Tom Port

CAI Meet Unsupervised Learning: Tools and Best Practices for Optimising NLU Performance for Improved CX

This session examines the demands on AIOps teams to maintain accuracy and NLU performance against the overwhelming volume and variation of user queries, the difficulty of translating this into reliable training data (without breaking things) and doing so in a timely manner that addresses both the demands of the business (for new scope and functionality) and the desire to improve overall satisfaction of conversational self-service experiences.

Leveraging a real-world example, we will present solutions for: • identification and treatment of confusion in NLU design • measurement of the accuracy of your model over time • minimising “learning delay” • reduction of learning workloads for humans • application of changesets into new NLU models • regression protection

In this live demonstration, prepared for both business and technical teams, we will evaluate the NLU design and benchmark the bot accuracy, produce a change set for identification and treatment of areas of confusion, automatically push the change set into the NLU model and evaluate the regression impact to confirm that the changes can be made safely.

Tom Port is the VP Sales at ServisBOT, the Conversational AI Platform helps businesses automate interactions across customer and employee journeys using AI. Prior to ServisBOT, Tom was a member of the founding team at AutoEntry a fintech start up that was successfully sold to the Sage Group in 2019. Tom's passions lie in deeply understanding the nuances of pain felt by large enterprises and how we can use technology to help solve them.

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