Learn how a semantic layer made it possible for Quantatec to move away from hard-to-manage reports to sophisticated dashboards and AI chatbots, allowing their customers to quickly and easily get insights from their fleet data.
"Combining Large Language Models with a semantic layer not only enhances the ability of AI chatbots to understand and generate human-like responses, but also provides a simplified and meaningful representation of complex data, thereby improving user experience, efficiency, and security."
Quantatec previously used hard-coded dashboards to offer basic filters and report generation. But once they implemented the Cube Cloud semantic layer, they quickly delivered sophisticated dashboards, and most recently, an AI chatbot that their customers use to explore and ask questions of their data.
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- Quantatec's Cube Implementation: Quantatec, a fleet management company, uses Cube to solve performance issues and provide real-time updates for their customers. They developed a live dashboard and an AI chatbot that uses Cube to fetch data, providing quick and accurate answers to customers' questions.
- Retrieval Augmented Generation: Patterson Consulting uses a method called retrieval augmented generation, where an AI agent retrieves relevant information from a knowledge repository and augments the question to send it to the large language model for generation.
- LLMs and Semantic Layers: The combination of Large Language Models (LLMs) and semantic layers, like Cube, can create intelligent and secure AI chatbots. LLMs are crucial in understanding and generating human-like responses, while semantic layers provide a simplified and meaningful representation of complex data.
Watch the webinar at anytime to hear from Quantatec, Cube and Patterson Consulting. Register here.