Revolutionizing Knowledge Access: A Successful Document Q&A Case Story
Empowering Experts and Streamlining Support Channels together with a German Manufacturer with the power of LLM and Big Data.
This solution has revolutionized the way our experts interact with machine manuals. It's like having a knowledgeable assistant at their fingertips, saving time and improving efficiency.
Our Final Result
Overview
Challenge
Solution
Technologies
Impact
Our Final Result
Our solution revolutionized user experience, cutting support tickets and boosting efficiency. Integrating Anomaly Detection improved reliability and preempted issues, transforming expert knowledge access.
Our goal was clear: to develop a solution that would transform the user experience and enhance operational efficiency for experts using machines.
- In response to the challenges faced by experts in accessing machine manuals and troubleshooting efficiently, we embarked on a Proof of Concept (PoC) project.
- This evolved into an MVP for production, leveraging the Gemini Model from Google's LLMs and Big Data integration for enhanced functionality such as anomaly detection.
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Contact UsChallenge
Traditional manuals made it hard for experts to access crucial information swiftly, resulting in delays, higher support ticket volumes, and diminished productivity.
Streamlining Troubleshooting and Empowering Users
Traditional support channels often lead to delays and inefficiencies in troubleshooting machine-related issues. Experts struggle to find relevant information in manuals quickly, leading to increased support ticket volumes and decreased productivity. Our challenge was to create a solution that would streamline this process and empower users to access information seamlessly.
Solution
Our solution transformed the user experience, reducing support ticket volumes and improving operational efficiency, while the integration of Anomaly Detection enhanced reliability and preempted potential issues, revolutionizing knowledge access for experts.
Tackling the Challenge in a Four-Step Process
Development of a PoC
We implemented the Document Q&A Solution using machine manuals hosted on the Motius infrastructure. This initial phase served as a testing ground, allowing us to refine our solution and gather valuable feedback from users.
Moving to the clients infrastructure
This phase involved migrating our solution to the client's infrastructure, ensuring seamless integration and optimal performance. By leveraging Google, including the powerful Gemini Large Language Model, we enhanced scalability and reliability, laying the foundation for future iterations. Migrating from the Motius infrastructure to the clients infrastructure on the Google Cloud Platform required careful planning and execution. We replaced existing services with Google services to ensure seamless integration and optimal performance.
Development of a Minimum Viable Product (MVP)
During this phase, we gathered requirements based on feedback and the overall vision for the product. We also expanded the solution to include additional documents, such as service descriptions and FAQs, which presented architectural challenges. To address these, we implemented robust data structures and indexing mechanisms, ensuring efficient and cost-effective document retrieval and knowledge access.
Integration of Big Data capabilities
The third phase of our approach focused on integrating Big Data capabilities, such as Anomaly Detection, to further enhance the functionality and value of our solution. Leveraging data from the clients Data Warehouse, we implemented advanced algorithms to identify and address irregularities swiftly, improving operational reliability and preempting potential issues. Transitioning from the PoC to the MVP phase presented several technical challenges.
