AI Superior

Estimation of Fat and Muscle Volume of Human Eyes

Our customer, an Ophthalmology Centre, faced the challenge of accurately estimating the volume of fat and muscle in the human eye. This information was crucial for monitoring eye health, evaluating the effectiveness of interventions, and conducting comparative analyses.


We employed deep learning techniques to develop an advanced model capable of accurately segmenting fat and muscle tissue in each slice of an MRI orbit scan. By leveraging the model’s capabilities, we achieved highly precise volume estimation for the eye’s anatomical structures. Our deep learning model was designed to handle different views of the eye, including Coronal, Sagittal, and Axial, ensuring comprehensive analysis of the orbital region.

Urban Zone Pricing Analysis

A leading real estate online platform required a solution that would facilitate the value assessment of urban zones within a city. To provide industry professionals with more accurate insights, we developed a segmentation model to assist in this process.



We develop a solution that facilitates the assessment of prices for different areas within a city. The project involved analyzing the city map using a semantic segmentation approach to generate a detailed segmentation map. This map classified pixels into predefined classes such as roads, residential houses, infrastructure, green areas, and open land areas. Historical maps from different years were also incorporated to gain insights into the city's evolution over time, particularly focusing on the availability of open areas for real estate development. These insights allowed the real estate company to assess the value and desirability of specific regions within the city and make informed price evaluations based on their expertise and market knowledge.

Deep Learning Solution for Usage-Based Insurance

Our customer was looking for a way to reward good driving skills by offering cheaper insurance rates. However, accurately assessing driving behaviours and providing fair discounts based on individual performance has been a challenge. Traditional methods rely on limited data and subjective assessments, leading to inconsistent results. To overcome these challenges, AI Superior set out to develop an advanced system that could process and analyze telematic data to accurately evaluate driving behaviours and calculate personalized discounts.
We leveraged deep learning algorithms to develop a model capable of analyzing various telematic data collected from the driver’s phone. The system could analyze data from accelerometers, gyroscopes, GPS, and other sensors to detect driving modes, such as whether the user was driving or a passenger, and track driving behaviours like exceeding the speed limit, unnecessary acceleration, abrupt hard braking, and more. A scoring algorithm was then applied to each trip to provide an overall score. Based on this score, a personal discount was calculated for each driver. Additionally, the system provided recommendations to promote safe driving practices, further incentivizing drivers to improve their performance.

Graffiti Detection System

Our customer was faced with the issue of graffiti that appears overnight on their city walls. Locating these graffiti spots and organising their removal can be quite challenging and time- consuming. On top of that, manual methods of detection are often inefficient and can lead to delays in the graffiti removal process.

To tackle this, we developed an advanced model that enables rapid, real-time detection and localization of graffiti. Using a vehicle equipped with a 360-degree camera to patrol the city streets, our solution applies deep learning algorithms to detect graffiti with high accuracy and capture photographs of it in real time. Each detected graffiti spot is accompanied by the GPS location of the vehicle at the time of detection. This information is then transferred to an interactive map with markers. Each marker identifies a graffiti location on the map, enabling users to see the exact location and 360-degree image of the graffiti. Relevant metrics, such as the total number of graffiti spots, graffiti density map, and average size of graffiti, can also be extracted from this platform.

AI-Driven Pothole Detection

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.

Solar Panel Planning with AI

Our client, a solar energy company, faced the challenge of time-consuming manual methods for analyzing residential roofs for solar panel installations. To improve overall efficiency and accuracy, we developed an automated solution that enables efficient planning for solar panel implementation. The solution utilises deep learning models to accurately detect and segment roofs into distinct areas, and estimates the area and dimensions of each segment, enabling optimal solar panel placement on residential roofs

Proptosis Detection Application

Our client, an ophthalmology center, required an automated solution that could assess eyelid positioning and identify proptosis-related abnormalities.

To overcome this challenge, we used deep learning algorithms to create Eye Metrics, a cross-platform web application, accessible on mobile (iOS and Android) and desktop devices, that empowers users to capture facial images and calculates key metrics for ophthalmological analysis.

Eye Metrics revolutionizes eye health analysis by accurately assessing eyelid positioning, detecting abnormalities related to proptosis, and providing a seamless experience for healthcare professionals.

AI for Deal Sourcing Optimization

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 time.

Construction debris detection from a drone

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.

Trash object detection from a drone

Summary: For a large semi-government organization, our team developed a system that detects litter objects from images captured by drones and helps to manage trash collection activities. We designed and developed a GIS-based application that allowed convenient interaction with detected litter objects and facilitated trash collection activities via optimal route planning and tracking of the collection progress.

Challenge: Many thousands of litter objects were dragged by waves to a coastal area, scattering them across a territory of around one thousand square km. Being dispersed across many single islands these objects were quite difficult and costly to identify by a human. Hence, it was required to design and implement an efficient strategy that allowed the detection and collection of litter.

Solution by AI Superior: We applied our proprietary computer vision technology for object detection, classification, and segmentation and designed and developed an interactive GIS-based application to display results and operate collection activities. The computer vision detection technology was applied to images captured by a drone. The drone flights were operated by a partnering team that provided RGB images covering the whole territory (the flight altitude was around 50 metres high above the land)

Outcome and Implications: the developed solution allowed to reduce the time required to perform trash detection activities by the factor of 25. This resulted in significant cost savings, halving the overall detection and collection costs. Furthermore, the collection time decreased by a factor of 4 while the automated computer vision solution allowed a 7% higher detection accuracy compared to a human expert. Additionally, this system decreased carbon footprint by a factor of 19.

The picture shows the dashboard of the application.

Cross media-type recommender system

AI Superior Solution

We developed a hybrid recommender system to suggest new content items to customers according to earlier identified consumption patterns and content similarity degree. As the result, every customer was recommended relevant content that, in turn, increased engagement.

Additionally, unsupervised clustering was applied to partition customers into groups based on their behavior and the content they consume. Besides personalization capabilities, it brought additional insights for understanding their customer base and offline marketing campaign adjustment.

Information Extraction from Invoices

Summary

We developed a service that extracts data from different types of invoices. Our service allowed the customer to automate the data entry process, ultimately saving 850 man-hours per month and decreasing the recognition error-rate by half.

Challenge

The customer, a large multinational equipment distributor, was interested in streamlining their invoice processing activities through digitization. They wanted to extract account information from invoices in their custom-made ERP system. Prior to our involvement, the customer was doing this manually, and the process was taking several minutes per invoice. As the number of daily invoices began to grow, it soon became apparent that a human-centric solution was no longer feasible. Moreover, in their efforts to increase manual processing speed, more and more human errors were being made.

Solution by AI Superior

The solution delivered is a web service that allows users to upload an invoice and receive extracted information in a predefined structure. The service relies on a pipeline that consists of the following components:

Document scans processing and enhancement.

Invoice type classification.

Identifying information blocks.

Information extraction based on Optical Character Recognition (OCR) and post-processing.

Information packaging and dumping.

Outcome and Implications

The developed service allowed the customer to streamline invoice processing in the CRM without any human intervention. It reduced the error rate by half since most errors fell into the category of human error. The speed of invoice processing increased by a factor of 25, and the customer saved 850 man-hours per month.

Churn Prediction for players

Summary
Understanding 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%.

Challenge

Establishing 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 Superior

Machine 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%

Real Estate Property Estimation

Summary
We developed a tool based on a machine learning model to automate real estate object estimation and find attractive offers on the market. The tool can also monitor the real estate market to identify ongoing trends. The component allowed the customer to obtain real estate market insights that are hidden from other stakeholders. Due to the tool’s success, the customer achieved a distinct competitive advantage and has already undertaken approximately 20 million euros worth of real estate investments based on the insights obtained through the solution.

Challenge


The customer is a real estate company operating in the DACH market and has the following objectives:

to monitor and predict price dynamics on different property types (residential and commercial)
to have an accurate automatic property evaluation
to automatically discover attractive properties for investment purposes

Solution by AI Superior

We established a data-collection pipeline to analyze over one million real estate items and trained a deep learning model on that data. The resulting model allowed the customer to:

Get a price estimation for a real estate object based on its parameters (house features, amount of land, availability of facilities, region and geographical location, etc.)
Identify the degree with which each property parameter contributes to the overall price
Predict market movement, capture temporal dynamics, and notify in case of a significant change
Automatically identify underestimated properties on the market

Additionally, we developed an interactive visualization tool for user-friendly access to the model’s insights.

Outcome and Implications

The system allowed the customer to react quickly to market changes and obtain high-quality insights for potential investments. Powered by the system’s insights, the customer performed around 20 million Euros worth of investments across multiple real estate properties that strongly demonstrated a positive return on investment.

Risk Estimation and Management

Summary

For a niche insurance company operating in a medical/health domain we developed a prediction machine learning-based model to estimate the risk of an economical loss. The machine learning model is based on neural networks and built by consuming historical medical data over five consec-utive years. The developed model significantly outperformed statistical approaches. With this mod-el, the customer was able to optimize its pricing policies which resulted in significant savings.
Challenge

The insurance company operating in a medical/health domain was facing the challenge of pricing policies development. For them, it was important to understand risks related to a particular patient and adjust pricing policy models accordingly. In turn, the customer was expecting to experience considerable savings.
Solution by AI Superior

We built an application based on a machine learning model to predict the probabilities of a particular disease according to many input features and parameters including medical history. For that, we trained a deep learning model that was effectively dealing with intrinsic challenges such as class im-balance. Additionally, we built a validation framework to objectively compare multiple approaches and ensure that the created model was significantly outperforming others.
Outcome and Implications

The developed Data Science solution significantly outperformed the baseline models relying on statistics. The model outcome was used to optimize pricing policy to increase revenue and better manage risks.

HR and Recruiting: People Analytics

Summary

We developed automated candidate scoring and candidate-to-company matching systems that al-lowed our customer (an online recruiting company) to automate the process of assessing technical skills thoroughly. Due to the success of the solution, scoring experts were able to save 800 man-hours per month. Additionally, they enjoyed advanced functionality to their platform.
Challenge

The customer is an online recruiting platform that connects remote technical workforces with companies across the globe. Finding the right candidates with the right skill and expertise level is vital to providing quality candidate recommendations to companies looking to hire new workers. Additionally, automating the interview processes was crucial to scale up assessment procedures without making drastic changes to their internal human resources function. These two business challenges were reduced to technical ones:

Automatic candidate scoring
Automatic candidate-to-company fit estimation

Solution by AI Superior

To automate candidate scoring, we developed a Data Science System that utilized historical candidate data and the experts’ scores related to their performance during technical/coding interviews and tests. The Data Science system was trained to automatically score candidates based on their code, test outcomes, and technical challenges results. Additionally, to provide automatic candidate-to-company fit, we developed a Data Science solution that, by analyzing key features of companies and candidates, was able to provide the degree of the match according to various criteria. This allowed candidates to get the recommendation of companies to apply as well as provided candidates with an easy way to get the best fitting talent. This Data Science solution’s unique feature is the module that extracts skills from candidates’ profiles and enables skill matching and gap analysis in a fully automatic way.

Content Recommendation

Summary

To help a media company increase the lifetime value of their customers, we developed analytics to provide item recommendations from a diverse set of customer sources. The developed solution allowed the customer to increase the diversity of content consumed by their users by 5% and, as a result, increase the lifetime value of a customer.
Challenge

The customer is a big media company that owns various TV and radio channels, audio podcasts, magazines, and newspapers. They were interested in a personalized recommendation system for their existing users and content consumers. The content type is diverse (TV programs and shows, news articles, etc.). And every user has preferences that have to be understood and taken into consideration while recommending a new content item. The challenge is to create such a complex system that would recognize individual users’ consumption patterns, understand their content prefer-ences and recommend new content items that users are likely to consume. With such personalization capabilities, the customer is expected to increase engagement and decrease churn.
Solution by AI Superior

We developed a recommendation system that utilizes several factors to provide recommendations. The system has the following capabilities:

Estimates consumption patterns of individual users
Understands content preferences of each user (topics of interest, content type, etc.)
Estimates demographics and technical means which are used by every user to access the con-tent
Assesses content items similarity from different perspectives

To enable this, we developed several analytical components: NLP-based topic discovery and content tagging module, content items similarity extraction analytics, consumption patterns extractor, collaborative filtering-based recommender, item-to-item recommender, hybrid recommender that takes into account all the listed modules.

Social Media Analytics for Marketing Activities

Summary

For a banking organization, we developed an interactive tool that performs social media data analysis to facilitate marketing activities by understanding audience interests, social group affiliation, demographics, etc. The solution enabled the bank to provide discounts and offers to increase loyalty and decrease churn.
Challenge

The customer is a European bank serving hundreds of thousands of customers. To decrease churn and improve loyalty to its brand, the customer wanted us to develop a solution that would enable understanding of their clients, their interests, social status, etc. Based on this, the goal was to:

Provide discounts and offers within a particular category, e.g., car credit or additional Visa card
Provide offers from partners, e.g., cruises for those who like travel or sports equipment for people who enjoy sports
Understand the audience for designing service packages, e.g., online service for young people
Detect brand ambassadors in order to encourage them to promote new services

Solution by AI Superior

Please see additionally to the text our Video.

Based on customer requirements, we developed an analytical module and an interactive tool that allows us to extract and analyze insights obtained from social media data, visualize them and repre-sent them in a consumable form. Within this effort, we developed the following analytical modules:

Audience interests extractor
Social groups affiliation estimator
The social class-based segmentation module
Geography-based clustering module

Outcome and Implications

The developed Data Science Solution Tool allowed the customer to interactively explore its audience and provide relevant offers and discounts that resulted in increased loyalty and decreased churn. According to customer feedback, the developed system allowed them to retain most clients that were previously going to churn.

Road Entities Recognition and Traffic Analytics

Summary
We developed a road traffic analysis system for a system integrator executing a governmental project. The developed system can process video streams from traffic cameras and perform analysis of road scenes: detect objects and estimate their properties (trajectory, speed, etc.), identify traffic jams and violations, recognize car models and their license plates, and more. The system provides a safer environment for the city and the ability to monitor roads without human intervention.

Challenge

The customer, a system integrator executing governmental projects, required a road traffic analytics system that would identify different road entities (cars, pedestrians, bicycles, etc.), track them, and perform further analysis, e.g., identify car type, model, and make, color and other. The main chal-lenge was to create a robust system that could be easily transferable across different camera types and points of view, as well as to work in different environments.

Solution by AI Superior

The system that we developed includes multiple analytical components:

Road entities detection (cars, pedestrians, bicycles, trucks, buses, motorcycles)
Car type, brand, and model recognition (7000+ unique models)
Object tracking
Car color recognition
License plate recognition and validation
Illegal parking analysis
Anomaly detection (e.g., a pedestrian on a highway)

Satellite Image-based objects detection