Guest Post: How Remberg Improves the Transparency of AI Features with Langfuse
by Hagen Schmidtchen, co-Founder of Remberg
At remberg, we prioritize transparency and efficiency in all of our AI-driven features. To achieve this, we have integrated Langfuse into our processes. Langfuse is a powerful tool that allows us to provide a clear and detailed view of how our AI features are being used, ensuring that we have complete insight into how our users interact with our technology.
We successfully use Langfuse for the following reasons:
Tracing individual steps
With Langfuse, we can meticulously track every step of the AI process. By logging both the inputs and outputs of large language models (LLMs), we gain a comprehensive understanding of what is happening behind the scenes. This level of detail helps us to troubleshoot, optimize workflows, and ensure that every interaction is transparent and understandable. It also helps us to gain an understanding of which types of interactions are working well, which ones still require optimization and which ones exceed the capabilities of the system (shining a light on where we have to better manage our user’s expectation or limit inputs).
Tracking token usage
Efficient resource management is critical to delivering consistent performance. Langfuse allows us to track token usage at the user or tenant level. This detailed tracking helps monitor resource consumption, ensure fair usage, and plan for scalability. It also helps us optimize the cost-effectiveness of our services, which benefits both our users and our operations.
Generating LLM performance metrics
Performance is the key to user satisfaction. Langfuse helps us generate detailed performance metrics that are essential for maintaining and improving the quality of our AI features. By analyzing these metrics, we can identify potential problems, implement improvements, and ensure that our features are performing at their best. This continuous monitoring helps us stay ahead of the curve in delivering world-class AI solutions.
Gathering user feedback on our LLM app
User feedback is invaluable in the development of our AI features. Langfuse allows us to systematically collect and analyze feedback. This feedback loop ensures that we are constantly aware of our users’ needs and can make informed decisions to improve their experience. It allows us to refine our features based on real-world usage and user suggestions, resulting in more intuitive and effective solutions.
Developing the AI Copilot for remberg XRM
At remberg, we are advancing our capabilities by developing an industrial AI copilot for our XRM (Extended Relationship Management) system. This AI copilot is designed to streamline and enhance user interaction with our XRM solution, making it more intuitive and efficient. The AI Copilot will assist our users by automating routine tasks, making intelligent suggestions, and delivering insights derived.
By using Langfuse, we are ensuring that every aspect of AI Copilot is transparent, optimized for performance, and set up the right way to gather the insights we need to continuously improve our AI capabilities, and we look forward to continuing to work with the Langfuse team and their technology.
About Hagen Schmidtchen
Hagen is Co-Founder and Chief Architect of remberg. With a background in software engineering and Machine Learning, he is now leading remberg’s automation and AI initiatives and has first hand experience across the full product delivery chain, including the usage of Langfuse. Find Hagen’s profile on LinkedIn
About remberg
The Remberg XRM is a cloud-based, mobile and user-friendly software that has been specially developed for service and maintenance teams. It integrates powerful generative AI to optimize workflows and increase efficiency. An integrated AI Copilot helps to answer inquiries faster, to formulate or translate e-mails effectively, to better understand complex cases and to fill out forms and reports in seconds.