Generative AI Assistant

Problem: F50 Sales and marketing employees spend countless hours accessing multiple internal and external data sources to search for information, to analyze data, and to create content.

Solution: the Argus team created a web based application that can be accessed by the sales and marketing teams via desktop or mobile so that the teams can search and collate information from various data silos using simple natural language prompts. The teams can now access 1000s of pages of proprietary technical product data and can search, edit, and create content on the fly.

The Argus team used a Retrieval-Augmented Generation (RAG), a novel approach to natural language understanding and generation. Unlike traditional generative models, RAG incorporates a retriever module, enabling it to retrieve information from external knowledge sources during the generation process. This integration aims to enhance contextual relevance, accuracy, and informativeness in generated content.