clients.

Looking for a First-Class Technology Consultant?