Suggestr
Amazon-level personalization for Shopify merchants
Suggestr is a self-serve, no-code personalization solution for the 20M+ e-commerce brands that sell on platforms like Shopify. Marketplaces like Amazon drive up to 30% of their sales from personalized surfaces (recommendations, bundles, frequently bought together, et cetera). They have large data science teams that build such in-house recommendation engines. However, DTC brands can't afford to do this, and they don't have enough customer data to make the conventional recommendation approaches work. Suggestr uses innovative multi-modal AI technology that sees and understands products (needs 100x less data), allowing it to work with brands of all sizes (SME, mid-market, enterprise). The solution takes less than 30 minutes to integrate and helps merchants increase online store revenues by up to 10% with AI-driven upsells and cross-sells.
Active Founders
Aditya Mehta
Aditya is a technologist that has built and sold recommender systems to banks. He has operational experience in e-commerce and knows the pain points of sellers first-hand. Before building Suggestr, he built a talent marketplace startup, where recommendations were used for talent matching.
Oleksii Sidorov
Oleksii is an AI Researcher with 15 papers published in ML and AI. He's been a part of the Vision & Language team at Facebook AI Research (USA), and previously the Experimental Psychology lab at the University of Oxford (UK) and KU Leuven (Belgium), applying AI to the analysis of human behavior. Currently, he's a technical co-founder at Suggestr, bringing top-quality AI recommendations to 20M+ SMEs.
