A global distributor with multiple locations throughout the U.S. and internationally sought to enhance its processes.
With a small sales team managing hundreds of orders per day across hundreds of thousands of SKUs located in various locations, this distributor faced a variety of challenges, including:
In fewer than 20 days, the Crowe AI transformation team went from discovery to deployment using the following timeline:
Our team employed various Microsoft AI tools to build an easy-to-use product search chatbot in the Microsoft Teams™ platform by loading and interpreting both structured and unstructured data from the company.
The chatbot operates in three easy steps:
The company’s process for locating a quote or order was inefficient and prone to delays, which resulted in a lack of productivity and a decrease in customer service.
Part information was fragmented across multiple resources, including both structured datasets from the enterprise resource planning (ERP) system as well as unstructured documents such as specification sheets and manufacturer catalogs.
When a customer submitted a quote or order, the sales team began its search in the ERP system. If team members couldn’t find the part, they often resorted to an internet search. However, that approach bypassed the wealth of information available in those specification sheets and manufacturer catalogs.
To address the inefficiencies in the part-finding process, the Crowe AI transformation team used the company’s existing Microsoft technology stack and integrated generative AI to identify, extract, and search part information from unstructured documents.
The company provided an export of the parts listed in its ERP system, as well as a variety of PDFs with product information. Our team then used generative AI to extract information and create search indexes. These indexes allowed salespeople to efficiently search for all data in one place.
With help from our AI transformation team, the company: