DigitalGenius brings practical applications of deep learning and artificial intelligence into customer service operations of leading companies.

Its Human+AI Customer Service Platform combines the best of human and machine intelligence enabling companies like KLM Royal Dutch Airlines, Unilever, Magoosh, Joybird, Coinmama and TravelBird, to deliver on increasing customer expectations. 

At its core are deep-learning algorithms, which are trained on historical customer service transcripts and integrated directly into the contact center’s existing software (like Salesforce Service Cloud, Zendesk, and others). When new messages come in via channels like email, live chat, social media, and mobile messaging, the DigitalGenius neural network takes the following 3 actions:

– Predicts and auto-fills all case meta-data related to the incoming message (tags, routing, etc)

– Predicts the best response to the incoming message and shows it to the contact centre agent for approval or personalisation. Upon the agent’s action, the approved message is sent to the customer, and the algorithm goes through continuous learning.

– Finally, any suggested answers above a certain confidence threshold can be automated altogether.

Latest Tweets from @digitalgeniusai

  • We're teaming up with Gen25 to deliver AI through social media channels at scale. Salesforce customers will now be able to run a full spectrum social media customer service capability in Facebook, WhatsApp and Twitter, backed by our automation capabilities ,
  • Liability is a tricky concept for launching autonomous machines. Head of AI, Conan, thinks lack of clarity in liability has put the breaks on autonomous driving, despite huge gains in this technology itself. The full convo is here:
  • Right now we're using systems to reach a goal, but we really need to embed values into autonomous AI agents, according to Conan, Head of AI. We talk about this and more in our last webinar AI & ethics:
  • The Algorithmic Accountability Act is being discussed on the floor of the US Senate, which appears to be the first such concerted move in the US to try to create a framework to prevent bias ,
  • While building algorithms and systems are complicated, should the builders be the only ones compensated? What about individuals whose work creates data to train the algorithm in the first place? Conan talks about this and more in our webinar AI & ethics:

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