Poor search functionality on e-commerce sites could be costing you up to one-third of potential sales growth, with unhelpful search results occurring nearly a third of the time and resulting in significant customer attrition. By implementing generative AI models, even basic ones, businesses can address issues such as misspellings and synonyms, improving search accuracy and customer satisfaction with minimal upfront investment, potentially recovering lost revenue and accelerating growth.

Led by Engenai CEO and co-founder, Irena Stetska, Engenai stands out with its commitment to democratizing AI, transformative interaction with information, flexibility, cost management, and ongoing innovation, targeting e-commerce, enterprise, digital marketing agencies, and technology/product companies. Stetska’s excitement for AI’s practical applications, Engenai’s mission to democratize AI, and the talented team driving the company’s vision contribute to a thrilling journey shaping the future of technology and business solutions.

Engenai is dedicated to optimizing search and discovery experiences for your customers by guiding them through personalized journeys. By integrating Generative AI, we can seamlessly cater to both specific and exploratory queries, leveraging client data to provide tailored suggestions and enhance user satisfaction, ultimately driving conversions and maximizing engagement.

A conversation with Engenai co-founder, Sergio Gonzales. Engenai offers AI-powered search intelligence to enhance customer discovery and revenue generation while protecting competitive advantage. By democratizing access to AI, Engenai enables businesses to leverage flexible, cost-effective solutions for accelerated time to market. Future plans include expanding offerings in retail, commerce, and enterprise, integrating cutting-edge AI models to continually enhance services for clients.

To address the challenge of limited user information for recommendation engines in a privacy-focused world, leveraging Large Language Models (LLMs) alongside vector databases allows customers to express their needs naturally, akin to in-store interactions, enabling efficient handling of broader queries. This solution, requiring minimal changes, enhances user experience by prioritizing customer voice and needs over intensified technological tracking.

Engenai offers an effective solution to enhance e-commerce search capabilities by integrating LLM technology, improving the customer experience through more relevant and user-friendly search results without requiring extensive system changes. This approach addresses the limitations of traditional search and filter functionalities, ultimately leading to higher conversion rates and increased revenue for e-commerce businesses.