Conversational AI and Intelligent Virtual Agents are powered by machine learning algorithms. In order to perform effectively and deliver a compelling customer experience, IVA models require vast amounts of training data. The more data the model is trained with, the better it will perform.

Data labeling and annotation is a critical part of training AI models. The process of labeling data is often complex and requires a technical understanding of the data labeling process.Labeling data is typically time consuming and a resource intensive activity. It is not uncommon for data scientists to spend hours labeling data for their models.

The problem is that the data scientists who train AI models are in short supply and can also be expensive resources to employ. Data Scientists also tend to have more experience with the machine learning itself and less knowledge around customer experience or domain expertise with the application.

With many Conversational AI applications, vertical domain knowledge and context are critical to performance. Even when trained by data scientists, AI models are often limited by the domain knowledge of the person who trained them. By democratizing the training of Intelligent Virtual Agents, organizations can quickly scale up their AI applications.

Introducing QAi, Xaqt’s new tool that allows anyone in your organization with domain knowledge to train AI models for Intelligent Virtual Agents.

QAi is an acronym for Quality Assurance in Ai. It is both an AI model training tool and a quality assurance tool that allows non-technical staff to make corrections to AI models.

The idea behind QAi is that anyone can train Intelligent Virtual Agents without having to understand the underlying machine learning algorithms. This is a significant step towards democratizing AI and allowing more customer facing roles to interact with Intelligent Virtual Agents.

QAi is a HITL, or Human In The Loop solution. It works similar to a traditional Quality Assurance product for monitoring and scoring agent calls, however it includes an easy to use data annotation tool. This means that anyone can score an interaction handled by an Intelligent Virtual Agent with a similar process as live agent calls.

QAi can be integrated into the agent desktop and interactions that require review are assigned as tasks in a workflow. This means agents can be dedicated to reviewing IVA interactions or they can be automatically assigned these tasks during periods of slow call volume.

QAi works across all digital channels, including voice, chat and SMS. Voice calls also reference the call recording to that an agent can listen to the actual interaction.This means that a company can use QAi as part of quality assurance process for all their Intelligent Virtual Agent applications.

The combination of QAi’s ease of use and the ability for customer service agents to contribute to AI training, enables companies to quickly scale up their use of Intelligent Virtual Agents and ensure their continuous improvement.

QAi also allows companies to monitor how their IVAs are performing, which is a critical step in improving the management of AI models. Continual performance monitoring takes place with Xaqt's Conversational Experience Analytics (CXA) portal. CXA is a platform that provides real-time insights into the performance of AI models and Intelligent Virtual Agents. CXA provides visibility into areas such as sentiment analysis, intent detection, and machine learning performance. This visibility is used to inform decisions about the deployment of new AI models.

Improving the management of IVAs can improve their performance and produce better customer experiences and more effective interactions.