Graphpath is a machine learning Platform that democratizes access to AI so companies of all sizes can implement powerful enterprise grade solutions. Our data science tools leverage machine learning to maximise Customer Lifetime Value (CLTV) by concentrating on four pillars:
Personalized Product Bundles to maximize AOV
Retargeting to increase repeat business
Loyalty to maximise customer’s lifetime span
Subscription models for lifetime span
We are proud to start by showcasing our 1st recommendation tool that helps ecom stores to drastically improve their conversion rate and AOV by personalizing bundles to high converting segments in real time. We utilize the store-based model approach to create our models only from your unique data to provide better recommendations since in the case of recommendations for a store with your unique set of products, “your data” could provide better recommendations than “more data”.
We are building a community of ecomm store managers as we want to ensure we maximise LTV by constant validation of our real users.
Based in San Francisco and Miami, GP has been funded by Rokk3r, an established VC/venture builder specialized in innovative personalization exponential technologies (ie. machine learning, Artificial Intelligence, Computer Vision, etc.) and it is comprised of a diverse multi-disciplinary team with strong leadership support from Rokk3r and a strong focus on Data:
Launched and scaled 3 ecommerce marketplaces, from Automotive and Real Estate to Home categories, in Mexico and Brazil and private labels through Amazon FBA in the US. Gemma worked in 3 Company Builders/ Venture Capital funds building solutions that leveraged AI.
Helped create and support over 40 software products involving Web, Mobile, IoT, Machine Learning, Computer Vision, Platforms, and Marketplaces.
Ph.D. candidate, MSc. Electrical Engineering at the University of Connecticut, B.S. Electrical Engineering at the Universidad Industrial de Santander. Diego has more than fourteen years of academic, governmental, and private-sector research and development and interests in various information technologies such as optimizing assets, operations research, predictive models, and data science in general.
Senior Full Stack Engineer