Best Chatbot Software

Dialogflow from Google Cloud is a natural language understanding platform that makes it easy to design and integrate a conversational user interface into your mobile app, web application, device, bot, interactive voice response system, and so on. Using Dialogflow, you can provide new and engaging ways for users to interact with your product. Dialogflow can analyze multiple types of input from your customers, including text or audio inputs (like from a phone or voice recording). It can also respond to your customers in a couple of ways, either through text or with synthetic speech. Dialogflow CX and ES provide virtual agent services for chatbots and contact centers. If you have a contact center that employs human agents, you can use Agent Assist to help your human agents. Agent Assist provides real-time suggestions for human agents while they are in conversations with end-user customers.

Aisera stands at the forefront of innovation, introducing a revolutionary solution that redefines the way businesses and customers thrive. Through cutting-edge AI technology, Aisera offers a proactive, personalized, and predictive experience that automates operations and support across various sectors, including HR, IT, sales, and customer service. By providing consumer-like self-service resolutions, Aisera empowers users and drives their success. Unleashing the power of digital transformation, Aisera accelerates the journey towards a streamlined future. By harnessing user and service behavioral intelligence, Aisera enables end-to-end automation of tasks, actions, and critical business processes. Seamlessly integrating with industry-leading platforms such as Salesforce, Zendesk, ServiceNow, Microsoft, Adobe, Oracle, SAP, Marketo, Hubspot, and Okta, Aisera creates exceptional business value.

swivl is simplifying AI training. In general, data scientists typically spend 80% of their time on non-value-added tasks such as finding, cleaning, and annotating data. Our no-code SaaS platform helps teams outsource these data annotation tasks to a vetted network of data annotators to close the feedback loop in a cost-effective way. This involves the action of training, testing, and deploying machine learning models with an emphasis on natural language processing, audio, and generalized data categorization.