Bitext partners with Amazon Connect (for English for now)

[fa icon="calendar'] Jan 9, 2020 6:23:35 PM / by Bitext posted in Machine Learning, NLP, Big Data, Bitext, Natural Language, Text Analytics, Artificial Intelligence, Deep Learning, Chatbots, NLG, NLU, AI, Multilanguage, NLP for Chatbots, Finance, Banking, "Multilingual synthetic data", aws

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Today, we're proud to announce that we have partnered with Amazon to bring the power of our Multilingual NLU engines (powered with our Synthetic Training Data) to Amazon Connect. As a first step, we’ve set up two new services -Instant LexBot and LexBot Booster- available on the AWS Marketplace, with our Custom LexBot projects service also available on demand.

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Amazon re:Invent: the Age of Data

[fa icon="calendar'] Dec 12, 2019 7:00:00 PM / by Bitext posted in Machine Learning, NLP, Sentiment Analysis, Big Data, Bitext, Deep Linguistic Analysis, Natural Language, Text Analytics, Artificial Intelligence, Deep Learning, NLG, POS tagging, AI, Multilanguage, NLP for Core, NLP for Chatbots, "Multilingual synthetic data"

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A few days ago, Amazon Web Services organized AWS re:Invent, one of the world biggest IT events, focusing on everything Amazon has to offer. Among the great amount of novelties that were announced, some of them were very interesting for us. 

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Training AI Faster with a Human in the Loop

[fa icon="calendar'] Oct 16, 2019 5:30:00 PM / by Bitext posted in Machine Learning, Bitext, Natural Language, Artificial Intelligence, Chatbots, NLG, NLU, AI, NLP for Chatbots, "Multilingual synthetic data"

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One of the most significant aspects of a virtual agent is how fast it can learn. With a human in the conversational loop, training AI goes much faster: your bot learns and changes, keeping knowledge up to date. Plus, users never get the “Sorry, I did not understand your request” response, your brand will be able to solve the problem right away.

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AI and chatbots: How to design a great conversation?

[fa icon="calendar'] Sep 16, 2019 4:56:38 PM / by Bitext posted in API, Machine Learning, NLP, Big Data, Bitext, Natural Language, Artificial Intelligence, Deep Learning, Chatbots, Phrase Extraction, NLG, TechCrunch, NLU, AI, Multilanguage, NLP for Core, NLP for Chatbots

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Reducing complicated, confusing processes down to a natural conversation is potentially a huge business opportunity for anyone willing to jump headfirst and create a great user experience. Chatbots are only as smart as the words you feed them. If a bot is too rudimentary, people will lose trust in the company and will feel ignored and unappreciated. UX problems appear when user deviates from the designed linear flow.

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How chatbots enhance customer experience in contact centers

[fa icon="calendar'] Sep 9, 2019 5:03:00 PM / by Bitext posted in API, Machine Learning, NLP, Big Data, Bitext, Natural Language, Artificial Intelligence, Deep Learning, Chatbots, Phrase Extraction, NLG, TechCrunch, NLU, AI, Multilanguage, NLP for Core, NLP for Chatbots

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Most customer service and contact center executives are honing in on bots because they can handle large volumes of queries. Thus, their service center staff can focus on more complex tasks. As the technology behind bots has improved in terms of natural language processing (NLP), machine learning (ML), and intent-matching capabilities, companies are increasingly willing to trust them to handle direct customer interaction.

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AI for Fraud detection in Trading: A Case Study

[fa icon="calendar'] Aug 7, 2019 4:20:00 PM / by Bitext posted in API, Machine Learning, NLP, Big Data, Bitext, Natural Language, Artificial Intelligence, Deep Learning, Chatbots, NLG, TechCrunch, NLU, AI, Multilanguage, NLP for Core, NLP for Chatbots, Finance, Banking

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Those familiar with stock markets surely know how traders communicate with each other via chatrooms in order to keep up with the trends and get actionable insights from peers. Most of these platforms, especially those offering free access, are a cluster of activity that needs to be moderated to ensure the interactions are civil and legal.  Why do it manually through  professional moderators when you can use an automated AI system?

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