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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.

Schedule a demo
Meet our successful clients in E-Commerce and Finance
heurekaLogo - logo
Pilulka - logo
DecoDoma - logo
Astratex - logo
Allegria - logo
Cityzen - logo
Essox - logo
Moneta - logo
Generali - logo
heurekaLogo - logo
Pilulka - logo
DecoDoma - logo
Astratex - logo
Allegria - logo
Cityzen - logo
Essox - logo
Moneta - logo
Generali - logo

Our clients

Download case study
View references

Watch how Zoe.ai
recommendation works

video

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.

How to Start with Zoe.ai?

1.
Create an account

Create an account

The registration process is simple and easy, after filling in basic information, the account will be created.

Schedule a demo 

2.
Provide an item catalog

Provide an item catalog

Your users will receive recommended items picked from the catalog managed via our items API.

About Items API 

3.
Record user interactions

Record user interactions

Install JavaScript tracker to your application with a simple code snippet and start collecting users' behavior. Provide item feed through API.

Install tracker 

4.
Serve recommended items

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.

Recommend items 

What do our customers say?

Heureka - František Šeda - logo

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 - Jozef Filo - logo

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

Zira - logo

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

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.

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.

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