Improved Customer Service Interaction
This Business Use Case is about Probabilistic Technologies as well as Deterministic Technologies.
Key Features and Benefits
- Improved Customer Service: Answer complex queries by linking and presenting relevant information, resulting in more effective customer interactions.
- Efficient Information Access: Enables fast and precise retrieval of relevant documents and data, significantly reducing the time taken to address customer needs.
- Increased Efficiency: Speeds up response times and enhances service quality by providing better-informed answers, leading to higher productivity and customer satisfaction.
Enhance Customer Service Interaction Through Knowledge Graphs
Delivering effective customer service often relies on the ability to quickly access and connect relevant information. Traditional approaches lacking contextual information struggle with handling complex queries, leading to delays and less satisfying interactions.
Knowledge graphs powered by Graph-RAG and Document Processing, can help transform organizations and how they handle customer inquiries. Knowledge graphs structure data into an interconnected format, enabling fast and precise Q&A over Document Stores. By extracting triples, a comprehensive knowledge base can be built that links relevant information across multiple sources. This structured approach significantly reduces the time required to find accurate answers, enhancing both response speed and service quality. As a result, customer service teams can deliver more informed and efficient responses to complex questions, driving higher satisfaction levels.
For example, a support team could utilize a knowledge graph to seamlessly access detailed product information, troubleshooting steps and customer history, allowing them to provide more relevant and timely assistance to customers.
Technical Capabilities
Technical capabilities encompass the range of skills, tools, and methodologies to implement and manage advanced technological solutions.
Document Processing automates the digitization and processing of documents to minimize manual work. This technology extracts relevant data and content from texts, resulting in efficient information retrieval.
Graph-RAG leverages graph structures to improve information retrieval, providing more precise and contextual responses by utilizing linked data. Integrating Retrieval Augmented Generation (RAG) principles, it combines retrieval-based and generative AI models. This approach supplements the knowledge of large language models (LLMs) with specific, up-to-date information from graph structures, resulting in more accurate, relevant, and human-like responses while minimizing inaccurate answers.
Technical Use Cases
Explore the specific functionalities and applications of technology solutions.
Enables precise answers to specific questions from large document collections to improve access to important information.
Links and visualizes data with the help of ontologies into a single source of truth to improve data and knowledge integration and decision making
Recognizes subject-predicate-object relationships in text to create structured knowledge representations for deeper analysis.
Andrew, Tim, Inga, Mark and Nathalia
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