Accelirate Partners With Chirrp.AI to Deliver Enterprise-Class Chatbot Solutions
Accelirate Announces Its Partnership with Chirrp.AI That Strengthens Its Enterprise Chatbot Solutions Capability
“We have been delivering Business Process Automation solutions using RPA and AI technologies to our clients for the past two years and many clients have been asking about the feasibility of using chatbot solutions for internal and external use cases. We found the chirrp.ai product to be very interesting as they have been refining their capabilities over the last three years and have a competitive product,” says Ahmed Zaidi, managing partner and chief automation officer of Accelirate.
“There are many chatbot solutions out there, however, we wanted to make sure that the platform should be able to handle low-, medium- and high-complexity use cases. A moderate-complexity use case can simply be a chatbot answering employee or customer queries but should also be capable of integrating with back-office systems and push/pull data. The high-complexity use cases are where many clients envision using chatbots as an NLP/NLU-powered application-delivery mechanism which can handle complex user queries as well as application rules and workflows right from within the chatbot interface.”
“At chirrp, our mission is to enable enterprises to provide relevant and accurate chatbot conversations. We want to do that by using best-in-breed A.I. technologies to create the right enterprise-class solution. This has enabled enterprises to achieve desired results while providing the flexibility to use their choice of a broader cloud platform. Partnering with Accelirate will allow us to provide additional integration capability,” said Chirrp CEO and co-founder Mallesh Murugesan.
The chatbots can be initially configured and set up to understand structured as well as unstructured customer queries and provide them with appropriate answers without involving a human. For example, if a customer is unable to interact with the self-service portal for whatever reason, they could ask their question to the chatbot, for example, “What is my credit balance?” The chatbot can be trained to handle multiple variations of such questions through pre-trained data. The chatbot can then gather the relevant customer information, query the backend systems (which can be accomplished by using RPA robots) and present the information to the customer interactively. This entire workflow can be completed without human intervention thus reducing the call center costs considerably. However, the chatbots must be properly trained to handle corner cases and hand over to a human at the appropriate times otherwise a negative customer experience can quickly erode any benefits from deploying such technology.
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