At Deepdesk we develop AI technology that is not aimed at replacing people, but to aid them. The focus of our technology is to make jobs less tedious by helping to eliminate repetitive tasks. Currently, we are focusing our attention on the contact center space, where we enable contact center agents working within digital channels (chat, WhatsApp, Messenger, etc.), email, or voice to reduce the amount of time they spend searching for the best response to a customer query, and the time they spend typing or sharing it. To achieve this, we train our neural networks with our customers' historic data from previous conversations to provide automated suggestions with the best answers to customer queries.
Because our AI is able to understand customer conversations, Deepdesk’s Agent Assist presents the contact center agent with the best possible answer to any customer query, as well as links to related content that may live in their internal knowledge bases, public facing FAQ’s, or any other resource they use. By presenting the most relevant information to the agent for the specific questions being asked, Deepdesk can drastically reduce training times and make every agent an expert from day one.
Using real-time analysis paired with customer satisfaction data, our AI also learns which responses at which time lead to the most positive customer outcome and uses this information to continue to train our AI in real-time. This means more happy customers, which translates into higher customer satisfaction scores. Increased happiness also extends to the agents: a direct result of using Deepdesk has been an increase in agent happiness and retention.
Deepdesk technology is based on our proprietary unsupervised machine learning engine. Since autocomplete technology is expected to respond in real time, we developed an ensemble algorithm that combines a number of technologies and AI models to provide suggestions under 100ms. Deepdesk’s unique approach also always ensures relevant content with automatically and frequently updating models, using recent conversation data.