Customized AI brain solutions at the departmental level
The root causes of inefficient cross-departmental knowledge retrieval are: differences in terminology and different business processes.AUM is optimized by..:
- Create a proprietary knowledge base: Create separate AI instances for each department (HR/legal/finance, etc.) and inject department-specific documents (e.g., personnel systems, regulatory casebooks, accounting standards, etc.).
- Intelligent Terminology ConversionFor example, when the Sales Department inquires about "Customer Success Rate", the system can automatically correlate with the "Closing Rate" field in the CRM system.
- workflow interfacingSupport for automatically generating Notion documents from search results, sending emails or triggering the approval process (e.g., initiating the modification process directly after contract search)
- Multimodal results presentation: Automatically generate visual charts for complex queries (e.g., "Show budget execution of each department in the last six months").
Practical example: When the R&D department inquires about "production line troubleshooting solutions", the system can intelligently identify the equipment model associated with maintenance manuals, historical work orders and supplier contact information to form a complete solution package.
This answer comes from the articleAUM: A private enterprise AI knowledge base client running locally offlineThe
May not be reproduced without permission:AI productivity tools " How to optimize the efficiency of knowledge retrieval in cross-departmental collaboration?
