Our cases

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.

In the highly competitive e-commerce industry, customer service plays a crucial role in sustaining and growing the business. Customers nowadays expect instantaneous, accurate, and personalized responses to their queries. Traditional customer service methods are no longer sufficient to meet the growing demands and expectations of the online shopping community. To bridge this gap, an AI-powered shopping assistant bot capable of understanding and navigating the vast product catalog and providing instant, relevant answers is a necessity.

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

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.

With the app, users can access a variety of exercises that measure essential football skills. These exercises are designed to be both fun and interactive, while also providing analytical insights to the player and suggesting exercises that target and improve their weak areas.

Mediatool initiated a project utilizing OpenAI’s chat models through few-shot learning to generate complex JSON structures for automatic data retrieval and visualization, based on input and feasibility study by Modulai. This allows customers to chat with an AI assistant in a user-friendly way to retrieve data for decision-making in seconds. Chat interface removes technological barriers for customers who can now use natural language to communicate with the platform.

The model was integrated into the central control system of the fueling rig, taking the role of the main detection mechanism, firstly to make sure the car is in the right position, and secondly to guide the fueling arm to the fuel door lid in real-time.When optimizing the model for CPU inference, we explored TensorFlow Lite and OpenVINO. In the end, we used OpenVINO which is a framework for optimizing deep neural nets on Intel CPU’s.

In close collaboration with Ahlsell’s data scientists, the team developed an end-to-end pipeline handling data ingestion, data processing, and model predictions. The recommendation system consists of a collaborative filtering model as well as a content-based model.

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.

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.

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.

Today, more than 5,5 million people worldwide live with type 1 diabetes and need insulin-based treatment to survive. When it comes to diabetes, there’s no “one treatment fits all”, therefore doctor’s need support in optimizing treatments for each patient.

Every day, thousands of people suffer from stroke due to an underlying undiagnosed AF condition. Stroke often results in conditions such as paralysis or, in many cases, death. For this reason, precise and early diagnosis of AF is an essential part of stroke prevention.

The goal was to optimize the vast logistics chain by minimizing unnecessary transports between their local shops and main warehouses.

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.

Lindex is a major retailer, active in the Nordics and throughout Europe. They have several million registered customers, and is one of the prominent brands in women’s’ wear, lingerie and kids wear. Nowadays, users expect retailers to provide them with recommendations based on what they’ve clicked on and bought. A feature that both generates more sales and provides the e-shopper with a sense of being cared for.

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.

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.

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.

Data from 2.5 millions catches was collected and refined. Each data point was annotated with date, time, location, and fish species. Detailed weather information was added to each catch; air temperature, air pressure, wind speed, cloud cover, and precipitation. Temporal data was transformed into astronomical conditions such as moon phase, solar irradiation, and azimuth angle. A global climate model was created,