There’s been a massive shift in how we do business, thanks to AI customer service. The addition of easily accessible AI technology has opened up many new opportunities for call centers to improve their operations and elevate to an entirely new level. And it’s still growing, with 45% of support teams already using AI.
The addition of large language models (LLMs) like ChatGPT opened the door to tons of new opportunities and use cases. But they’ve also opened Pandora’s Box—what we took for granted only a couple years ago has brought plenty of security, privacy, and even copyright issues.
In this blog post, we’ll explore what LLMs are, the difference between public and private AI options, and show you why private LLMs are the future for customer service—and your business.
Before we jump into the technicals, what exactly is an LLM? Panos Karagiannis, co-founder and CEO of Moveo.ai, spoke about LLMs in our AI in Customer Experience webinar:
“LLMs are essentially a type of neural network that is very good at understanding and generating language.”
LLMs are sophisticated (and complex) AI systems designed to understand and generate human language with incredible accuracy, almost as if you were speaking to a real person. They’re built atop intricate neural networks and process massive amounts of text data to deliver those humanlike responses.
Within that text data, these LLMs learn to understand and reproduce text that reads as if a human has written it. They learn from the patterns and relationships between words and phrases—kinda like your smartphone’s built-in word prediction, but far more powerful.
At the heart of these LLMs is the transformer architecture, which handles long-range text dependencies. It involves multiple layers of these neural networks that interpret and generate language. These networks comb over large-scale text data and combine it with machine learning to teach the model how to predict and generate text based on the patterns it finds.
We’ve already seen plenty of these AI tools in action—Google’s Gemini (formerly known as Bard), Anthropic’s Claude, and the most famous, OpenAI’s ChatGPT. They’ve made huge impacts in various industries thanks to their lightning-fast ability to generate coherent, readable text that can be used in many different ways, even within call centers.
We already know the big names for public AI models, and you’ve most likely seen or used them yourself by this point. However, despite the popularity of these tools, there are a few glaring issues under the surface that the average user probably doesn’t even consider. This has led to the release of private LLMs, which offer nearly identical functionality at the cost of a little more hard work.
A private LLM is a model that you train and control entirely in-house. They’re specifically tuned to your business requirements, meaning you have full control over the data it’s used to train on, giving it domain-specific knowledge about your business operations and information to perform tasks. They offer a couple of key features, such as:
On the other hand, public LLMs such as ChatGPT are pre-trained by the companies that develop them, which are then made public via APIs and web interfaces. These models typically have a more general purpose and often use the data and prompts fed to them to continue learning, making sharing private information a risk. However, they do offer great features like:
But what are the key differences between public and private LLMs? Let’s take a look:
When you’re looking to improve your existing customer service, integrating a private LLM into your workflows can offer significant advantages over public models. Want to know why? Let’s break it down:
Nothing is more important than keeping your customer data safe, as even one data leak can end up doing massive damage to your brand and your business. According to IBM, the global average cost of a data breach in 2023 was $4.45 million, an increase of 15% over the last three years.
Unlike public models, which process data over the internet and may expose it to external threats (or regurgitate it to those outside your organization), private LLMs operate on your infrastructure or in a secure cloud environment.
This more secure approach minimizes the risk of data breaches and ensures you stay compliant with privacy regulations like GDPR and CCPA. Additionally, having more control over your data can give you and your customers better peace of mind knowing that their information is safe.
It’s no secret that AI tools can hallucinate (i.e. giving plausible sounding but incorrect, or completely nonsensical, responses). And while we can laugh when Google suggests that you eat at least one rock a day, this is simply too big a risk for companies that want to rely on AI in customer service.
Private LLMs can be trained and fine-tuned on your specific datasets, making them exceptionally accurate and relevant to your business needs instead of creating nonsense answers when it doesn’t know. Moveo.ai has even run experiments that show private LLMs outperforming GPT-4 models when dealing with hallucinations.
Every business is unique and has its own requirements and needs. Private LLMs give you the option to customize and control how they learn and respond, unlike public ones. You can tailor your models to reflect your brand’s voice, incorporate specific knowledge bases, and adapt them to the growing needs of your call center as time goes on.
This level of customization is simply not possible with public LLMs because they’re trained for general use and are unable to retain knowledge in most cases. By having a model that understands your specific needs, products, services, and customer service protocols, you can deliver a more personalized and effective customer experience.
With 81% of customers expecting faster service as technology advances, agents need to be capable of responding quickly. Using private LLMs, you can optimize for performance within your infrastructure and ensure faster response times compared to public LLMs.
This reduced latency can be a sizable difference too. Moveo’s LLM, for example, operates up to 4.5 times faster than GPT on average. That makes a massive impact on your customers, as quick and accurate responses are key to delivering seamless, efficient customer experiences.
While the initial investment in private LLMs is going to be higher than simply using public models, they end up saving costs in the long run. With private models, you avoid ongoing subscription fees that are associated with public ones.
Plus, the improved accuracy and efficiency of these private LLMs cut down on operational costs and help streamline workflows, leading to better ROI through improved efficiency and customer experience.
As you can tell, using a private LLM can make a pretty big impact on your business overall. But what are some specific areas where you might benefit from AI customer service? Here are four key use cases that highlight how you can transform your customer service operations:
One of the most time-consuming aspects of customer service is dealing with repetitive queries. Instead of taking up agents’ time to handle routine questions, private LLMs can be trained to respond to these questions and offer solutions based on the context of your business.
By training your LLM on the specific facets of your business and common customer concerns it can provide instant, accurate responses, which improves efficiency and customer satisfaction—and frees up your agents to work on more challenging issues.
This automation both speeds up your response time and ensures that AI answers remain consistent with the information it’s trained on.
Personalization is key to customer satisfaction, with over 81% of customers saying they wanted a personalized experience. Using a private LLM lets you analyze customer data and interaction history to deliver tailored responses for individual customers.
Unlike generic, canned responses from public models, private LLMs trained on your customer data and knowledge base can recognize patterns within customer queries and handle more complex issues that are relevant to your business, such as troubleshooting tech issues. General-purpose models lack the training or context to solve these issues, which can lead to incorrect information being given to your customers.
Private LLMs can be a key part in helping drive revenue through upselling and cross-selling opportunities beyond just standard customer support. With this personalized data and a deep understanding of your business’ products, these models can suggest relevant products or services during interactions, whether it’s self-serve or assisting agents.
For example, if a customer is inquiring about a product, your AI customer service can recommend complementary upgrades and items while answering common questions. Or, in a case where a customer might be lost to churn, your private LLM can recommend sales and discounts to help retain their business.
Maintaining high quality is key for any call center, but it can often be a tedious and manual process. Integrating a private LLM can automate the monitoring and evaluation of customer interactions, providing better insights into agent performance while freeing up human evaluators to dive deeper into the data collected.
These private models can significantly improve the workflows of your QA team, including transcribing and analyzing calls, flagging issues like non-compliance, and identifying areas where agents may be struggling. Adding this level of automation ensures consistent quality throughout your entire call center while giving you deeper insights into your customer experience.
Adding a private LLM to your contact center can revolutionize your entire process by automating repetitive tasks, creating a personalized experience, and much more. However, implementing one of these AI customer service tools isn’t as simple as just plugging in and taking off; they come with their own set of obstacles that need to be addressed before you can take off the training wheels. Here’s how:
It’s already apparent how impactful AI has been on the world, and it’s already making massive differences in customer service. And with the option of creating and using private LLMs to manage and automate processes, companies can get even more value from this technology without the risks of public AI models.
Data security, accuracy, customization, performance, and cost-efficiency are only some of the benefits that come with AI customer service, and get even stronger with a private LLM versus traditional generative AI models. If you plan carefully, take it slowly, and expand with care, you can get the maximum value from AI-powered customer service.
And an essential way to ensure that your AI implementation is accurate and consistent is through a solid quality assurance foundation. With Scorebuddy’s GenAI Auto Scoring, you can get 100% coverage for all of the interactions in your call center, ensuring that your customers remain safe and that your AI stays aligned with your business.
Try a free demo of Scorebuddy’s GenAI Auto Scoring and see how it can revolutionize your call center today.
