Generative AI

Revolutionizing CX

Generative artificial intelligence (AI) is driving new innovation across all industries, leading us into the next technological revolution. Already, AI powers the Internet of Things (IoT), robotics, big data, and other emerging technologies, contributing to a predicted 26% increase in global GDP in the next 7 years, according to Gartner.

The use case for AI in customer experience (CX) is profound. A current run-rate of 1.6% of agent interactions are already performed by conversational AI, a number that is predicted to skyrocket to 10% in the next 4 years. This increase in automation will primarily come from simulated voicebots and text-based chatbots.

What is Generative AI?

Generative AI uses machine learning technology to analyze existing data, then creates new content in a similar style to the original data.

Generative AI produces new data rather than simply predicting an output of information or classifying what already exists.

How We Win

How We Win

At IntouchCX, our Laivly solution uses machine learning to write, communicate, and solve problems like a human while remaining true to your unique brand voice. Laivly operates with empathy, transforming your brand’s CX with real-time decision making and action between support workflows and the customer-facing agent. This reduces total customer interaction time by 55%, leading to increased accuracy and CSAT.

IntouchCX has been pioneering AI-powered technology with Laivly since 2018, and is already product-ready with the next iteration of Generative AI, boosted by ChatGPT.

As a front-runner for advanced CX, our solution provides seamless and secure interactions between attended AI and digital workers to efficiently process all back office and manual tasks, guide agents on open items that may require manual validation, and generate on-brand verbatims, case notes, chats, and emails in real-time.

Why Laivly?

Our agile technology eliminates the need for APIs, complex integration, or replatforming. Our solution accesses workflow processes through the front end, just like an agent, and continuously learns and improves at lightning speed, leading to reduced agent effort and overhead within months of implementation.