The AI capability 'suggested replies' in Deskhero utilizes advanced natural language processing and machine learning algorithms to generate relevant and context-specific responses. Here's how it works:
Use case 1
Data Analysis and Embeddings - Deskhero analyzes previous tickets, knowledge base articles, and other relevant data to generate embeddings vectors. These vectors capture the semantic meaning of the data.
Use case 2
Vector Database - The generated embeddings vectors are stored in a vector database, allowing for efficient and fast retrieval of similar and relevant data.
Use case 3
Query Processing and ChatGPT - When a new ticket or search query comes in, Deskhero runs it against the OpenAI Embeddings API to generate a query vector. The system then searches the vector database for similar and relevant data. The relevant data is sent to ChatGPT, an advanced language model, along with the original question. ChatGPT generates a response based on the attached data, which is presented as a suggested reply or search response to the user.
