Our cases
- Industry
- Areas of AI
- Capabilities
Social feed and video recommender for Frever
Engineers at Frever and Modulai teamed up in close collaboration to create an end-to-end machine-learning-based feed recommender system. A multi-model system architecture was developed and populates the feeds of every user. Information about the content of the video as well as indicators of the users' preferences is taken into account to ensure the best possible experience and relevance.
Frever’s unique video-creation and social content sharing app enables their users’ creativity. Users create personalized avatars and express themselves through music videos, stories, and vlogs.
On Device Research: Smarter Operations using AI
On Device Research is a UK-based company that has a platform that allows users to complete online surveys in the exchange for money.
Machine learning has huge potential in making day-to-day business more intelligent. This case highlights how incorporating models in the everyday workflow for On Device Research can improve security and enhance user experience
Predicting a company’s future financial performance
Modulai built a deep learning based system for predicting the values of various fields in a company's annual report - a year in the future.
Currently, there exist ML models to predict different financial KPIs such as debt, profits, revenue, etc. of a company based on their historic values. The values of these fields are related and dependent on each other.
Recommendation system for Skincity
Providing accurate skin product recommendations online is difficult. We built a system that combines the knowledge of the skin therapists with product- and customer behavior data to increase product recommendation accuracy with the aim of driving higher conversion rates and customer satisfaction.
The client is an online skincare clinic that offers a finely-tuned selection of professional skin care products and make-up. Central to their operation are the customized product recommendations customers receive from their experienced skincare therapists.
Customer feedback clustering using state of the art NLP
Using recent advancements in Natural Language Processing (NLP), the Modulai team developed a model for clustering customer feedback into topics, making it possible to monitor sentiment across these and detect potential negative responses in real-time so that companies can take immediate action.
The client is a Stockholm-based startup founded in 2015 that helps businesses grow by the voice of their happy customers, providing a cloud-based solution for customer referrals, responses, recommendations, rewards, reviews, retargeting, and retention. Staying alert to customer opinion is key since reviews and testimonials are an important part of many companies marketing strategies.
Real-time and In-session: state-of-the-art recommender system for Ahlsell
The Modulai team worked together with Ahlsell’s Applied AI team to develop a real-time session-based recommender system. The system weighs a customer’s intent in a given session on their website or smartphone app to provide contextually relevant recommendations.
In a case like this, careful balancing of model complexity and predictive performance is key to best meet Ahlsell’s needs. Higher predictive performance means customers getting recommendations better suited to their needs, indirectly increasing Ahlsell’s revenue. However, real-time, low-latency inference in the cloud is not free, and customer experience is negatively impacted by slow(er) load times.
Bayesian hierarchical model for company credit risk
We developed a credit system to assess the credit risk associated with small to medium-sized enterprise (SME) lending.
The client is an American software company that provides solutions to financial institutions. Their offer includes technology for compliance, credit risk, and lending solutions used to manage risk.
Transaction risk model for a major online payment platform
A team from Modulai trained a model to predict risk direct banking transactions. The project included a full specification of the decision engine infrastructure as well as the deployment plan.
This client is a large fintech in the payments space providing an alternative to credit cards. They process many millions of transactions yearly for thousands of e-commerce sites and online service providers. Each transaction might expose the different parties to various risks, and containing these risks are crucial.
Predicting the risk of mission failure in shipments of pharmaceuticals
We applied a set of machine learning techniques to predict the risk of shipment-failure in shipments of temperature-sensitive pharmaceutical goods.
This client is one of the world’s leading providers of solutions for tracking sensitive pharmaceutical goods during shipment and throughout the whole supply chain. Each shipment is tracked minute-by-minute by a temperature logger.
Intelligent indoor climate solution for real estate developer
Predicting what the temperature will be in a meeting room, 25 minutes into the future.
We used data that had been gathered over a year at the company’s HQ. A combination of temperature, CO2, outdoor temperature, climate system, and meeting room booking data.
