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Our cases

AI for E-sports

Highlight detection and automatic video editing for online game streamers

Modulai has built a object-detection based system for detecting highlights and memorable events in online gaming streams for automatic editing of dense highlight reels.

Modulai has built a object-detection based system for detecting highlights and memorable events in online gaming streams for automatic editing of dense highlight reels.

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Generative AI

Customizable and safe: A search and chat solution

Aiming for companies and use-cases that either can't risk compromize data security or need special adaptation, we've developed a modular platform to fill the gap between traditional SaaS solutions and custom development.

Based on the learnings from numerous projects over the past two years, we have developed a safe and isolated RAG solution for companies with needs beyond vanilla SaaS solutions.

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AI for Media

AI-generated imagery for Bonnier News

In collaboration with Bonnier News, we explored the use of generative AI for replacing stock photos in ad production.

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AI for Media

Empowering Journalism with AI: Unlocking Podcast Insights

Modulai collaborated with Svenska Dagbladet to harness AI technology for transcribing and analyzing Swedish podcasts.

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AI for Media

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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AI for Retail

AI powered shopping assistant for e-commerce

Based on discussions and feedback from multiple e-commerse platforms, we've developed an easily integratable shopping assistant that lets site visitors ask natural language questions about the product catalouge and get recent, relevant and actionable resposes in real-time.

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.

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AI for Media

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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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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AI for E-sports

Shot speed prediction using object tracking for Passion Football

The ICONS app by Passion Football lets football players of all levels develop their skills and challenge others. We developed a solution that uses the smartphone camera to track and accurately estimate a players shot speed.

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.

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AI for Media

Chat-based data visualization solution for Mediatool

The solution provides a seamless experience in extracting and summarizing data through natural language queries in a chat interface that retrives needed information and visualizes it directly in the interface.

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.

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Automating wood quality assessment

Modulai developed a system to automatically determine wood quality and count age rings. By optimizing the sorting process based on wood quality, sawmills can efficiently allocate high-quality wood for premium products and lower-quality wood for other applications.

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Protected: Generating Headlines at Scale: Adapting to Diverse Media Brands

Modulai collaborated with Expressen to enhance their AI-powered headline generation tool. By using previously published articles as examples, we developed a data-driven solution that generates headlines and subheadings tailored to each brand’s unique style and tonality. This approach is expected to yield even better results as more advanced language models become available, with minimal additional development efforts.

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AI for Heavy industry

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 Heavy industry

On-device, real time car fuel lid detection  

We developed a CPU-optimized object detection model for on-device detection of fuel lids on cars.

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.

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AI for Retail

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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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 Retail

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.

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AI for Retail

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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AI for Life sciences

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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AI for Life sciences

Detecting underlying paroxysmal atrial fibrillation for Zenicor

Together with Zenicor Medical Systems AB, we trained a deep learning model to predict an underlying paroxysmal atrial fibrillation (AF) condition in patients.

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.

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AI for Heavy industry

Predicting when rented equipment will be returned

As one of the largest rental equipment providers in the Nordics and suppliers to thousands of construction sites, this client needed a system capable of predicting when lent out equipment was expected to be returned.

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

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AI for Media

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.

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AI for E-sports

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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AI for Retail

Personalized recommendations for Lindex

Lindex is dedicated to offer their customers a relevant and transparent personalized experience, in a multi-channel context. To be able to deliver on that front, a robust recommender system is considered a vital cornerstone.

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.

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AI for Proptech

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.

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AI for Life sciences

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.

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AI for Fintech

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.

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AI for Retail

Forecasting the success of fishing trips, for millions of Fishbrain users

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,

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