If you are considering using LLMs to automate your customer service operations, you probably have been searching for answers to questions like “Is it safe to use ChatGPT? Or “Can I use privately-hosted LLMs”. Your quest ends here!
This blog delves into the differences between self-hosted LLMs and third-party systems like GPT-4 focusing on quality, security and customization.

The LLM revolution

Large Language Models or LLMs are generative artificial intelligence models, trained on vast amounts of data. These models are crafted to not only understand the intricacies of language but also to respond contextually, creating a conversational experience that mimics human interaction.

The year 2022, OpenAI released ChatGPT and chaos unleashed! ChatGPT is the perfect example of what LLMs are and completely altered the AI landscape. Many tech leaders followed OpenAI, releasing their own LLMs such as Google (Bard) and Facebook (Llama 2). All these models opened doors to new possibilities and applications, with chatbots being one of the most prominent ones.

Chatbots powered by LLMs, can be fed with specific business data and deliver instantaneous, human-like responses, 24/7. However, everyone quickly realized that although impressive, ChatGPT and the rest of the LLMs hidden behind APIs are not the solution to everything. Data privacy, hallucination issues and inability to provide real-time personalized answers are among the biggest limitations.

Read more: Limitations of ChatGPT in customer service

The Open Source Approach: GPT-4 and beyond

LLMs such as GPT-4 are trained on billions of parameters, which makes them ideal for general use. However, the associated cost of training such models is enormous, making it almost impossible for smaller companies and AI labs to compete with tech giants. As a result, companies had no choice but to use GPT-4 and the so-called “GPT-wrappers” emerged. These companies usually provide a chatbot solution, by relying on an API integration with OpenAI’s GPT-3.5 or GPT-4. There are many reasons why this approach is not optimal but the main three are listed below:

• Data protection
  Entrusting sensitive data to cloud-based platforms raises valid concerns about data security, with potential implications for the loss of intellectual property. This concern extends to the potential compromise of confidential business information and client databases through unauthorized access.

• Scalability
  GPT-4 technical details, such as the number of parameters and its training data, have not been disclosed, which enforces the mistrust and skepticism of many enterprises around closed-source Language Models. While the exact size of GPT-4 is not known, it is a very large language model, which makes it very difficult to scale to many concurrent requests and reduce its response times.

• Access to dynamic data from APIs
  While GPT-4 excels in answering general questions, it fails to solve business specific problems. GPT-4 struggles to follow explicit instructions and cannot reliably connect to third-party systems. Consequently, businesses find it challenging to offer tailored personalized customer service, such as addressing order-related inquiries in eCommerce scenarios or providing transaction status updates in Fintech applications.

Privately-Hosted Large Language Models

Llama-2 by Meta was released in 2023, followed by an open-source LLM wave which permits companies or AI providers to self-host LLMs in their own infrastructure. Many independent AI labs and a few Conversational AI platforms like Moveo.AI, started training their own proprietary LLMs using other open-source LLMs as a base, such as LLAMA-2 and Mistral. The fundamental difference lies in having access to Language Models hosted privately —instead of relying on external APIs.

Moveo’s proprietary LLMs are trained specifically for customer service in eCommerce, Fintech and Gametech, bringing the power of language models directly into the enterprise infrastructure. This not only enhances processing speed but also grants organizations greater control over data privacy and security. While Moveo provides a cloud offering with all the data privacy and security guarantees, it can also be deployed on air-gapped on-premise systems for enterprises that do not allow any data sharing outside their own infrastructure.

Beyond FAQs with Process Automation

The benefits of privately-hosted models extend far beyond mere efficiency. Moveo.AI empowers enterprises with bespoke solutions, facilitating not only FAQs but also process automation on a grand scale. Use-cases range from real-time insights by analyzing first-party data to personalized customer interactions, showcasing the adaptability and versatility of privately-hosted LLMs in an enterprise setting.

• Greater security, privacy, and compliance
  Paramount concerns such as security and compliance especially in regulated industries is a huge concern. Moveo.AI addresses these concerns with diligence, ensuring that organizations can leverage the power of language models without compromising on data integrity, offering the option of on premise installation.

• Personalization
  Unlike GPT-4 or other third-party models, Moveo’s LLMs are fine-tuned on business conversations, ensuring utmost accuracy in the information provided. Instead of generic long responses, Moveo’s LLMs provide a more conversational experience with follow up questions ensuring an improved customer experience. Moveo’s Language Models can not only answer the majority of user questions but can also reliably connect to external APIs to provide the ultimate personalized customer experience.

• Scalability
  In most enterprise customer support applications, Service-Level-Agreements (SLAs) are in place to ensure a reliable performance by the AI Agents when it comes to response times and number of concurrent conversations it can support. From our experience, the standard offering of GPT-4 fails to achieve enterprise-grade SLAs, as it is very slow and cannot handle a large load of requests. Moveo.AI on the other hand, is capable of dealing with unexpected spikes in demands, supporting enterprise growth. From eCommerce with Black Friday and Christmas to GameTech and World Cup.

Read more: Moveo.AI vs GPT-4

Conclusion

The landscape of LLMs for enterprise applications has witnessed a transformative journey, from API-based models like GPT-4, to ChatGPT-wrappers like ChatBase to proprietary models developed by companies like Moveo.AI. The choice between self-hosted and API-based LLMs depends on the unique needs and priorities of each enterprise.. What will the LLMs be used for in the organization?

In scenarios where compliance holds paramount importance in operations, opting for proprietary LLMs becomes imperative. These proprietary models provide greater control over data handling, offering a tailored approach to address unique compliance challenges faced by the organization. On the other hand, for enterprises seeking a rapid implementation of pre-built models to streamline processes like automating simple FAQs, ChatGPT-wrappers offer a pragmatic solution. This approach however, could be considered a short-term solution due to its scalability concerns.