Although a rage, chatbots continue to underperform as they struggle to fully understand user queries and the context of the question. Majority of the chatbots available today are rules-based and rely on scripted responses that lead to a poor experience. As a contrary Natural Language Processing (NLP), chatbot is the future of human to machine conversation. While the market is replete with NLP engines from behemoths like Amazon, Microsoft, and Google, they are black boxes. Training those engines with company specific training data often lead to inconsistency, regression, and ambiguity in understanding the human questions. What it lacks is a solution that can analyze and benchmark a company’s chatbot training data and provide insights into the areas where it falls short.
QBox helps companies assess the quality of the NLP training data and recognize the ability of a chatbot to serve customers efficiently. As QBox provides visibility into the impact of a change or addition to the training data of the NLP data model, companies can make informed decisions on scaling their chatbots’ domain knowledge and understanding performance.
Offering 10 free trials to test NLP data models, QBox lowers the barrier to entry into the chatbot arena for any company. “Companies can now take control of their chatbot’s quality and release their solutions into the market quickly,” says Chris Sykes, CEO, QBox. They can also track and analyze the incremental progression of understanding of the data models after every training phase. This transparency helps them to scale with a high degree of accuracy while making chatbot understanding as human-like as possible. Industries from banks to retail to professional services among others can reduce the human capital and improve customer experience with QBox.
At the outset of a client engagement, the NLP data models are exported to QBox’s SaaS platform where they are tested for correctness, confidence, and clarity of the model. The test identifies both the areas in the training data that are performing well and the ones underperforming or creating confusion where changes are required. If there are any regressions in the data models after comparing to previous tests, the user retests them and makes appropriate changes to the models. After all the appropriate modifications are made, users run another test on the new version of the training data to check for improvements. These tests enable chatbots to clearly understand the context of the questions and predict the correct answers. QBox provides a score ranging from 1 to 100 based on which companies can retest their models. This helps them to improve the benchmark from the KPI (correctness, confidence, and clarity) of their base performance and avoid regression or ambiguity in the future. Apart from measuring the performance of the training data, QBox has capabilities to monitor live interactions that further helps in delivering relevant experiences.
Acknowledging the limitation of a machine in interacting with a human being who is more dynamic in terms of interactions, QBox has built a separate platform that allows companies to quell the negative experiences. QBox works with all major NLP providers and its capabilities complement those providers’ offering. That is why QBox integrates seamlessly with NLP platforms from providers like Microsoft Azure, IBM Watson, Amazon AWS, Google, Facebook, and open source technologies.
Having an emerging partner ecosystem with Microsoft and Intel, the company plans to introduce more variants of its platform in different industries such as life sciences and financial services.
Description Provides a natural language processing (NLP) driven platform for businesses to fully utilize, test, and manage powerful conversational AI chatbots to make informed decisions consistently and at scale