Find and compare the best Artificial Intelligence software for BotCore in 2024
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Use the comparison tool below to compare the top Artificial Intelligence software for BotCore on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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Dialogflow
Google
216 RatingsDialogflow by Google Cloud is a natural-language understanding platform that allows you to create and integrate a conversational interface into your mobile, web, or device. It also makes it easy for you to integrate a bot, interactive voice response system, or other type of user interface into your app, web, or mobile application. Dialogflow allows you to create new ways for customers to interact with your product. Dialogflow can analyze input from customers in multiple formats, including text and audio (such as voice or phone calls). Dialogflow can also respond to customers via text or synthetic speech. Dialogflow CX, ES offer virtual agent services for chatbots or contact centers. Agent Assist can be used to assist human agents in contact centers that have them. Agent Assist offers real-time suggestions to human agents, even while they are talking with customers. -
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IBM Watson
IBM
1 RatingLearn how to implement AI in your business. Watson can help you predict and shape future outcomes, automate complicated processes, and optimize employees' time. To predict and shape future outcomes and automate complex processes, integrate Watson into your workflows and optimize your employees' work hours. To tap into organizational data, integrate Watson into your apps. This will allow you to put AI to use across multiple departments, including finance, customer care, and supply chain. You can make better, more personalized customer experiences, scale the expertise and make smarter business decisions using deep insights from data. -
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Amazon Polly
Amazon
Amazon Polly turns text into speech. This allows you to create apps that talk and create new types of speech-enabled products. The Text-to-Speech service (TTS) by Polly uses advanced deep learning technology to synthesize natural sounding human voice. You can create speech-enabled apps that work in many countries using dozens of realistic voices from a wide range of languages. Amazon Polly also offers Standard TTS voices. However, Neural Text-to Speech (NTTS), voices are available that offer advanced speech quality improvements through a machine learning approach. The Neural TTS technology of Polly also supports two styles of speaking that will allow you to better match your application's delivery style to the speaker: a Newscaster reading style, which is best suited for news narration use cases; and a Conversational speaking style, which is ideal to facilitate two-way communication such as telephony applications. -
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Amazon Rekognition
Amazon
Amazon Rekognition allows you to easily add image and video analysis into your applications using proven, highly-scalable, deep learning technology that does not require any machine learning expertise. Amazon Rekognition allows you to identify objects, people and text in images and videos. It also detects inappropriate content. Amazon Rekognition can also be used to perform facial analysis and facial searches. This is useful for many purposes, including user verification, people counting, public safety, and other uses. Amazon Rekognition Custom Labels allow you to identify objects and scenes in images that meet your business requirements. You can create a model to help you classify machine parts or detect plants that are sick. Amazon Rekognition Custom Labels does all the heavy lifting for you. -
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Azure Machine Learning
Microsoft
Accelerate the entire machine learning lifecycle. Developers and data scientists can have more productive experiences building, training, and deploying machine-learning models faster by empowering them. Accelerate time-to-market and foster collaboration with industry-leading MLOps -DevOps machine learning. Innovate on a trusted platform that is secure and trustworthy, which is designed for responsible ML. Productivity for all levels, code-first and drag and drop designer, and automated machine-learning. Robust MLOps capabilities integrate with existing DevOps processes to help manage the entire ML lifecycle. Responsible ML capabilities – understand models with interpretability, fairness, and protect data with differential privacy, confidential computing, as well as control the ML cycle with datasheets and audit trials. Open-source languages and frameworks supported by the best in class, including MLflow and Kubeflow, ONNX and PyTorch. TensorFlow and Python are also supported. -
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Amazon Lex
Amazon
Amazon Lex allows you to create conversational interfaces in any application by using voice and text. Amazon Lex offers advanced deep learning functions such as automatic speech recognition (ASR), which converts speech to text, or natural language understanding (NLU), which recognizes the intent of the text. This allows you to create applications that are engaging and have lifelike conversations. Amazon Lex gives developers the same deep learning technology that powers Amazon Alexa. This allows them to quickly and easily create sophisticated, natural-language, conversational bots ("chatbots") with ease. Amazon Lex allows you to create bots that increase productivity in the contact center, automate simple tasks and improve operational efficiency across the enterprise. Amazon Lex is a fully managed service that scales automatically so you don’t have to worry about infrastructure management. -
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LUIS
Microsoft
Language Understanding (LUIS), a machine learning-based service that builds natural language into apps and bots. Rapidly create custom models that are enterprise-ready and can be continuously improved. Natural language can be added to your apps. LUIS is a language model that interprets conversations to find valuable information. It extracts information from sentences (entities) and interprets user intentions (goals). LUIS is seamlessly integrated with the Azure Bot Service, making creating sophisticated bots easy. You can quickly create and deploy a solution faster by combining powerful developer tools with pre-built apps and entity dictionary, such as Music, Calendar, and Devices. The collective knowledge of the internet is used to create dictionaries. This allows your model to identify valuable information from user conversations. Active learning is used for continuous improvement of the quality of the models.
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