JeanWear has an extensive knowledge base that includes product manuals, policies, and FAQs. Managing this knowledge base and finding relevant information quickly can be challenging. JeanWear uses Deskhero's AI capabilities to overcome this challenge.
The AI uses OpenAI Embeddings from the knowledge base articles, uploaded files, and scraped website data to understand the content. When a customer support representative searches for information, the AI uses these embeddings to provide the most relevant results.
Deskhero's AI also generates suggested replies based on the knowledge base. When a representative is responding to a ticket, the AI suggests possible replies based on the information in the knowledge base, significantly reducing the response time.
JeanWear also uses Deskhero's custom fields feature to categorize their knowledge base articles. This makes it easier for the representatives to find specific types of information.
Overall, Deskhero's advanced AI capabilities have made it easier for JeanWear to manage their knowledge base and provide quick and accurate responses to their customers.
How does Deskhero's AI improve knowledge base management?
Deskhero's AI uses OpenAI Embeddings to understand the content of the knowledge base. It uses these embeddings to provide relevant search results and suggest possible replies to tickets.
How does Deskhero's AI generate suggested replies?
When a representative is responding to a ticket, Deskhero's AI suggests possible replies based on the information in the knowledge base and the context of the ticket.
Can Deskhero's knowledge base be categorized?
Yes, Deskhero allows you to use custom fields to categorize your knowledge base articles. This makes it easier to find specific types of information.
* This article provides an example of how a fictive company in the Manufacture of Jeans and Denim Wear industry is using Deskhero. Sign up now for free to discover how it can benefit your own organization.