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.
This website uses necessary and analytic cookies to measure traffic.
We may place these for analysis of our visitor data, to improve our website and to give you a great website experience. For more information about the cookies we use manage the settings.
Fully individualized
experience through
AI Deep recommendation
Improve conversion by displaying relevant items to each visitor individually. Self‑learning AI evaluates the visitor behavior in real‑time.
Watch how Zoe.ai
recommendation works
Recommend relevant items
Individualize content for each visitor
Increase revenue by 5-25% by showing visitors what they most likely want to see and buy
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.
Own AI engine
Using digital body language and a true real-time personalization
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.
Quick onboarding
Plug and play Software-as-a-Service
A cloud-based service that is ready to be deployed and used. Easy onboarding and deployment are provided in the comprehensive documentation.
Zoe.ai is nextgen
recommendation engine
1999+ Generation 0
Basic personalization, but not in real-time. Do not adapt well, expert segmentation. Manual effort needed without additional gain.
2010+ Generation 1
Basic recommendations and no personalization. Trending bestsellers. Simple, working, but do not reflect the visitor’s intentions and preferences.
2020+ Deep recommendation
Self-learning Deep AI recommendation. Learning from behavioral data. Real-time shopper behavior is used. Automation and self-adaptation.
Why is Zoe.ai different?
In-session real-time analysis
Our technology gains knowledge and personalizes on-the-fly within one user session and then improves the recommendations.
Digital body language
Behavioral Signals help us to understand the shoppers' intent. Everyone is unique. We embrace it.
Self-learning AI
Fine-tuned next-gen technology using deep neural networks that outperform generic engines. The more it is used, the better models are provided.
Trusted by
How to Start with Zoe.ai?
1.Create an account
The registration process is simple and easy, after filling in basic information, the account will be created.
2.Provide an item catalog
Your users will receive recommended items picked from the catalog managed via our items API.
3.Record user interactions
Install JavaScript tracker to your application with a simple code snippet and start collecting users' behavior. Provide item feed through API.
4.Serve recommended items
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.
What do our customers say?
Heureka.cz
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
Pilulka.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
Pick a Product
Behavior-as-a-service
Use detailed behavioral signals in real-time in order to reveal suspicious and fraudulent users during online loans or in insurance claims processes.
Do you have any questions?
We are one click away.
Fully individualized experience through AI Deep recommendation.
For developers
Deep recommendation
Behavior-as-a-Service
Products
Contact us
All rights reserved by Lundegaard a.s.