Want to answer customer inquiries faster and more accurately - without the manual effort? A company-specific AI makes it possible. Retrieval-augmented generation (RAG) enhances responses by combining a trained AI model with real-time company data, ensuring relevant, context-aware answers.
In this article, we’ll explore how we successfully integrated CustomGPT.ai into an authoring company’s service request process. Our goal? To automate request handling, boost efficiency, and enhance customer satisfaction.
Retrieval-augmented generation (RAG) is a powerful method that enhances the performance of large language models (LLMs). Instead of relying solely on pre-trained data, RAG pulls real-time, external knowledge before generating responses. This approach allows organizations to apply AI to specialized topics or internal data without expensive retraining.
By retrieving information from reliable sources, RAG ensures more accurate and relevant answers. The result? A cost-effective way to adapt AI systems while improving response quality—without the need for ongoing, resource-intensive model updates.
LLMs are transforming AI, but they have limitations. One major issue is hallucination, where AI generates inaccurate or completely fabricated answers. Additionally, LLMs rely on static training data, meaning they may provide outdated information and lack real-time knowledge updates.
This is where RAG makes a difference. Instead of relying solely on pre-trained data, RAG retrieves and integrates real-time, relevant information before generating a response.
How RAG works:
By combining these steps, RAG enhances accuracy, ensures up-to-date responses, and delivers more reliable, context-aware answers - making AI-powered customer service smarter and more effective.
CustomGPT.ai is a powerful solution for creating language models tailored to a company's specific needs and data. In our project, we integrated CustomGPT.ai with a Retrieval-Augmented Generation (RAG) system to streamline service request handling. This ensures responses are based on current, relevant company data, making them precise and contextual.
The key role of CustomGPT.ai in this project was to automate suggested responses for incoming customer emails. By implementing this system, Customer Service Representatives (CSRs) saw a significant reduction in workload - instead of crafting each response manually, they could rely on AI-generated suggestions.
The service request process follows a structured flow to ensure efficient request handling:
Categorization
CSRs assign each request to a category - such as Complaint, Advice, or Support. Each category has subcategories for more detailed classification.
Decision table for response qualification
After categorization, a decision table determines whether the request qualifies for an automated response. Frequently recurring requests (e.g., Advice) trigger automatic response generation. More complex cases (Complaints or Support) may require manual processing. The RAG system then searches the company’s database for relevant information and generates a precise, context-aware response.
Suggested responses
The AI-generated response appears in the Pega interface, where CSRs can:
Interaction processing
Once sent, the system provides completion control options:
Editing flexibility
If needed, service managers can modify AI-generated responses to better fit the request. If they reject a response, they must provide a reason and draft their own reply. This balance of automation and flexibility ensures efficient, high-quality service.
To seamlessly integrate CustomGPT.ai into customer service, we connected it to Pega via a REST API. Pega automatically processes service requests and generates context-aware response suggestions based on company data.
When a request is received, relevant details such as the date of receipt, subject, and email text are transmitted to CustomGPT.ai through an interface. The AI then uses RAG to create a response and feeds it directly back into the Pega system.
The integration was implemented in stages, starting with a check to determine whether an automated response already exists. If no response is available, the system retrieves the necessary information and generates one. A decision table controls whether the request is answered automatically or forwarded to a Customer Service Representative (CSR) based on its category.
To ensure stability and error handling, a retry logic was integrated. If an API call fails, it is automatically reattempted using a queue processor. In the user interface, CSRs can see a transparent status display and choose to edit, reject, or send the AI-generated response.
The integration of CustomGPT.ai into the service request process has brought numerous benefits:
The successful integration of AI tailored to the company's requirements into the service request process of the authoring company shows how targeted automation can make customer service more efficient and responsive.
By combining the generative capability of LLMs with the retrieval augmented generation approach, it is possible to ensure that contextual responses are always generated using up-to-date and relevant company data. This results in accurate, contextual responses that facilitate the work of CSRs, increase efficiency and improve the quality of customer communication at the same time.
By connecting to Pega, the solution could be seamlessly integrated into existing processes. As part of the platform, it enables the flexible automation of recurring tasks - without removing the human factor from the process. This allows service employees to focus their time more efficiently on more complex and individual requests instead of dealing with standard requests. Faster response times, reduced manual effort and better scalability are just some of the benefits. Most importantly, the technology remains adaptable - it grows with the company's requirements and can be continuously optimized.
With this approach, the authoring company has not only improved its internal processes, but also increased customer satisfaction. A good example of how smart digitalization can make everyday life easier for everyone involved - and that's exactly what matters.