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AI for your Business

Artificial intelligence is enabling businesses to work smarter and faster, doing morewith significantly less. As technology and businesses continue to advance, more ofour clients are looking for powerful, sophisticated solutions that will improve and streamline operations.


Retail Ecommerce

Our AI powered software and apps can take your retail ecommerce store to the next level of customer engagement.

We can use artificial intelligence in certain ways like recommending the customers what to purchase depending upon his/her past purchases or items put in the search box, or providing chat bots/live messages with recommendations or solving queries.

AI powered ecommerce can make the shopping experience way more personal for your shoppers. Some of the areas where we build AI powered algorithms:

  • Product Search
  • Product Mapping/ My Style
  • Coupons & Offer Prediction
  • Recommended Products
  • Customer Service

Content Aggregation

Content aggregation is now being redefined with Artificial intelligence. There are 3 essential functions that a content aggregator performs –

  • Aggregation of content
  • Categorization and
  • Personalization according to the users.

With the application of artificial intelligence, you can send personalized recommendations to your users with relevant articles, videos, podcasts, news, research papers, etc.

We can build a truly personalized content aggregation and recommendation platform that evolves according to a user’s interest by aggregating content and categorizing it in a meaningful way. As a user interacts more with our platform, it will evolve according to their interests.


Movie & Music Recommender

Building a recommender system using AI that predicts the likelihood that a user would prefer an item, is an opportunity that our business apps provide.

Based on previous user interaction with the data source, the system takes the information from similar users with same interests, analyze their behavior, or historical trends, making the system capable of recommending an item to a user.

For a media commodity like movies and music, suggestions are made to users by finding user profiles of individuals with similar tastes. Initially, user preference is obtained by letting them rate movies of their choice. Upon usage, the recommender system will be able to understand the user better and suggest movies that are more likely to be rated higher.