Challenge
Customer advisors and logistics planners in the timber industry must access information from a wide range of sources on a daily basis:
- Product data sheets and technical documentation
- Inventory and stock availability
- Order and delivery information
- Complex product combinations and compatibility rules
Critical information is often distributed across multiple systems. Searching for relevant data consumes valuable time and creates inefficiencies between ERP systems, document repositories, and customer service operations.
At the same time, customers expect fast and accurate answers regarding product availability, delivery times, and product configurations.
Approach
SWMS developed a conversational AI assistant that intelligently unlocks existing company knowledge and makes it accessible through natural language interactions.
The solution combines modern AI technologies, including:
- Large Language Models (LLM)
- Retrieval-Augmented Generation (RAG)
- Document analysis and semantic search
- Real-time ERP and inventory integration
- Voice and chat interfaces
By connecting structured enterprise data with unstructured documents, the assistant provides contextual and highly relevant information whenever it is needed.
Solution
The result is an intelligent knowledge and process assistant for operational business processes.
Employees can ask questions in natural language regarding:
- Product availability
- Order status
- Delivery schedules
- Product compatibility
- Technical specifications
The assistant analyzes the request, retrieves relevant information from ERP systems, technical documentation, and internal databases, and generates context-aware responses in real time.
This enables organizations to automate routine inquiries, reduce response times, and improve the quality and consistency of customer interactions.
The underlying architecture is modular and scalable, allowing the solution to be transferred to other industries such as manufacturing, logistics, field service, and technical support.
Funding
This project is being carried out within the framework of the European innovation project WASABI (“White-label Shop for Digital Intelligent Assistance and Human-AI Collaboration in Manufacturing”).
WASABI is co-funded by the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. 101092176. The activities of SWMS are supported through the WASABI Open Call Programme as a funded innovation experiment.
The views and opinions expressed are solely those of the authors and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.