Matching product to category tree automation
For a large e-commerce business, it's crucial to match its products with the merchants' offers as fast as possible. With the COVID-19 and rapid increase in online shopping, it came to a boiling point. Our client, an online platform of diverse goods, wanted to automate the onboarding of new merchants.
We created a web-based application powered by a machine learning model to match the merchant's product to the relevant category tree. We praise the FastAPI for its simple documentation, built-in perks, and development speed. It allowed us to come up with a simple application design in a short amount of time. Our deep learning model was able to predict the whole category tree to its leaves based on the title and product description only. This process can be transferred to any e-commerce platform with both deep and wide category trees to achieve the speed boost in terms of onboarding new resellers.
Predicting defaults for small and medium businesses
Predicting defaults and risk of small and medium businesses is a challenge that every financial institution has. Alior Bank wanted to enrich its modeling infrastructure by using transactions of SMB Clients. The bank didn't have the tools to analyze sequential information and diverse data types that are present in the transactions.
Together with Alior Bank we built a prototype of a framework called EventAI which simplifies model declaration. The framework let the analysts create sophisticated metrics describing the customers and use them easily. The tool we created is a solution that is available to all financial institutions in need of such mechanism.
Fraud detection for Polish Ministry of Health
GovTech Polska is using the competition formula to involve tech startups in solving state-scale technological challenges through Artificial Intelligence and Data Science. The central entity is the public sector, which reports challenges and looks for modern ways to solve them but the indirect beneficiaries are of course citizens.
The system is based on Machine Learning and Data Science algorithms to detect anomalies in hospital records that have financial impact on contract with the Ministry of Health. As a result, the Ministry can avoid contract abuse and save taxpayers millions of EUR, starting from 2020.
Session-based recommendation system
Recommending the most relevant products in real time is the Holy Grail of data science in the e-commerce industry. Together with Sephora, we have met the challenge to improve online recommendations and increase the click-through rate (CTR). Although offline recommendation approaches had already been used, they did not incorporate the latest user behavior. Our task was to implement a system that would generate accurate, real-time recommendations in response to users’ most recent activities in the app.
We approached the problem by creating a framework based on the accumulators - objects that transform raw events into meaningful data that can later be used by machine learning models. We proved the power of this method by winning the 2019 RecSys challenge. Having data prepared, we gathered existing offline recommendations with newly generated online ones, giving us a large set of potentially suitable products. The final step was to rank those products from the most to the least relevant and present the user only a couple of the best ones.
Predicting student progress, optimizing classes schedules
Predicting any kind of human behaviour is a difficult task, predicting student progress in online/offline courses is no exception. Our client, an international language school, wanted to improve scheduling of their offline classes by predicting students’ progress in the online part of the course.
Exploiting the power of EventAI in transforming event-based data into a meaningful machine-learning algorithm input, we were able to predict finishing times of course’s online chunks for a given student. EventAI, with the ease of declaring features and aggregation functions, made the feature engineering process simpler, less prone to errors and easy to control. Having the predictions, we were able to optimise offline classes schedule, improve classes occupancies and reduce students waiting time.
Price is one of the most vulnerable parts of any online venture. Every store is trying hard to find that sweet spot where the price is both profitable and emphasizes the value of the product. Instead of going the hard way, Frisco, the Polish market e-grocery giant, decided to approach this in a smart way by leveraging the power of machine learning.
Our goal was to increase sales without margin decrease for some categories and to increase margin without sales decrease of specified products in other categories, according to Frisco's strategy. We met this goal by adapting two concepts: price elasticity and market basket penetration. To investigate the relationship between the price change and market basket penetration for each product, we used a probabilistic approach. It allowed us to use different levels of price sensitivity, introduce expert knowledge into the equation, and gave the distribution of likely values. During the whole process, we gain a deeper understanding of the e-grocery industry, which we can transfer to other companies.
Implement digitalization program, to all offices and factories and 70 000 engineers. Build internal AI communities and make ‘AI as common as excel’.
We interviewed Data Science teams in 3 locations in the world and gathered insights about their cooperation with local and international teams. Over 70 easy-to-implement initiatives, majority of them involve no-cost and can be implemented in less than a month.