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A sample conversation if SMS Assist was implemented with a tech company.
Inspiration
In large corporations, such as RBC, the help desk gets called hundreds phone calls every hour, lasting about 8 minutes on average and costing the company $15 per hour. We thought this was both a massive waste of time and resources, not to mention it being quite ineffective and inefficient. We wanted to create a product that accelerated the efficiency of a help-desk to optimize productivity. We designed a product that has the ability to wrap a custom business model and a help service together in an accessible SMS link. This is a novel innovation that is heavily needed in today's businesses.
What it does
SMS Assist offers the ability for a business to automate their help-desk using SMS messages. This allows requests to be answered both online and offline, an innovating accessibility perk that many companies need. Our system has no limit to concurrent users, unlike a live help-desk. It provides assistance for exactly what you need, and this is ensured by our IBM Watson model, which trains off client data and uses Machine Learning/NLU to interpret client responses to an extremely high degree of accuracy.
Assist also has the ability to recieve orders from customers if the businesses so chose. The order details and client information is all stored by the Node server, so that employees can view orders in realtime.
Finally, Assist utilizes text Sentiment Analysis to analyse each client's tone in their texts. It then sends a report to the console so that the company can receive feedback from customers automatically, and improve their systems.
How we built it
We used Node.js, Twilio, and IBM watson to create SMS Assist.
IBM Watson was used to create the actual chatbots, and we trained it on user data in order to recognize the user's intent in their SMS messages. Through several data sets, we utilized Watson's machine learning and Natural Language & Sentiment analysis to make communication with Assist hyper efficient.
Twilio was used for the front end- connecting an SMS client with the server. Using our Twilio number, messages can be sent and received from any number globally!
Node.js was used to create the server on which SMS Assist runs on. Twilio first recieves data from a user, and sends it to the server. The server feeds it into our Watson chatbot, which then interprets the data and generates a formulated response. Finally, the response is relayed back to the server and into Twilio, where the user recieves the respons via SMS.
Challenges we ran into
There were many bugs involving the Node.js server. Since we didn't have much initial experience with Node or the IBM API, we encountered many problems, such as the SessionID not being saved and the messages not being sent via Twilio. Through hours of hard work, we persevered and solved these problems, resulting in a perfected final product.
Accomplishments that we're proud of
We are proud that we were able to learn the new API's in such a short time period. All of us were completely new to IBM Watson and Twilio, so we had to read lots of documentation to figure things out. Overall, we learned a new useful skill and put it to good use with this project. This idea has the potential to change the workflow of any business for the better.
What we learned
We learned how to use the IBM Watson API and Twilio to connect SMS messages to a server. We also discovered that working with said API's is quite complex, as many ID's and Auth factors need to be perfectly matched for everything to execute.
What's next for SMS Assist
With some more development and customization for actual businesses, SMS Assist has the capability to help thousands of companies with their automated order systems and help desk features. More features can also be added
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