Deep recommendation
Based on self-learning, AI models evaluate how visitors behave on the page and we can deliver personalized products for each visitor in real-time.
Improve conversion by displaying relevant items to each visitor individually. Self‑learning AI evaluates the visitor behavior in real‑time.
Improve an offer on the homepage, detail, cart or any other part of the shopping process by showing the most relevant items individually chosen for each visitor.
Visitors leave many behavior signals during their shopping and the AI engine enables us to recognize their intents and preferences. The engine dynamically analyzes current behavior within the session and offers relevant recommended items in true real-time.
A cloud-based service that is ready to be deployed and used. Easy onboarding and deployment are provided in the comprehensive documentation.
Basic personalization, but not in real-time. Do not adapt well, expert segmentation. Manual effort needed without additional gain.
Basic recommendations and no personalization. Trending bestsellers. Simple, working, but do not reflect the visitor’s intentions and preferences.
Self-learning Deep AI recommendation. Learning from behavioral data. Real-time shopper behavior is used. Automation and self-adaptation.
Our technology gains knowledge and personalizes on-the-fly within one user session and then improves the recommendations.
Behavioral Signals help us to understand the shoppers' intent. Everyone is unique. We embrace it.
Fine-tuned next-gen technology using deep neural networks that outperform generic engines. The more it is used, the better models are provided.
The registration process is simple and easy, after filling in basic information, the account will be created.
Your users will receive recommended items picked from the catalog managed via our items API.
Install JavaScript tracker to your application with a simple code snippet and start collecting users' behavior. Provide item feed through API.
Fetch individual recommendations on the fly through Zoe.ai REST API.
You control how the fetched results are presented to the user in your application.
Deploying Zoe.ai in the production verified that individual recommendations and a detailed understanding of our users' specific interests are the right way to improve engagement and final conversion. In an A/B test, revenue per session increased by up to 5% for the group of users who were offered products by Zoe.ai.
František Šeda, Product Head of Tribe at Heureka.cz
Zoe.ai has helped us offer more relevant products to customers in the recommendation areas. This makes the use of these areas significantly more efficient. We are now looking at other places where we can incorporate AI.
Jozef Filo, E-commerce product manager at Pilulka.cz
Together with Lundegaard we reviewed our solution, got useful tips and recommendations that potentially saved us from having serious production issues later. Furthermore, we are now confident that we chose the right technology and approach for our solution.
We were looking for professional and experienced consultants that have real-world experience with Apache Kafka and the guys from Lundegaard had proved themselves to be exactly that. They put in extraordinary effort in order to understand our solution and make it ready for production.
Nermin Šehić, System Designer, ZIRA
Based on self-learning, AI models evaluate how visitors behave on the page and we can deliver personalized products for each visitor in real-time.
Use detailed behavioral signals in real-time in order to reveal suspicious and fraudulent users during online loans or in insurance claims processes.
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