SitesAdd was born and developed in Germany
SitesAdd was founded with a simple purpose in mind: to help your business grow and thrive. We rely upon proven SEO strategies to maximize the number of visitors to your website, in turn strengthening your brand and boosting your sales revenue.
SitesAdd.com was founded in 2017 by a team of software engineers, students at the RWTH Aachen University (Rheinisch-Westfalische Technische Hochschule Aachen), in the western part of Germany.
SEO managers and Internet marketing specialists were attracted from outside; we selected them from a large number of the best ones.
We have developed first-class software and an accessible service add website for obtaining backlinks, with a very easy-to-use module.
Our program meets any market requirements in the field of SEO promotion. The whole process is 100% automatic from the first step to the report. We believe that sitesadd.com will become your most trusted source for backlinks and SEO services.
The main specializations of the educational institution are the faculties of natural sciences, engineering and machinery. The following areas have been actively developing in recent years:
Research institutes were established in Aachen by such well-known corporations as Microsoft and Ford. This allowed us to educate rain thousands of students in these fields.
To date, the knowledge obtained by students is widely used in IT technologies. The main task of the Aachen University graduates was to develop a module that fully included many methods related to promotion of Internet resources. At the moment, the module contains virtually all possible methods to build backlinks.
The University graduates, candidates of technical sciences department of computer science Victor Mosenkis, Jan Rihme, research student Klaus Leppkes participated in the module development, and I wish to give special thanks to Somnath Sikdar, Ph.D. in Theoretical Computer Science from the Institute of Mathematical Sciences, Chennai, India on graph optimization problems.
Deep knowledge of efficient algorithms for graph optimization problems. Fundamental contributions for optimization problems on sparse graphs. Current research on probabilistic graph models of complex networks provides groundbreaking insight into a network structure.