3rd Party Data, Analytics, Development
Machine Learning

Business Overview

REFLECTIZ, a technology company that provides the avenue for you to control what goes on behind the scenes of your websites, activities being done by third party service providers, and open–source frameworks. REFLECTIZ solution works in the background of your site seamlessly with no installation needed collecting information, actions and behavior while actively monitoring the whole process, providing advanced metrics and interactive management dashboards for the site owner or the third party installed on the site. This goes a long way to provide real–time monitoring of changes that affect performance and improve the revenue and income being generated by your site. Our platform carries out scans on sites using a well–defined profile, just like any other user on your sites to provide relevant behavioral site data. REFLECTIZ, Founded in 2016 which through many years of experience combined with advanced ethical hacking skills and an in–depth knowledge of the workings of the website has been able to evolved technologically and proofed themselves master of website solutions.

Operating Status
July 2016
Business model
Offering type
Funding stage
Series A
Business stage
Total funding
$6.5 Million
3rd Party Data, Analytics, Development, Enterprise

Funding Rounds

New wpDataTable

Date Announced

Funding Round

Amount Raised


December 2020
Series A
$5 M
March 2018
$1 M

AI Technology Stack

AI Description

We’re currently using AI for two main purposes. 1) By using ‘unsupervised learning’ approach, we’re categorizing websites around the world to a small amount of groups (around 20-30). Afterward we’re doing deep-analysis of few site in each group and labeling the group. by that we are able to auto-learn new sites or better categorize known sites without the need to analysis and can answer the question of “who is like me, who is better than me?” in more easy way (on technology aspects). We’re using the k-mean clustering algorithms using TensorFlow. The categorize process is still not on production. 2) Our second usage is anomaly detection in our big-data database in order to determent if any uncommon event has happen. We have many “human” rules today in the system and we use the anomaly detection to alert us for issues (or good changes) that we weren’t aware. We’re using Microsoft Azure Machine Learning Studio. We have more than 50 virtual servers gathering data in azure cloud, by our unique driver on browser. Our data includes many indicators from multiple (thousands) sites on the world, feeding our data-lake. Our data-lake contains data of the last 3 months more or less and until now we have gathered around 200TB with addition of 1TB per day. and we don’t have any rights issue with our data. The AI helps us to break the need to optimize site after site and build designated rules. With this amount of data, we had to using AI in order to make the system working. While we have some rules written by us, the AI help us to scale and to support many sites, different sites around the world. In our current stage, we’ re testing it ourselves comparing the analysis of the site done by us and the results from our AI algo. And most of the anomaly alerts are still being validated by us

AI employees
AI application
Vertical AI
AI types
Machine Learning
ML types
Unsupervised Learning
AI algorithms
Clustering, K-Means, Learning Classifier System
AI tools
Microsoft Azure Machine Learning Studio, TensorFlow
AI hardware

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