Our cases
- Industry
- Areas of AI
- Capabilities
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.
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.
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.
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.
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,
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.
