.png)
.png)
Ready to redefine your business? Let's talk AI!
Talk to our Gen AI Expert !
Unlock your business potential with our AI-driven solutions. Book your free strategy call today.
Book your free 1-1 strategic call
.jpg)
Generative AI is the wave of the hour and every company wants to be at the forefront of it, Gaming leaders like Krafton are not behind. This parent company for games like PUBG has been making waves with its innovative approach to customer support.
Why AI Customer Support? What’s the need?
To provide exceptional, cost-efficient customer support across multiple brands and languages, the company implemented an integrated support system that automates manual tasks. This strategic move has led to significant improvements in operational efficiency, data visibility, and customer satisfaction, resulting in a 15% reduction in support costs.
The company’s customer support team, known as Player’s Support, is dedicated to user retention. Their goal is to alleviate user discomfort, enhance player satisfaction, and devise customer care strategies through user analysis. The team handles approximately one million inquiries per year, supporting 13 languages for players in various locations.
In 2021, the company recognized the need for an integrated management system that could efficiently serve multiple locations and brands in the form of a bot. They faced challenges in supporting multiple languages, and the number of tickets handled by agents was inconsistent, making it difficult to plan and optimize agent time and costs.
To prevent the Player’s Support team from being overwhelmed, the company decided to use a complete customer service solution. They leveraged several features for ticket management, including prepared responses or actions (macros) that enabled agents to resolve tickets quickly. Automation helped agents run processes within specific time frames.
The Impact
This approach has yielded impressive results. The company has seen an 18% reduction in ticket processing time, a 15% reduction in agent costs, and a 28% reduction in inquiries with the help of an automated response system.
| Decision points | Open-Source LLM | Close-Source LLM |
|---|---|---|
| Accessibility | The code behind the LLM is freely available for anyone to inspect, modify, and use. This fosters collaboration and innovation. | The underlying code is proprietary and not accessible to the public. Users rely on the terms and conditions set by the developer. |
| Customization | LLMs can be customized and adapted for specific tasks or applications. Developers can fine-tune the models and experiment with new techniques. | Customization options are typically limited. Users might have some options to adjust parameters, but are restricted to the functionalities provided by the developer. |
| Community & Development | Benefit from a thriving community of developers and researchers who contribute to improvements, bug fixes, and feature enhancements. | Development is controlled by the owning company, with limited external contributions. |
| Support | Support may come from the community, but users may need to rely on in-house expertise for troubleshooting and maintenance. | Typically comes with dedicated support from the developer, offering professional assistance and guidance. |
| Cost | Generally free to use, with minimal costs for running the model on your own infrastructure, & may require investment in technical expertise for customization and maintenance. | May involve licensing fees, pay-per-use models or require cloud-based access with associated costs. |
| Transparency & Bias | Greater transparency as the training data and methods are open to scrutiny, potentially reducing bias. | Limited transparency makes it harder to identify and address potential biases within the model. |
| IP | Code and potentially training data are publicly accessible, can be used as a foundation for building new models. | Code and training data are considered trade secrets, no external contributions |
| Security | Training data might be accessible, raising privacy concerns if it contains sensitive information & Security relies on the community | The codebase is not publicly accessible, control over the training data and stricter privacy measures & Security depends on the vendor's commitment |
| Scalability | Users might need to invest in their own infrastructure to train and run very large models & require leveraging community experts resources | Companies often have access to significant resources for training and scaling their models and can be offered as cloud-based services |
| Deployment & Integration Complexity | Offers greater flexibility for customization and integration into specific workflows but often requires more technical knowledge | Typically designed for ease of deployment and integration with minimal technical setup. Customization options might be limited to functionalities offered by the vendor. |
At Fluid AI, we stand at the forefront of this AI revolution, helping organizations kickstart their AI journey in enhanced Customer Support with AI tech. If you’re seeking a solution for your organization, look no further. We’re committed to making your organization future-ready, just like we’ve done for many others.
Take the first step towards this exciting journey by booking a free demo call with us today. Let’s explore the possibilities together and unlock the full potential of AI for your organization. Remember, the future belongs to those who prepare for it today.
Fluid AI International Corporation (USA)