Artificial Inteligence Machine Learning Technical Support 
AWS Machine Learning - AI
APRIL 9, 2025
Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements.
TechCrunch
AUGUST 30, 2022
Founded out of Stockholm in 2017, Mavenoid works with hardware and consumer electronics companies including HP, Husqvarna, and Jabra, serving to automate technical support and onboarding for customers spanning everything from printers and ovens, to electric scooters and industrial equipment. ” Technical support.
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OTS Solutions
DECEMBER 13, 2018
5 Ways Artificial Intelligence Can Improve Your Business Right Now. Have you thought about artificial intelligence? Similarly, machine learning can help you reduce waste during production. Technical Support. Likewise, if your business provides technical support to customers, AI can help.
TechCrunch
MARCH 30, 2023
.” What makes Oscilar different, Narkhede says, is the platform’s heavy reliance on AI and machine learning. But not just any AI — Narkhede claims that Oscilar’s AI, developed in-house, requires much less first- and third-party data about past fraud incidents from customers to train machine learning models.
CIO
SEPTEMBER 29, 2023
Most relevant roles for making use of NLP include data scientist , machine learning engineer, software engineer, data analyst , and software developer. TensorFlow Developed by Google as an open-source machine learning framework, TensorFlow is most used to build and train machine learning models and neural networks.
TechCrunch
APRIL 12, 2022
While there may still be some debate over whether customers, or indeed agents or businesses, want a lot of video engagement in calls, there are times when you might imagine that could be useful, such as in cases of technical support. Observe.ai
John Snow Labs
APRIL 30, 2025
We developed advanced assertion detection models, including fine-tuned LLMs, transformers, few-shot classifiers, deep learning (DL) and rule -based approaches. To enhance fine-tuning efficiency, our fine-tuned LLM excludes the Conditional label , as its overlap with Hypothetical could introduce ambiguity.
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