Muteki Group

Mobile application that shows personal analytics and analyses data. Can be connected with different devices, such as fitness trackers, or show social networks activity.

ProblemThe client encountered difficulties in making well-informed decisions concerning store design, product placement, and shopper activation. Conventional approaches lacked the essential visual and interactive features required for effective decision-making in a highly competitive retail landscape.SolutionMuteki Group developed a virtual reality software solution that addressed the client’s challenges. By leveraging virtual reality technology, the software provided a realistic and immersive environment for the client to visualize and optimize their merchandising strategies. It enabled the client to collaborate with stakeholders, explore different store layouts, test product placements, and evaluate shopper behavior, all within a virtual space. This solution empowered the client to make data-driven decisions and enhance their competitive edge. ResultUpon completion of the project the client gota better understanding of customer behavioroptimized store layouts for increased salesenhanced product visibility and placementimproved overall customer engagementmore efficient decision-making process and enabled to win at the point of purchase. Technologies - JavaScript, TypeScript, React, HTML,CSS, AWS, PostgreSQL, Python, Django, REST API, Docker, Git, AI, VR

Fashion retail, both physical and online has hardly any personalization and uses very little data and technology when offering products. At the same time, over 70% of consumers expect personalized shopping experience. We want to offer personalization and at the same time the shopping convenience for people that don’t like shopping or don’t have time for it.Muteki Group developed a scalable web system using AWS services. Our AI solution analyses 100 data points and suggests outfits for customers taking into consideration their preferences, previous purchases and matching them with latest fashion trends. The system can generate hundreds of outfits in several minutes.

Problem: The existing real estate application faced several challenges that hindered its effectiveness. These challenges encompassed suboptimal performance, inadequate user experience, and the necessity to remain competitive in a rapidly evolving real estate landscape. The absence of advanced AI-driven features further limited the application’s capabilities and hindered its potential to offer cutting-edge services.Solution: The application’s architecture was revamped by implementing microservices, enhancing scalability, flexibility, and maintainability. The team worked on improving the application’s performance, achieving remarkable gains of up to 50% in certain services, ensuring smoother user interactions. A novel machine learning model was developed from scratch, leveraging PySpark and AWS SageMaker. This model could predict house sales, enabling users to make informed decisions based on data-driven insights. Result: The project’s results were transformative and impactful:The application’s performance was substantially enhanced, offering users quicker responses and improved navigation, contributing to higher engagement rates.The introduction of AI-powered predictive analytics provided users with valuable insights, aiding in making informed decisions when buying or selling properties.The modernization of technologies and migration to Elasticsearch led to a more efficient search experience, enabling users to find properties more effectively.Collectively, these enhancements culminated in an improved overall user experience, a competitive edge for the client, and a well-optimized real estate application capable of meeting modern market demands.Technologies: Java 8, Python, JavaScript, Spring 4-5, Spring Boot 2, MySQL, Elasticsearch, Hibernate, PySpark, AWS, Tomcat, Apache HTTP, Selenium, Maven, Jenkins.