Written by AQ | Sep 30, 2025 3:00:00 PM
- Troubles are handled by non-experts
- RAG guides the actions to take when a trouble occurs
- Only when the resolution method is unknown, communication is established with an expert, and the expert resolves the issue
- By recording the resolution method after the issue is resolved, the know-how accumulates in the database
For Various Industries Industry-Specific Use Cases
01. Manufacturing (Technical Support & Maintenance Support)
- Use Case
- Automated initial response to sudden error codes in complex manufacturing equipment.
- Digitalization of troubleshooting know-how from experienced technicians and immediate sharing with the work site.
- Guidance on parts replacement procedures by RAG based on past repair records.
- Effects
- Minimization of downtime.
- Improvement in the skills acquisition speed of junior and non-expert technicians.
- Reduction of on-site visits by veteran technicians and workload mitigation.
02.Finance (System Operation & Customer Support)
- Use Case
- AI implementation in help desk operations for inquiries about minor errors or operation procedures of the core system.
- Documentation of system failure recovery procedures by veteran operators, keeping manuals updated at all times.
- Handling customer inquiries about complex financial product regulations with specialist intervention, adding the response history to the knowledge base.
- Effects
- Reduction in help desk staffing costs and standardization of response quality.
- Shortening of recovery time during major system failures (MTTR reduction).
- Strengthening compliance in customer support responses.
03.Information and Communication Technology Industry (IT Trouble Handling/Help Desk)
- Use Cases
- First-level triage and RAG-based guidance for handling IT infrastructure failures or service usage issues by non-specialized operators.
- Escalation of only unresolved issues (where RAG cannot provide a solution) to expert engineers, with automatic recording of response logs and solutions into the Knowledge DB.
- Analysis of customer complaints and inquiries to identify gaps in manuals and FAQs, followed by automatic AI-powered content supplementation.
- Effects
- Improvement in trouble resolution rates by non-specialized personnel (increase in One-Stop Resolution).
- Maximization of labor productivity by allowing expert engineers to concentrate on high-level tasks.
- Elimination of operational quality variations by consistently maintaining the freshness and comprehensiveness of the Knowledge DB.