When working on AI projects, owning data to nurture your solution is key for good performance. Gathering e-mails and conversation logs to train your bot may be as good as a makeshift solution, but this lack of data can now be cut off at the root. Why not start farming your own data instead of harvesting it?
Our NLP API platform is the most comprehensive and accurate (more than 90% accuracy) in the text analysis market. You can find a wide variety of multilingual NLP tools and solutions that will help you create the best customer experience for your business. Watch our new video now and sign up!
According to Gartner, the Customer Engagement Center (CEC) is one of the fastest-growing application software markets. Have you already jumped on the bandwagon of contact center chatbots, but didn’t get the expected results yet? Most consumers switch to a competitor after one bad customer experience. Can automation actually improve service? Stop wasting time and start making your chatbot work!
What is the best way to generate leads in the finance sector? Digital strategies such as webinars, content marketing or paid social advertising may give great brand visibility, but are they actually initiating consumer inquiries into your products or services? If you really want higher economic outputs, it’s time to hit the target! Learn how Bitext AI solutions helped one of the leading German banks to boost its sales.
Although chatbots have become quite popular in recent years, there is still room for improvement. A well-trained chatbot must correctly react to any query sent by a user creating a successful human-like conversation. Is that happening? We don’t think so.
Round-the-clock service, cost reduction and delivering a better customer experience are, among others, the main benefits of chatbots for customer service automation. If you are thinking of setting up a conversational agent to take care of your customers, it’s all-important for you to know not only the bright side, but also the dark one. You just can't spin up a basic chatbot and expect it to work well.
Two concepts, one mission: to make machines understand humans. Natural Language Processing (NLP) and Machine Learning (ML) are all the rage right now as techniques that complement each other rather than as NLP vs ML. In this post, we will focus on NLP and how it works together with ML to solve the challenges Artificial Intelligence is posing.