KAI is a conversational AI platform powering virtual assistants and smart bots across mobile, messaging, and wearables. Built with industry-specific domain expertise, KAI-powered Bots and virtual assistants are well-versed in any form of business whether it’s finance, commerce, or any other industry.KAI do not require coding.
KAI enables a network of intents wired at runtime to enable human-like, cross-intent conversational experiences. KAI includes a deep-learning analytical toolset for data collection and analysis, model training, testing, and deployment. And its comprehensive, self-serve customer portal provides real-time reporting.
Built from the ground up, KAI’s agile development environment includes an easy-to-integrate API and modern SDKs, so users can deploy in weeks rather than months. Choose between on-premise or cloud-based deployments.KAI have the deepest AI portfolio built on decades of research at SRI International.
It’s a complete stack and ready to use out of the box, including a speech recognition engine, natural language processing, AI reasoning, and natural language generation.
The KAI platform can be rapidly calibrated, trained, and customized for each industry and business to uniquely fulfill requests, solve problems, and predict needs for the company’s customers. Whether the enterprise is retail, insurance, healthcare, travel, or transportation, KAI can be made fluent for its domain.
KAI enables the creation of a single “AI brain” that is extensible across channels for a seamless customer experience, including messaging, mobile, and wearables. And, customers can choose the communications mode they prefer: voice, text, or touch.
KAI-powered virtual assistants and smart bots can be easily customized: look, feel, tone and persona. KAI expresses the company’s brand values, delighting its customers with both function and form and rewarding their loyalty.
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Predictive Analytics Process Flow
For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.