Quarticon AI Tools

Quarticon was founded in 2010 with the mission of building an autonomous platform of products that improve the functioning of online shops and help them earn more.

The company’s main asset is modern technology, which is based on the use of artificial intelligence and machine learning algorithms. This technology allows the services to be operated on an ongoing basis with minimal human resources.

It supports the operations of over 300 e-shops in 20 countries around the world, including Poland, the Czech Republic, the Baltic States, France, Croatia, Serbia, Hungary, Slovakia, Greece, and the UK.

Quarticon was acquired by Centraals in 2025.

Quarticon's AI Tools

Product recommendations

Product recommendations personalize product and content offerings for each user individually

Search engine

An intelligent AI-based search engine that understands users much better than other search engines

AI-mails

Showcase tailored products that resonate with each recipient and include super smart AI-driven suggestions to enhance their shopping experience

Storefront

Showcase in your dedicated shop view products that resonate with micro communities of influencers youe are cooperationg with

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Machine Learning algorithms

Quarticon’s Smart Search employs several machine learning algorithms for various purposes, but a key algorithm used for anomaly detection is the Random Cut Forest (RCF).

This algorithm is an unsupervised machine learning method that helps identify anomalies in data by modeling a sketch of the incoming data stream and generating an “anomaly grade” along with a “confidence score” for each data point. This approach allows it to differentiate between anomalies and normal variations.

In addition to RCF, Quarticon’s Smart Search supports other machine learning functions, including:

  • K-means Clustering for data segmentation and grouping.
  • Logistic Regression for predictive analytics.
  • and Quarticon’s custom algorithms.

Quarticon’s Smart Search also integrates with vector databases to enhance its capabilities in generative AI applications, employing methods such as Facebook AI Similarity (FAISS) and Non-Metric Space Library (NMSLib) for vector search tasks.

Quarticon’s AI Product Recommendation uses several algorithms to enhance user experience and optimize results. A key algorithm in this product is Collaborative Filtering. It leverages user-item interaction data. It is user-based, for recommendations are based on the preferences of similar users, and item-based, where it looks at similarities between items. One of the technique used is the k-nearest neighbors (KNN) algorithm.

In addition to CF, Quarticon’s AI Product recommendations supports other machine learning functions, including:

  • Content-Based Filtering –  a method that focuses on the attributes of the items themselves and the preferences of the user.
  • Matrix Factorization  techniques, like Singular Value Decomposition (SVD), break down a large user-item interaction matrix into lower-dimensional matrices.
  • Deep Learning as Neural networks can capture complex relationships and patterns in large data sets.
  • Association Rule Learning in market basket analysis, as this algorithm finds interesting relationships between products sold together. 

World leading AI product recommendations and AI search engine

World leading AI product recommendations, AI search engine and much more. Try Quarticon’s AI tools.

Quarticon AI tools