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Sinch Reveals Its New AskFrank Question-Answering Search Engine

Sinch Reveals Its New AskFrank Question-Answering Search Engine

Sinch, a cloud communications and mobile customer engagement platform, launched AskFrank, an AI-enabled question-answering search engine. AskFrank integrates with a business’s existing customer engagement chatbots, contact center, website or knowledge base, landing the name from its frank, direct and honest answers.

Using AI to search content, it provides answers to questions that are not available in a chatbot’s database by searching assets based on the subject and the meaning. By periodically analyzing content from Confluence, Sharepoint, FAQ pages and other knowledgebases, AskFrank can look for the most appropriate answers through its machine learning algorithms.

AskFrank aims to help businesses:

  • Build their own AI chatbots by connecting AskFrank to existing internal and external knowledge bases;
  • Understand natural language to answer users' questions properly;
  • Provide information to chatbots and contact center agents to respond to customers on their preferred channel;
  • Respond in more than 100 languages;
  • Unify content from multiple sources;
  • Enable their chatbots to self-learn from conversation history; and
  • Reduce the number of common questions to the contact center, allowing agents to focus on solving the more challenging issues.

“In an economy where more and more organizations are relying on chatbots to engage with their customers to lower operating costs, we are seeing customers often frustrated in cases when a chatbot can’t provide them the right answer,” said Sunny Dhami, VP of Product Marketing at Sinch, in a statement. “Therefore, Sinch built AskFrank to improve the level of accuracy and customer experience in customer communication.  Available to Sinch’s customers using our conversational AI platform, Chatlayer, and cloud contact center solution, Contact Pro, AskFrank improves any chatbot’s capability to understand natural language much better, so supporting agents in offering response suggestions so that they become more efficient and less customers are forwarded to second and third-line support.”

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