AI Superior

Cities face numerous challenges in maintaining and inspecting their road infrastructure, particularly when it comes to efficiently detecting and evaluating road damage, such as potholes. Manual inspections are time-consuming, subjective, and often result in delays in identifying and repairing road defects. To address this challenge, we developed a platform that utilizes deep learning segmentation models to accurately detect and evaluate potholes and road damage. The platform accepts video footage or individual frames as input and applies a deep learning model to accurately segment potholes. Key features such as size and area are extracted for each segmented pothole, providing essential information for estimating severity levels. The platform also includes a scalable GIS application for visualizing road damage, customizable notifications for critical potholes, and filtering capabilities to prioritize repairs. Plus, it can be integrated into a real-time video processing pipeline for continuous monitoring and instant detection.  Read more: https://aisuperior.com/projects/empowering-cities-with-ai-driven-pothole-detection-and-road-damage-assessment/

Traditional deal-sourcing methods are time-consuming and limited by industry-standard classification systems like NACE (used in the European Union). These systems lack codes for emerging market fields such as machine learning and artificial intelligence, making it difficult to identify relevant companies within these market fields. To solve this, we developed a solution that addresses the challenges faced by investors in finding niche markets and assessing their financial states efficiently. Powered by Natural Language Processing techniques and Deep Learning models, our solution collects, processes, analyzes, and displays data from various sources, enabling users to perform semantic and syntactic searches, explore company clusters, review similar companies, and more. The solution also incorporates financial data extraction and aggregation, providing insights into the development of relevant companies over timeRead more:https://aisuperior.com/projects/ai-for-deal-sourcing/

SummaryUnderstanding the reasons and preventing customer churn is a critical component for the sustainability of the customer-oriented business. For one of the online gaming platforms, AI Superior created a machine learning model that learns player behaviour throughout the game and predicts the probability of churn for a particular time horizon. This solution helped a customer to predict the player’s churn event and employ the most relevant retention strategy. As a result of this project, our customer was able to decrease churn to 11,3%.ChallengeEstablishing a stable data collection and processing pipeline that didn’t affect the business operation due to high downtime was one of the most critical and challenging requirements from a customer. AI Superior’s data engineering team was able to successfully meet this requirement and build a high-quality data pipeline with almost no downtime of the customer’s online platform.Solution by AI SuperiorMachine Learning model that predicts churn over a time period for individual players. Moreover, model explainability has been integrated into the A/B testing framework for experimentation on retention strategies was also employed to test newly introduced features in the game.Outcome and Implications:allowed to understand churn reasons and developed retention strategies that reduce churn to 11.3%

Summary: Our team developed a drone-based application that was able to detect and report 25 different types of construction debris. The solution allowed to automatize the construction site inspection process while reducing human involvement and average inspection costs considerably.Challenge: A city municipality requested a solution that allowed them to fully automate construction site compliance monitoring and detect abandoned construction debris such as bricks, cement blocks, sand heaps, metal and wooden sticks, etc. It was crucial to have an automated solution that would minimize human involvement in the inspection process thus reducing labor costs as well as the time required for an inspection.Solution by AI Superior: We applied our proprietary computer vision technology for object detection, classification, and segmentation to detect 25 different classes of construction debris. We built a GIS dashboard to allow selection of a construction site and the visualization of all the debris detected within it. Additionally, for every detected object the system provided an estimated size (area) of construction debris (for a single object and clusters of objects of the same type) as well as the amount of detected objects. The application provided insights, while employing a GIS dashboard and exposed APIs to query detection results – this allowed the solution to be integrated to practically any other system.Outcome and Implications: The solution was adopted by multiple city municipalities demonstrating its operational and economical effectiveness. Furthermore, according to our customer estimates, the system saved 320 man-hours per month and reduced average inspection costs by 40%.The picture shows the result of the Construction debris detection from a drone. The different objects are dyed in different colors.