RAG in Logistics

An international logistics company handles a large amount of information related to logistics statuses, invoices and delivery data. Together with the innovation and technology area, they require a solution with generative AI.

Context

A digital solution is created where the company's logistics information is uploaded and made available through a platform with artificial intelligence. This solution corresponds to a RAG or Retrieval-Augmented Generation.

Esta plataforma con Inteligencia Artificial permitirá a los administradores y colaboradores consultar sobre productos específicos, obteniendo información sobre facturas, estados logísticos y visualización de PDFs.

RAG or Retrieval-Augmented Generation process

Reports - Invoices

Content Split

Requestor Prompt

Information Delivered

What does a RAG do?

RAG or Retrieval-Augmented Generation. Imagine you have a huge library with all the books you could ever need.

When you have a question, you would not only like someone to answer it, but also to search through the books to give you the best answer possible. That's basically what a RAG does.

Project objectives

• Fast and efficient access to logistics and invoice information.
• A centralized and easy-to-use system for managing and querying data.
• Reduction in time spent searching for and verifying information.

Problems to be solved with Artificial Intelligence

• Innovate and be evangelizers in digital solutions with AI in the company.
• Optimize manual and repetitive processes that consume a lot of time.
• Facilitate the search for specific information quickly.
• Governance of information dispersed in multiple systems and digital and manual formats.