Making field support for overseas customers instantly responsive with AI.
Reduced business travel support costs by ¥180 million per year
Travel support costs
¥180 million reduced annually
Head office inquiries
380/month → 95/month
SLA achievement rate
91% → 98%
Global Field Support
Semiconductor Service Operation
Customer Site
Company E’s main customers are semiconductor factories around the world, and response speed during equipment trouble is directly tied to customer satisfaction and contract renewal rates。
Head Office Support
However, with many equipment generations and 20 years of design changes, modification histories, and trouble records scattered across systems, local engineers had relied on inquiries to head office specialists and business travel support。
AI Knowledge
As improving profitability in the after-sales service business became a management priority, this case shows how introducing CLAVI Mining simultaneously improved field support independence and the cost structure。
Industry
Development and manufacturing of semiconductor manufacturing equipment
Target department
Technical support and field engineering departments
Implementation period
8-week PoC → 6-month phased rollout
Challenges Before Implementation
When overseas local engineers handled equipment trouble at customer sites, an average of 380 inquiries per month were sent to specialists at the head office. Considering time differences, language barriers, and the location of documents, it was not uncommon for answers to take more than half a day. Customers repeatedly gave feedback such as, “Japanese support quality is high, but response is slow.”
For serious trouble cases, specialists from the head office had to travel to the site, and annual travel expenses and labor costs alone exceeded ¥200 million. After the COVID-19 period reduced the mobility of business travel, building a structure that could resolve issues locally became a management priority.
There was also a constant risk of providing incorrect procedures when design change history did not match the generation of the equipment installed on-site. Two years earlier, there had actually been a case where confusion between models caused incorrect operation and component damage, so management repeatedly questioned whether AI could deliver accurate model-specific search.
Reasons for Selection
Company E required AI to learn from 20 years of design drawings, equipment-specific manuals, and trouble records as-is, and to search accurately by equipment model number. General-purpose LLMs sometimes confused information across different model numbers, so they were removed from consideration early.
CLAVI Mining became the deciding choice because it can search while taking equipment model numbers and modification versions into account, and its patented technology for preventing incorrect answers structurally suppresses misinformation from different generations. In a PoC using 20 major trouble cases from the past five years, the match rate between AI-proposed cause hypotheses and the actual causes reached 83%.
Multilingual support was also highly valued because overseas local engineers could ask questions in their local languages, structurally reducing inquiries to head office specialists. Another major factor was that CS-guided implementation enabled operations in the local languages of nine sites to launch within three months.
Effects After Implementation
RESULT
01
[Travel support cases] 210/year → 78/year, a 63% reduction. Travel and labor costs were reduced by approximately ¥180 million, and the operating profit margin of the after-sales service business improved by 3.2 points.
RESULT
02
[Head office inquiries] 380/month → 95/month, a 75% reduction. Head office specialists were able to focus on design improvement work, also helping accelerate the development schedule for next-generation equipment.
RESULT
03
[Field response time] Average customer-site downtime decreased from 2.4 hours to 0.8 hours, a 67% reduction. Customers’ lost production opportunities were also greatly reduced.
RESULT
04
[Customer satisfaction] SLA achievement rate rose from 91% to 98%. Multiple major customers cited this as a reason for continuing contracts, and the following year’s maintenance contract renewal rate exceeded 97%.
Insights from This Case and Future Development
The essence of Company E’s case is the fact that profitability in after-sales service is determined by knowledge responsiveness. In the semiconductor industry, where equipment generations and model numbers continue to grow more complex, the speed of field engineers’ judgment is directly connected to customer satisfaction. Pre-travel diagnostic accuracy, on-site problem-solving capability, and reduced inquiries to head office can all be structurally improved through the use of knowledge AI.
At Company E, the use of CLAVI Mining began in the after-sales service department and is now expanding to the design department. The company has started building a system that immediately feeds trends from past field trouble back into the design phase, improving both market quality and design quality in an integrated way.
The company has also stepped into a marketing strategy that positions AI-based preliminary diagnosis as a service value in maintenance contracts with major customers, making it a differentiating factor in after-sales service. The service business of equipment manufacturers has entered an era where AI utilization creates a clear gap in competitiveness.
Comment from the Head of Technical Support
“Equipment trouble depends on model number and generation. Because CLAVI is designed to prevent incorrect answers for different model numbers in principle, local engineers can now trust AI responses. The turnover rate of local engineers is also improving.”