Case Studies
AI teacher assistant for children with Special Educational Needs and Disabilities
Context
Teachers with little or no experience working with children with Special Educational Needs and Disability (SEND) may struggle to find the best approach to support them. Additionally, there are not enough professionals, such as therapists and resource teachers, to respond promptly to every case. An AI-powered platform could help share their expertise with other teachers and specialists, ensuring timely and adequate assistance.
Engagement
Highly experienced professionals, eager to share their knowledge, offered their expertise to design and develop a knowledge sharing platform.
The platform is:
Web based – to ensure access to the solution
Simple – to ensure usability
Scalable – to allow for the continuous ingestion of new data
Efficient – allow for the concurrent usage of at least 5 members at the same time
Results
By leveraging the well-established practices of experts and our expertise in AI development, we have created an online form which is filled from professionals and collects use cases. Thus, real-life examples are included in a knowledge base as part of a RAG system. The data collection process was designed to be as easy as possible leveraging voice recording and transcription capabilities.
The form is structured to cover all aspects of communication with children, including the situation that occurred, the professional’s response, and the child’s reaction. Additionally, experts can evaluate the entire scenario, providing valuable insights for the knowledge base.
The AI Teacher Assistant Platform
AI model development and certification for cardiovascular medicine
Overview: Our client is a European MedTech start-up focused on the detection of cardiovascular anomalies. The start-up’s objective is to challenge the market for ECG signal-based detection of heart pathologies, such as, but not limited to, atrial fibrillation, premature beats, subventricular tachycardia, etc, obtained from affordable wearable devices such as the Polar H10. The company required an ML-based model development and preparation for the Class 2A medical device certification, in order to ensure that their system is safe and effective for patients to use at home.
Team: Our team of experts included a Data Engineer, Data Scientists, ML Engineers and a Project Manager
Approach:
Discovery phase
Our Data Scientists collaborated with the client in order to understand their existing ECG signal processing system, as well as their computation-based algorithms. We created an automated testing tool that assessed the performance of the current solution against a range of internal and external medical data sets. Based on this analysis, we provided recommendations to improve the scope of detectable medical conditions and identified areas for improvement.
We advised the client on the certification process and prepared an action plan for the ML-related work and documentation needed for the Class 2A certification. We also provided improvement recommendations and execution support, which helped the client enhance their ECG signal processing system and algorithms. Our team completed the analysis and recommendations in less than 8 weeks, providing actionable advice, which helped the client reduce the application processing time.
ML-based signal processing model development phase
The client provided the raw ECG signal data, including labelled data with annotations of R-peaks, pathologies, as well as ‘soft’ and ‘hard’ noise segments. This labelled data was used as inputs for the models and deemed as their training set. The quality and accuracy of pathology predictions (detection) heavily depend on the cleaness of the ECG signal. This is why it is important to have robust, well-performing models in terms of accuracy and precision, which can distinguish between the clean and noisy parts of the ECG signals. Our Data Scientists developed ML-based models for ‘soft’ and ‘hard’ noise detection. Upon successfully passing the strict model performance acceptance criteria of the client, our ML Engineers created the production pipelines for training and inference and integrated it into the client’s platform and business processes. Our collaboration continues with the development of further pathology detection ML-based algorithms.
Results: Through our active collaboration with the client's team, we were able to complete the model certification preparation work in just 6 months, helping the client achieve their goal of obtaining a Class 2A certification for their AI-powered medical device system. This certification allows the client to offer their product to a wider range of customers, enabling them to provide early detection and prevention of heart-related medical conditions.
Stock market data tool integrated with Bloomberg
Overview: Our client, a financial services company, approached us with a challenge to improve their data analysis tools and provide automation to their team's daily work. They wanted a solution that would help their surveillance organization gain more insights into potential scenarios. Our approach was to create a solution that would enable them to predict MTD/YTD prices.
Approach: We worked in close collaboration with the client to analyze multiple numerical methods and compose different types of feature vectors to improve the prediction model's accuracy. We developed a script that extracts information and an algorithm that calculates the analysis of historical price changes.
We integrated multiple data sources into the solution, including the Bloomberg service terminal, to ensure we had the most accurate and up-to-date data. We developed an API and a user-friendly interface for filtering and searching the information we extracted and analyzed. We used Keras to predict MTD / YTD prices developing a stock prediction model with a neural network to predict the returns on stocks.
Tech stack: Python, Django,Vue.js, AWS, Keras.
Results: The performance of our solution was excellent, and we optimized it to achieve more than 80% accuracy in predicting prices. As a result, the tool became an essential asset for our client, empowering their surveillance organization to understand market trends better and predict future prices with greater accuracy.
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