Single-turn bots couldn't manage complex daily routines. Now, specialized AI agents break down and orchestrate multistep user schedules.
One of South Korea's leading telecommunications providers operates a digital assistant application managing over 1.1 million monthly active users under strict national data localization regulations.
The company needed to evolve its application from simple, single-turn responses to orchestrating complex, multistep user goals like scheduling and...
“Our goal was to develop highly individualized agent services, creating an assistant that remembers the customer’s usage history and uses it to provide the proper services to the customers.”
SK Telecom's Virtual assistant is part of this use case:
Mobile network operator providing wireless, broadband, and AI-driven services.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Related implementations across industries and use cases
Staff spent 3+ hours daily searching files and formatting visual slides. Now, they use conversational AI to auto-generate tailored decks.
Labor shortages and high turnover bottlenecked call centers. Now, staff use an AI assistant to accelerate responses and cut wait times.
Agents struggled to retrieve info during 3.5m monthly calls. Now, AI transcribes and prompts them with answers instantly.
Strict rules blocked conversational mobile banking. Now, generative AI instantly calculates complex FX rates and repayment fees.
Early outputs felt like awkward translations. Now, slang-fluent personas drive a lifestyle AI platform for 6.5M users.
Reps manually calculated variances across three systems. AI now compares statements in seconds, spotting fee changes instantly.
Manual fine-tuning held back deployment. Automated optimization cut inference time 63% and agent routine work by over 50%.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.
Single-turn bots couldn't manage complex daily routines. Now, specialized AI agents break down and orchestrate multistep user schedules.
One of South Korea's leading telecommunications providers operates a digital assistant application managing over 1.1 million monthly active users under strict national data localization regulations.
The company needed to evolve its application from simple, single-turn responses to orchestrating complex, multistep user goals like scheduling and...
“Our goal was to develop highly individualized agent services, creating an assistant that remembers the customer’s usage history and uses it to provide the proper services to the customers.”
SK Telecom's Virtual assistant is part of this use case:
Mobile network operator providing wireless, broadband, and AI-driven services.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Related implementations across industries and use cases
Staff spent 3+ hours daily searching files and formatting visual slides. Now, they use conversational AI to auto-generate tailored decks.
Labor shortages and high turnover bottlenecked call centers. Now, staff use an AI assistant to accelerate responses and cut wait times.
Agents struggled to retrieve info during 3.5m monthly calls. Now, AI transcribes and prompts them with answers instantly.
Strict rules blocked conversational mobile banking. Now, generative AI instantly calculates complex FX rates and repayment fees.
Early outputs felt like awkward translations. Now, slang-fluent personas drive a lifestyle AI platform for 6.5M users.
Reps manually calculated variances across three systems. AI now compares statements in seconds, spotting fee changes instantly.
Manual fine-tuning held back deployment. Automated optimization cut inference time 63% and agent routine work by over 50%.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.