Gravity is a personalization engine vendor offering a product portfolio called Yusp, filling multiple needs using the same underlying technology. Yusp has all primary product modules and marketing modules as digital personalization engine defined by Gartner (Market Guide for Digital Personalization Engines) as well as several other product modules, which enable to personalize ten different business models including also the physical shopping experience.
The team behind Yusp, as an R&D company, has been focusing on Data Science since 2006: the research and development of recommendation algorithms and their applications to a multitude of business models. The core of this team comprises of the same data scientists that, as the leaders of The Ensemble team, tied for first place in the Netflix Prize competition and are now also key members of the EU-funded CrowdRec project which aims to develop the next generation of recommendation systems.
Yusp has never lost any A/B test against competitors or in-house solutions thanks to Yusp’s exceptional algorithmic portfolio, which incorporates predictive, adaptive learning analytics to anticipate future behavior or estimate unknown outcomes. And Yusp’s scalable, performance-oriented architecture enables real-time responses within milliseconds (with SLA guarantee) all around the globe from one of the 4 data centers.
Supported by its worldwide strategic partnership with Deloitte, the company is a prominent player of the next generation digital transformation and personalization landscape.
To enable businesses to be relevant and not forgotten.
Our vision is to enable enterprises with large clientele to engage with their consumers by using exceptional machine learning algorithmic portfolio, which incorporates predictive, adaptive learning analytics to anticipate future behavior or estimate unknown outcomes on a low-cost, low-risk basis.
Domonkos is the CEO of Gravity R&D. He received his PhD in computer science in 2000, then he worked in the academia (Technical University of Budapest, Humboldt University in Berlin) on machine learning techniques and control problems as a researcher and project leader.
Domonkos won several international data science competitions on the field of recommendation systems, text and data mining. He founded Gravity R&D with his fellow researchers in 2007 to exploit the experience learnt during the participation in the Netflix Prize.
With his lead the company achieved its break-even point in 2014, and keeps the flexibility and openness of the start-up atmosphere and an spirit of innovative forward thinking in the company.
He has 22 years of business development, new technology launch and sales operations leadership experience in a range of industries as top manager in his former positions at Procter & Gamble, Metro Cash & Carry, and Magyar Telekom.
As a Managing Director of Gravity Bricks&Mortar, he had a significant role in creating a global partnership with Deloitte in 2016 to commercialize and implement the products of Gravity for retail, telecommunication and banking industry worldwide. Since October 2016 he has been responsible for marketing and sales at Gravity R&D.
Bottyán has been working with Gravity from the very beginning – originally as a data miner and currently as leader of the product development and marketing at the company.
He was member of the data mining team Gravity which finished (tied) with the highest performance in the Netflix Prize which was the most reputable open competition for the best collaborative filtering algorithms.
His main interest is large scale machine learning and he is constantly focusing on how to use machine learning and data mining techniques in real life problems and how to productize these solutions.
Istvan is the leader of the core software development. He ensures that RECO is scalable, the source code is consistent, and bugs are caught before hitting the production environment.
István has been a core member of Gravity since its inception. He received his PhD in recommender systems summa cum laude from Budapest University of Technology and Economics, and his MSc degree also degree summa cum laude from the same university.
Benedek got his Diploma at the Budapest University of Economic Sciences and started his career at Deloitte&Touche. After working there for two years as an auditor, he moved to the FMCG sector as a controlling professional and spent four years at Tesco and two years at Heineken.
In 2008 he became the Controlling Director of the biggest Hungarian Real Estate Group (Futureal Group) and worked there for four years. From 2011 he was a Supervisory Board member of the Group’s Venture Capital Found (Finext). In 2012 he started his own business as a controlling advisor for Hungarian SMEs (main partners: Hilltop Vinery – the biggest Hungarian wine exporter, Ubichem Group – one of the biggest R&D companies in the Hungarian chemical industry).
Keeping his own business, he became the CFO of Gravity R&D in March 2014.
As the leader of the data science team, Balázs is responsible for research and data mining activities in Gravity. He coordinates the team and also conducts his own research in the field of machine learning and data mining. The research revolves around (1) developing advanced recommender algorithms to make Gravity’s recommender engine even better; and (2) exploring new fields and application areas for recommender systems. He also coordinates and consults data mining projects (e.g. customer data analysis, fine tuning) within the company.
Balázs received his MSc in computer science and engineering from the Budapest University of Technology and Economics with highest honors in 2011 and is working towards finishing his Ph.D.
As a key member of data mining team, György is mainly responsible for customer specific data mining research and coordinating the technical integration of CERT. He has six years of experience in data mining and machine learning. Before joining Gravity he was working on various data and text mining projects as a researcher-developer.
In Gravity he started as a data miner, but he also participated in many core development projects, and he also has a deep knowledge of the SaaS operation. He became the head of web integration team at Gravity in 2013. In this position, he is responsible for the integration process, client specific developments, and fine-tuning of the recommendation performance.
Gábor has 17 years experience on the field of architecture design, scaling and operations. He is responsible for the deployment processes, training sessions and additional services which may come with the solution, including performance tuning, infrastructure questions and security measurements.
Gábor is a technology enthusiast and likes to work close with customers in multi-cultural environments. Previously he filled major technology management roles at Ustream, the Telemedia Group, where he was working in several countries, also supported many Fortune 500 companies.
He has been working for Gravity R&D for almost 5 years, he is one of the key employees of Gravity, his deep technical knowledge, experience and people management skills play a major role of growing the business day by day.
Machine Learning Engineer – Budapest
DevOps Engineer – Budapest
Data scientist & recommender system expert – Budapest
Front End Developer – Budapest
Senior Software Engineer – Budapest
Software developer – Budapest
Solutions Engineer – Budapest
Gravity R&D has received its “Series B” financial investment from the European Investment Found under the JEREMIE (Joint European Resources for Micro to Medium Enterprises) program through the Budapest-based venture capital fund management firm PortfoLion.
Project Title:“Semantic Based Intelligent Entertainment Activity Planner (MAGICIAN project)”
International EUREKA project ID:MAGICIAN, HU_12-1-2012-0008
The amount of support: 32 895 386 HUF (Intensity: 48.34%)
Receiver: Gravity Research & Development Kutató-, Fejlesztő- és Szolgáltató Zrt.
International cooperating partners:
Türk Telekom (Turkey)
The project “Semantic Based Intelligent Entertainment Activity Planner (MAGICIAN project)” is funded under the EUREKA Program.
The goal of the MAGICIAN project is to build a semantic based intelligent recommender system that provides the end users with a set of personalized quality entertainment, leisure and travel options.
Collects the entertainment, leisure and travel options from multiple sources
Executes real-time data processing while collecting data
Provides intelligent recommendations based on user/group profiles and interests
Improves the currently available static and non-personalized information based solutions significantly
Provides the sector with modularly applicable solutions
Ensures the protection of personal data
The realization of this project brings a significant change in the field of leisure and entertainment related recommendations.
The duration of the project: 01.12.2012 – 20.11.2014
Supporter: National Innovation Office
Contributing organization: National Innovation Office