Want to know the different technologies used in conversational AI of Clinc

Clinc is a leading and innovative company, where the left and right brain coverage to build the unparalleled services and tools in the financial service industry. The team of experts in this Clinc firm are using the patented technology in order to give the best focus on research for their customers. This team has established totally 8 research publications to date and 20 patents in the approval process as pioneers in the conversational artificial intelligence (AI) field.

Conversational AI technologies:

The following are the most commonly used technologies in the development of the conversational AI to offer a variety of financial services.

  • Messy language

There is no problem with messy language because AI from this company learns different speech patterns to recognize the slang of the language and understand the contextual clues. It means your clients can speak to AI as they would normally speak to any other persona, natural, messy, and conversational.

  • Conversational healing

The technology of Clinc enables your clients to change the details they have already given or are looking at a particular point in the conversation.

Clinc conversational AI

  • Context retention

Whether your clients are picking up where they left off the conversational or starting a new one, the technology given by this company retains the history by also ensuring them feel understood and remembered.

  • Slot mapping

With slot mapping, the organizations can ensure your clients have a wonderful experience by ensuring they are completely understood the first time.

  • Data curation

Data is the significant factor to conversational AI but not all types of data will be perfect. The data curation tools given at Clinc platform make it very easy to clean up any unpredictable details to make a particular solution is production ready.

  • Crowd sourcing

This technology helps to source data with their patented crowd sourcing built-in tool in order to rapidly make the new agent capabilities and reduce the model readiness.

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