Bringing intelligence to systems across industries

We’re great at AI, collaboration, and innovation

How we implement AI

We develop the AI in your product

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Recommender systems

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.
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Recommender systems

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
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September 01, 2022

Leveraging 3D Engines for Data Generation in Deep Learning

Applied to the specific case of scene text detection.

In the field of computer vision, synthetic data generation is especially interesting since the number of relevant resources and tools have grown and improved significantly over the years. The development has not been in the field of machine learning though, but rather in game engines such as Unreal Engine, Blender and Unity. Often produced by professional designers, realistic scenes are produced offering great details.
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August 11, 2022

Explainable AI in medicine: Detecting AF in ECG data

Machine learning models usually do not explain their predictions. This is a significant barrier to adaptation in domains like medicine, where understanding how the model works is vital.

In this blog post, we take a look at how explainability techniques can be used on a deep learning model predicting atrial fibrillation from sinus ECG (electrocardiogram) data.
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Recommender systems

Product recommendations for Ahlsell’s website and app

We helped out with kickstarting the in-house AI capabilities at Ahlsell by collaborating on the development of a recommender system for their website and smartphone app.

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.

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Computer vision

Detecting faulty transmission pole guy wires in drone images 

The system consists of deep learning and traditional machine learning parts and is able of screening vast amounts of drone images to detect faulty guy wires.

The objective of the project was to create a machine learning pipeline to analyze images from power lines and detect lack of tension in guy wires. To solve this problem, the team broke down the problem in different stages and created a multi-model AI solution, in close collaboration with the client.

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AI for Medtech

Detecting food intake in glucose time-series from diabetes patients

Together with Digital Diabetes Analytics, we created a meal detection system using glucose level data to help people with insulin-treated diabetes.

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.
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Recommender systems

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.

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Supercharging Medtech

In recent years, the Medtech sector has increased its utilization of machine learning to make smarter and more efficient medical products.

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Experts in fintech-specific AI

The financial tech sector is our bread and butter. We have developed solutions in multiple contexts over the years and are well versed in the specific challenges of the domain.

Related
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September 09, 2020

Classification of hypoglycemia causes in blood sugar time series

We give an overview of a study conducted in collaboration with Daniel Espes and Per-Ola Carlsson at Uppsala University aiming to improve the treatment of type 1 diabetes.

Type 1 diabetes is one of the most common chronic disorders among children and adolescents but affects people of all ages and globally more than 5 million people are affected.

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AI for Fintech

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.
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AI for Fintech

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.

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Object Detection

Real-time object detection for online gaming tournaments

Abios Gaming is a startup that focuses on tracking real-time events in online gaming tournament video streams. They asked us to improve detection performance and make previously impossible detections, possible.

Among the objects to be tracked were various weapons and tools the players use in-game, as well as certain interactions. The system detects and tracks timestamped events in correspondence with the objects the players possess, or use, to provide a structured stream of events that captures essential information about the evolution of the tournament.
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