Why would a customer services business choose to use a Custom LLM over an Open Source LLM?
The difference between the two LLMs is seen in terms of their training data, fine-tuning process, and specific applications.
Open Source Large Language Model:
- Training Data: Large language models like GPT-3 or GPT-4 are pre-trained on a massive corpus of publicly available text from the internet. This corpus typically covers a wide range of topics and languages.
- General Knowledge: Base models are designed to have broad general knowledge and can generate coherent and contextually relevant text on a wide array of subjects.
- Fine-Tuning: While they start with general knowledge, these models can be fine-tuned on specific tasks or domains to make them more useful for particular applications. However, this fine-tuning is usually not highly specialized and can be applied to a variety of tasks.
Custom Large Language Model:
- Training Data: Custom large language models are built on top of base models but undergo additional training with specific, proprietary, or domain-specific datasets. These datasets can include a company's internal documents, customer interactions, or industry-specific information.
- Tailored Knowledge: Custom models are trained to have a deeper understanding of the specific knowledge, terminology, and context relevant to a particular business, industry, or use case.
- Fine-Tuning for Specific Use Cases: Custom models are fine-tuned extensively for a narrow set of tasks or applications, making them highly specialized and optimized for the intended use case. This fine-tuning process makes them more valuable for tasks like customer service, medical diagnosis, legal advice, etc.
In summary, while a LLM is a generic AI language model with broad general knowledge, a Custom LLM is a specialized version of the base model that has been fine-tuned on specific data and applications to provide more relevant and accurate responses within a particular domain or industry.
Custom models are tailored to meet the unique needs of businesses and organisations, making them a powerful tool for addressing specific challenges and delivering tailored solutions in areas like customer service, healthcare, finance, and more.
How Custom LLMs can improve customer services
Personalised Customer Experience
Custom large language models can be trained with your specific industry knowledge, product information, and customer data, allowing them to provide highly personalised responses to customer inquiries. This personalization goes a long way in making customers feel valued and understood. By tailoring responses to individual needs, businesses can foster stronger customer relationships and increase loyalty.
Unlike human customer service representatives who have fixed working hours, language models are available 24/7. This means that customers can get assistance at any time, even outside of traditional business hours. This round-the-clock availability is especially valuable for international businesses with customers in different time zones.
As businesses grow, so does the volume of customer inquiries. Scaling customer service teams to meet this demand can be costly and time-consuming. Custom large language models can handle a high volume of inquiries without the need for hiring and training additional staff. This scalability ensures that customer service remains efficient and cost-effective.
Human agents may inadvertently provide inconsistent responses due to variations in their understanding of policies or product knowledge. Custom large language models, on the other hand, offer consistency in responses. They can be programmed to follow brand guidelines and provide uniform information, ensuring that customers receive accurate and reliable assistance every time.
In a global marketplace, offering support in multiple languages is crucial. Custom language models can be trained to understand and respond in various languages, breaking down language barriers and catering to a diverse customer base. This capability can give businesses a competitive edge in the international market.
Reduced Response Times
Customers expect quick responses to their inquiries. Custom large language models can process and generate responses in real-time, significantly reducing response times compared to traditional email or ticket-based support systems. Faster responses lead to higher customer satisfaction and retention.
Hiring and training customer service agents can be a significant expense for businesses. Custom language models, once developed and deployed, can provide cost-effective customer service solutions. They require minimal ongoing maintenance and can handle a high volume of inquiries at a fraction of the cost of maintaining a large human workforce.
Our experiences building Custom LLMs for the customer service industry
Optimising Call Centre performance for Interact
Historically, the call centre sector has grappled with optimizing operations and delivering outstanding customer service. As an industry leader, Interact sought to overcome these challenges. Conventional approaches to monitoring and enhancing call centre performance have been inadequate in resolving these concerns.
Deeper Insights developed an AI-powered solution for Interact to tackle call centre challenges in operations and customer service. By leveraging natural language processing and computer vision, the technology offers real-time feedback and post-call analysis. Monitoring key metrics and assessing agent performance, the solution equips agents with essential tools for improvement, ultimately enhancing customer experiences in the call centre environment.
A partnership between Interact and Deeper Insights has led to the development of an innovative AI-powered solution that transforms call centre operations. This cutting-edge system employs natural language processing (NLP) including the latest Large Language Model (LLM) technology and computer vision to deliver real-time feedback and post-call analysis for call centre agents.
The solution monitors essential metrics, including talking speed, cross-talk, monologuing, extended silence, energy level, speaking/listening ratio, and script adherence. Additionally, a post-call scorecard assesses agents' utilization of filler words, loudness variation, confidence, and script adherence.
THE END RESULT
The AI-powered call centre solution developed for Interact enables call centres to elevate agent performance and enhance customer experiences. By providing real-time metrics and post-call analysis, agents are equipped with the tools they need to improve their performance, leading to better customer experiences and satisfaction.
"Deeper Insights has consistently delivered outstanding guidance and developed pioneering solutions, enabling us to transform our call centre for the future."
-Jack Sands, Interact, CEO
Building a media monitoring chatbot prototype for Microsoft
The integration of Deeper Insights' AI tools and Microsoft's products, including Cortana, Bing, and Cognitive Services, was a key aspect of the development process for a prototype chatbot with the purpose of demonstrating media monitoring capabilities within a chat environment. This project was initiated in order to evaluate the capabilities of Microsoft's then recently launched Bot Framework and LUIS chatbot services, with the ultimate goal of building a chatbot from the ground up using these tools.
To achieve this objective, a team comprising experts from Deeper Insights and Microsoft engineering worked closely together to design and develop a prototype chatbot. Through their combined efforts, they were able to successfully accomplish their goal of creating a fully functional v1.0 version of the chatbot and did so in just five days. This prototype was then released on multiple chat platforms, including Skype and Slack, as a means of showcasing the capabilities of the media monitoring chatbot to users.
THE END RESULT
The successful completion of this project not only demonstrated the effectiveness of the Deeper Insights AI tools and Microsoft's products in building a chatbot, but also highlighted the efficiency of the two teams in bringing the prototype to fruition in a short period of time.
"DI's technology are great examples of combining bespoke AI technology with various Microsoft products such as Cortana, Bing and Cognitive Services. Running a joint engineering project to integrate Microsoft Bot Framework to DI's stack was a productive way to add an intuitive multi-canvas interface to their product offering."
-Petro Soininen, Microsoft Engineering