#MLOPSLIVE WEBINAR SERIES
Session #31
Building Scalable Customer-Facing Gen AI Applications Effectively & Responsibly
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.