What is your primary use case for Conversations by NLX?

My main use cases for Conversations by NLX involve customer support and patient services, including rescheduling, booking appointments, and cancelling. For customer support and patient services with Conversations by NLX, I manage appointment details by rescheduling and cancelling, ensuring we have enough intent, fetching the data using entities, and implementing FAQ automation by adding patient-related questions. Calendar integration enables users to easily reschedule, cancel their appointments, and book new ones. Regarding my use cases, we are implementing NLU (natural language understanding) to fetch user intention, and we provide human-like behavior with voice chat, making the interaction more similar to speaking with a human.

Conversations by NLX is used for customers to get account details, card details, to block a card, or for loans, to get loan details, and to get a checkbook request or locate a bank branch. We create a flow to do the task with Conversations by NLX by dragging and dropping and making a flow to accomplish this, and integrating APIs and REST APIs from the backend core banking APIs to authenticate the user or the customer. We can then proceed with the main task. For example, if a customer wants to block a debit card or wants any details, we can create a flow by creating the intents and entities. This defines what the user wants and what we are looking for. We can ask the required questions and create entities such as customer ID or registered mobile number as per the project requirements and specifics.

One of the reasons I explored Conversations by NLX was to investigate the platform's capabilities, as I was in a role to evaluate different platforms that provide conversational AI solutions. Conversations by NLX came into that search, and I have thoroughly explored this platform. We were looking for a platform where we have the flexibility to choose between different models. We have different subscriptions at the company level, and we wanted to see which one would work better for us. Additionally, the flexibility to choose between different voices was important to us. We have our custom voices and corporate voices as well. These were the main things I was exploring in the first phase. I have recently learned that Conversations by NLX is part of Amazon Connect. This was one of the reasons we considered it, because at Deutsche Telekom we were also exploring the migration from Cisco to Amazon Connect. That's the reason we got Conversations by NLX into our consideration. We currently have a hybrid cloud setup and we have a requirement for private cloud as well, but we would like to see how Conversations by NLX could be integrated there.

My main use case for Conversations by NLX is that we are working in the aviation domain, developing a customer-related chatbot product for our employer and for our internal employees to provide information related to canceling tickets, booking tickets, or receiving notifications related to them.A specific example of how I am using Conversations by NLX is that we developed one chatbot where employees are engaged. We use the chatbot to provide information related to booking, flight takeoff, or landing information, and employees can update their own seats. These are the types of information we have provided using this project, and these are the problem statements we are including in Conversations by NLX, with multiple business logics and logical problem-solving that we have used to solve these problems.

We're using Conversations by NLX or the ACXD platform with AWS to bring to life AI agents and deterministic bot flows into the contact center for our customers. We created a demo with Conversations by NLX to provide customers an overview of how the Live Sync feature with ACXD can be brought into the customer's requirements and their current customer flows to show them how the tool works and exactly how we can build it out for them. Our other use case for Conversations by NLX is to provide customers a faster delivery time for their migrations into the Amazon Connect platform. The ACXD tool, which is Conversations by NLX, helps customers deliver quickly and design and deploy quickly customer interactions using voice bots, chatbots, and other solutions.

We used Conversations by NLX during one of our POC initiations to automate our tier-one customer support, handling everything from basic troubleshooting to order status inquiries. It integrated smoothly with our backend systems, so the automated flows have access to real-time data. A great example of how Conversations by NLX automation helped our Taiwan customer support was during a major regional product promotion in Taiwan last quarter. Our local support queue usually gets completely slammed with routine inquiries about shipping timelines and promotional code glitches. Before Conversations by NLX, our small, Taipei-based team would get buried under a mountain of identical tickets. We deployed Conversations by NLX with a localized automated flow in Traditional Chinese. It handled 65% of those incoming tier-one inquiries right out of the gate, verified the customer IDs, and had the real-time tracking data from our logistics backend. We noticed a drop in average wait times for customers, as the automated flows handle a large portion of routine questions immediately. It also relieved a lot of pressure on our support team, allowing them to dedicate more bandwidth to complex, nuanced issues. Overall, it has really helped us scale our operations efficiently.

I used Conversations by NLX just once as my main use case. I wanted to learn how to use Conversations by NLX for conversation AI, such as practicing how to talk to someone. I practiced simulated conversations with Conversations by NLX, including talking to my boss and an angry peer, and I discussed these scenarios.