The conversational AI market is divided into three key categories: Conversational AI specialists, Giant cloud providers, and Smaller market players. All of these companies are working to solve the same problem: create first-class customer experiences. So, how do they differ in their approaches, and what role does conversational AI play in taking the digital shelf?
So, which approach is best for your company?
Conversational AI specialists offer the most comprehensive and sophisticated platforms for managing customer conversations, but their solutions are also the most complex and expensive to deploy.
Giant cloud providers offer conversational AI services, but they are not as comprehensive as the specialists. They provide basic NLU and dialog management capabilities and leave the Digital assistant development to their customers.
Smaller market players offer simpler and more affordable solutions for creating and managing customer conversations. They provide basic NLU and dialog management capabilities and rely on their customers to develop Digital assistants.
While Digital assistant creation tools are quite common, there are very few platforms that focus on deploying maintaining, and improving them. This means that most Digital assistants are not maintained for large periods of time and are eventually discarded.
Knowledge graphs, Graceful fallback, and Search Fallback are the different ways in which a conversational AI agent can answer questions for which they have not been trained. KAIron is one such platform that helps digital assistants with domain-specific data.
kAIron is one platform that focuses on this important aspect. You can create, train, deploy and monitor platforms in a matter of minutes. The best part is that you have tons of inbuilt integrations being added every week. In short, having the best chatbot does not make any difference if you don’t have a platform that can help you maintain it.
There are two categories of Digital assistants: One that works by following a series of rules, and another that uses artificial intelligence. AI-based Digital assistants are equipped with an artificial brain, also known as artificial intelligence. It is trained using machine-learning algorithms and can understand open-ended queries. Not only does it comprehend orders, but it also understands the language. As the bot learns from the interactions it has with users, it continues to improve. The AI Digital assistant identifies the language, context, and intent, which then reacts accordingly.
A web-based, microservices-driven suite called KAIron is intended to help RASA contextual AI assistants be trained at scale. It is intended to make the life of those who deal with AI helpers easier by providing a no-coding online interface for adapting, training, testing, and maintaining such assistants.
kAIron is open source. Contextual AI assistants’ adaptation to a particular area is one of the largest issues with chatbot adoption in businesses, according to users. This entails a lot of labor, such as reviewing documentation, validating the accuracy of responses, etc., when designing intentions. KAIron wants to assist users to accomplish this by offering a no-coding self-service framework.
kAIron focuses on technology that handles data pre-processing. These include developing knowledge graphs that can be used to generate questions, answers, and intents, as well as Question augmentation automatically and leverage relevant information for the agents in real-time. It also handles bot maintenance and post-processing, such as analytics and follow-up communications.
kAIron helps to identify the user context and then change engagement strategy using Proactive digital assistants. It is a highly data-driven service from value chains. kAIron also does grooming and change of lead conversion pipeline using Conversational AI which is a highly data-driven service from Digital Platform.