Privacy and anonymity are at the heart of Hello Ebbot's Dynamic Code project.
As one of the leading truck suppliers in the world, Jungheinrich has a wide range of trucks.
Read more about how we have successfully streamlined service departments in the public sector!
Say hello to Jeanett! NetOnNet's digital employees who manage the purchase journey from start to finish.
Elgot is Öresundskraft's digital employee who revolutionizes the way the energy industry handles customer journey
Understanding that typos can be one of the reasons why Ebbot cannot give the right response, our NLP team at Hello Ebbot decided to develop a new feature to autocorrect spelling mistakes, specifically for Swedish language! Our spellcheck corrector not only considers context to provide better correction, but also has fixed performance.
Support comes in different forms. Customer service is most common. But this also includes internal support,
After one month of preparing the dataset and training, we proudly present to you a T5 (Text-To-Text Transfer Transfromer) based model that paraphrases any questions in the Swedish language. By learning from paraphrased questions by the Swedish T5, we are no longer limited to just the topics in our current questions database.
Are you wondering if it is safe to share your information with Ebbot? Well, you can put your mind at ease now as we reassure you that with Hello Ebbot's special dishwasher, your personal data is completely secure! How does this dishwasher work and how did we make it possible, you may ask? Please keep reading and we will answer all your questions 👀
Recently, one of our clients asked us to teach Ebbot to detect toxic messages in conversations. Thanks to this special request, we got a chance to work on one of the most difficult topics in the Natural Language Processing (NLP) field. And yes, we can't be more excited! 🥳
After successfully extended a powerful Natural Language Processing (NLP) model called "SentenceTransformers" to Swedish language, we decided to continue with another exciting project aiming to half-automate the intent training process by grouping similar sentences.
Implementing a chatbot project can be a long and complex journey if you don't know how to do it. There are many risks associated with automating customer caps, and ultimately the reputation of the organization is at stake, as the chatbot becomes the welcome mat for customers to create that first impression that makes up the majority of what they'll think forever. It's also easy for a chatbots project to drag on, costing the shirt.
We tell you about the customer service employee: your most important resource to influence what customers think of you. You will get 5 tips on how to best increase motivation, performance by creating the right conditions and a sustainable way of working.
Do you know that not every sentence is useful for the learning process? There is information that we do not want Ebbot to memorize, such as phone numbers, emails and spam messages. That is why we decided to build a Machine Learning (ML) model to classify messages as spam or not spam to filter out unnecessary data.
Based on UKPLab's publication, we extended the English SentenceTransformers to a Swedish model using TED2020 parallel sentences dataset and achieved 95.6% accuracy.