@lachlankirkwood Thanks for the question! We use our own NLP model based on deep learning, neural network and mix of statistical methods. For extra precision we have also added correction layer created by multi-lingual linguistic experts. Our intent detection engine uses a language model generated from a set over 24 billion statements from Internet users and articles collected in the SentiOne database - we selected millions of statements from sources such as forums, websites, blogs, social network sites (Facebook, Twitter, Reddit etc.), records of public chats, etc. Thanks to the use of advanced language processing algorithms in the NLU module, such as proprietary NLP modules, the Proper Names Detector, and a proprietary parser of parts of speech for colloquialisms, and others, it became possible to separate the business layer from the language comprehension layer.
Hey Product Hunters 👋It’s Bart from SentiOne here. I’m one of the co-founders and I am super excited to share with you our recent developments in customer service automation!
Many years ago SentiOne was focused on online monitoring and social media listening - think market research by reading customers’ feedback and opinions online. Over the time, our clients (mainly marketing agencies and FMCG brands) told us: “Hey, social listening is great, but we want to to reply to our clients and join the conversation” and that is how we entered customer service field. We moved from Listen to React, and created a platform where you can integrate all customer service channels and reply to customer messages from one tool. No need to switch between profile or accounts.
Now, customer service teams work on SentiOne React to manage customer communication on social media channels, email, WhatsApp and even blogs, forums and various portals. They save up to 50% of servicing time, thanks to this integration.
What else can SentiOne React solve for you?
We are focused to save as much time as possible, so you can handle more requests with the same resources. For example: templated responses so customer service agents do not have to copy paste messages from their documents. Or, we implemented AI to create Suggested Answers to the most frequently asked questions. We have also saved time with smart routing - algorithm that automatically assigns tickets to agents or teams based on their availability or preferences.
Anyway, I will be sticking around all day to answer your questions and would love any suggestions that you have (criticisms openly welcomed and encouraged!). Enjoy!
Bart
COO & Co-founder
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