How Wovenware Does it
Support your human workforce by employing chatbots to automate repetitive questions, personalize the experience and free staff up to handle the tough stuff.
Wovenware follows a human-centric approach to chatbot development, to augment human capabilities and deliver measurable business value.
We rapidly build and test the performance of your chatbot to ensure that it is meeting your needs. But we don’t stop there. Once your chatbot is deployed, we continue to refine and test your chatbot to ensure that it is learning through new data and evolving to meet your changing needs.
Before writing the first line of code
What Companies Should Consider when Implementing Chatbots
As organizations become more dependent on chatbots, it’s essential that they provide the most intuitive, engaging user experience. Below are some tips to accomplish this
Using the voice of our customer as a north star
Learn how companies like yours
are winning with Wovenware
Check out how other companies have overcome digital challenges that may be similar to your own and how we did it together.
Leveraging Chatbots To Allow More Meaningful Connections With Students
Inter-American University
Looking for better ways to engage with students and provide faster communication, the University turned to us to build a chatbot that could field frequently asked questions 24/7 while allowing staff to dedicate more time to provide more meaningful guidance to students.
Chatbots: An insight Into Their Evolution
A group of people at MIT developed the first chatbot, known as “the mother of chatbots,” Eliza. Though different from today’s chatbot, it served its purpose of imitating human-like interactions, by using a model known as the decision tree. Nowadays, chatbots, at their root, are a form of AI capable of answering frequent questions from consumers, citizens, or customers. But, exactly how far have they come?
Teaching Chatbots to Do the Right Thing
Along with the great opportunity that AI provides comes great responsibility. As we develop and train algorithms, we have a responsibility to make sure that we are creating ethical human stand-ins and that we are not inadvertently imbuing them with our own biases.