Abto Software chatbot experts will suggest the most efficient implementation strategy for your business based on your:
– Meet stakeholders, confirm business priorities
– Evaluate available resources, policies, user audiences
– Define target KPIs
– Perform initial data analysis
– Design principal architecture and key AI/NLP technologies
– Define version 1 scope, plan milestones, costs, and duration
– Plan production delivery and maintenance for version 1
– Approve the entire roadmap with the customer
– Develop essential flows/cases to get to market quickly
– Collect feedback and store conversational data
– Implement efficient user interfaces for desired communication channels
– Implement continuous training
– Develop new flows/intents
– Adjust the solution based on user feedback from previous releases
– Begin planning and implementation of a contextual AI-based chatbot (human-like)
Our AI/NLP developers have delivered a conversational AI chatbot to automate customer service routine. It uses natural language processing (NLP) and deep learning techniques to recognize customers’ intent behind text inquiries. The solution can take a short conversation and do an upsale. German and English languages are supported. The customer service chatbot is able to display the user profile and updated balance within a chat interface.
Business value of implementing conversational AI chatbot:
With more than 1 mln users every day seeking for expert’s help on the platform, JustAnswer is always looking for ways to improve and augment their services.Having a chatbot as the first touchpoint on the platform helped our partner provide better, more personalized service to their customers.The virtual assistive technology has been in beta testing for three years being trained on 16 million questions and answers in the company’s database.
Business value of implementing AI-driven assistant:
Choosing the right technologies and languages for chatbot implementation is crucial for meeting short- and long-term business goals. Consider factors like exporting training data, configuring rules, and transitioning to a more human-like contextual solution, etc.