Best Natural Language Processing Software for Neo Enterprise Assistant Platform

Find and compare the best Natural Language Processing software for Neo Enterprise Assistant Platform in 2025

Use the comparison tool below to compare the top Natural Language Processing software for Neo Enterprise Assistant Platform on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Dialogflow Reviews
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    Dialogflow 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.
  • 2
    LUIS Reviews
    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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