#MLOPSLIVE WEBINAR SERIES

Session #31

Building Scalable Customer-Facing Gen AI Applications Effectively & Responsibly

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If you’re building a gen AI application, such as a conversational agent that will be interacting directly with your customers, you’re probably facing some of these challenges:

  • Minimizing AI hallucinations
  • Chatbot response times
  • Ensuring personally identifiable information (PII) remains secure
  • Ensuring the virtual agent is up to date with your company’s policies and procedures
  • Creating a virtual agent that can converse in the correct tone of voice with each customer, based on the data you have on file (hyper-personalization)
  • Optimizing customer satisfaction
  • Reducing time spent on each interaction
  • Working at scale in a cost-effective way

In this session, we discuss the unique challenges of implementing gen AI in production environments, when agents are in direct contact with your customers.

We shared the Iguazio & MongoDB one-stop-shop solution for building gen AI applications that scale effectively and efficiently, with built-in guardrails and monitoring. We showed how the end-to-end application lifecycle is addressed – From data management all the way to governance and monitoring in production.

We also presented a live demo of a customer-facing agent conversing with different customers at a large bank, and adapting its responses and tone of voice to each customer.