AI-powered Recommendation Engine
We’ve been impressed with the Tezeract team. Working with them does not feel like we’re dealing with a business; it feels like we’re dealing with a group of people who want us to be successful.


Suleman Niazi, Chairman & CEO
Konnect - Social Recommendation App
Project Overview
Konnect is an AI-driven recommendation engine that leverages advanced machine learning and natural language processing to transform traditional filter-based suggestions into personalized matches. Designed to connect users with compatible individuals worldwide based on their interests, preferences, hobbies, and distance, Konnect makes it easy to build meaningful connections and enhance social experiences.


Problem
Konnect’s problem was upgrading from a filter-based system to an AI-driven recommendation engine
Konnect was an existing social app aimed at democratizing local human connection. However, the app’s recommendation system was basic and lacked personalization. Users were not recommended based on others’ preferences or interests, nor did the system consider factors like location and regional preferences. As a result, the connections felt less meaningful and relevant. The goal was to transform this basic system into a personalized recommendation engine powered by AI. This new engine would help users effortlessly connect with compatible individuals worldwide, fostering more meaningful relationships and enhancing their social experiences. This is one of Tezeract’s success stories, where our AI-driven recommendation engine is finely tuned to individual preferences.


Solution
Integrate an AI-powered recommendation engine into konnect
Tezeract replaced the filter-based recommendation in Konnect and integrated it with an AI-powered recommendation engine to help users connect with like-minded people around the world and enrich their social experiences. It is a personalized recommendation engine built using advanced machine learning and natural language processing models.
Our AI recommendation engine simplifies the process of finding and connecting with people who share your passions, making it easier than ever to initiate conversations and build new relationships. Konnect’s advanced algorithms ensure that users receive highly accurate and relevant content suggestions based on specific interests and preferences. For example, if a user is interested in soccer and football, and another user in the same location shares the same interest, they will be recommended for socializing. Additionally, if a user has set gender-based preferences, the system ensures recommendations are tailored accordingly. This allows users to discover like-minded individuals from all corners of the globe, providing a diverse range of meaningful connections.
The Results
Highly Personalized User Experience
With tailored recommendations based on user interests, the engine ensures each user receives relevant, personalized content suggestions, enhancing engagement and satisfaction. This approach is proven to boost interaction by up to 40%.
Scalable & Consistent Recommendations
By leveraging a global data pool, Tezeract’s recommendation engine maintains high-quality suggestions for millions of users simultaneously, ensuring consistency and relevance across diverse user bases, which maximizes engagement.


What tech stack do we use for the AI recommendation engine case study?
Leveraging Konnect with Our Advanced Artificial Intelligence Technology Stack












The Challenge
- Upgrading to AI-Driven Personalization
Transitioning from filter-based recommendations to an advanced AI system that delivers precise, personalized suggestions.
- Ensuring Effective Global Connections
Accurately connecting users with compatible individuals worldwide based on their interests.
- Maintaining Recommendation Quality
Providing consistently relevant and high-quality recommendations across a diverse user base.




The Process
- Product discovery & scope
We aligned with the business vision to develop a thorough project brief, including in-depth market research, competitor analysis, and relevant data. We then translated the concept into a concrete plan by outlining user stories and features, such as building an AI-powered recommendation engine to validate the core assumptions of the MVP.
- Solution development
At this stage, our focus shifts to developing the AI solution for fashion brands by designing the architecture of the AI model and training it.
- Iteration and monitoring
After the product launch, we collected feedback from end-users to refine and enhance the product. We introduced new iterations and features concurrently to ensure an optimal user experience.
Key Features
- Tailored Recommendation
With Konnect, users receive customized recommendations that match their unique interests and preferences, ensuring a more relevant and engaging social experience.
- Feedback on Recommendation
It allows users to provide their feedback on custom-made recommendations and gradually improve its personalized recommendation tool from feedbacks.
- Global Recommendation Engine
It is trained on billions of data from worldwide so that it can provide custom recommendations to millions of people globally at the same time.


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