10,000 Inquiry Records Over 20 Years Transformed into Working Knowledge: KEM Removes the Burden from Veteran Engineers

Kyoto Electronics Manufacturing Co., Ltd.

Manufacturing·1-500 employees·Customer Support

10,000 Inquiry Records Over 20 Years Transformed into Working Knowledge: KEM Removes the Burden from Veteran Engineers

At a glance

• 10,000 inquiry records across 20 years transformed into a working knowledge base
• Inquiry volume grew 30% to 1,250/year before implementation; response hours exceeded 2,000 hours annually
• 200 FAQ answers deployed, organized across six product categories
• Built from real inquiry logs, not manuals, targeting the exact questions users actually ask

The Challenge

Kyoto Electronics Manufacturing (KEM) is a specialist manufacturer of analytical instruments, providing proprietary products that integrate cutting-edge technology, including moisture analyzers and other measurement instruments that support industry across multiple sectors. As inquiry volume grew nearly 30% in recent years, reaching approximately 1,250 inquiries annually, the Technology Development Division faced an escalating burden. Total response time over three years more than doubled to approximately 2,000 hours.

The nature of KEM's products made the problem worse. Highly specialized and diverse, they serve customers across industries from food manufacturing to pharmaceuticals. Even for the same product, measurement targets differ by user, so technical inquiries from the same customer often recur. Customers spanning this wide range of use cases and contexts required nuanced, context-specific answers.

The team facing the greatest burden was not junior staff, but experienced veterans. The more knowledgeable a person, the more likely they were to be pulled into inquiry responses, repeatedly interrupting development work in progress. When a question required investigation and verification rather than an instant answer, the team had no choice but to stop development work and redirect to inquiry handling. Since multiple staff collaborate on development projects, halting and restarting required coordination. With nearly 90 staff in the Technology Development Division, several simultaneous inquiry-driven work stoppages could affect the efficiency of the entire department.

Compounding the challenge, expert managers with deep knowledge in specific areas personally handled certain inquiries to ensure consistent answers on behalf of the company. Because managerial staff time was not included in the calculated response hours, the actual burden was likely even higher than the figures showed. Reducing this invisible burden was a core motivation for the initiative.

Why Helpfeel

The team's search for a solution centered on AI. AI chat tools that generate response text were considered, but many of these tools have hallucination risks. Since the AI generates the answer itself, accuracy is hard to guarantee, and humans still have to verify every response, meaning the workload doesn't actually change.

Helpfeel uses AI for its intent-prediction search, expanding the language understanding of search queries, but the actual response content is not generated by AI. This eliminates the risk of AI-generated misinformation and ensures accuracy. The availability of post-implementation operational support and trend analysis was also appealing.

The team considered building FAQs from product instruction manuals. Many inquiries ultimately lead to the same answer that could be found in a manual, basic operating procedures and so on. But in practice, users' measurement purposes, sample types, and operating environments vary greatly. Even if the manufacturer publishes correct, general guidance, a user facing a specific problem will hesitate ("maybe my case is special") and want to confirm by phone or email unless they find an answer that matches their exact situation. Building from product manuals would cover most cases, but would never hit 100%.

The key insight came from learning that if actual inquiry records existed, Helpfeel Analytics could analyze inquiry patterns to identify high-priority topics and generate FAQ article drafts based on historical responses. Every product has its own characteristic questions, so by analyzing the most frequently asked topics from the accumulated data and prioritizing articles accordingly, the team could surgically target the specific inquiries they most wanted to reduce.

When Technology Development Division staff tested the FAQ, deliberately trying tricky questions across a wide variety of patterns, it consistently returned accurate, appropriate answers. This gave the team frontline confidence that "this will work." The calculation that the cost savings from reduced inquiry handling time exceeded implementation costs also supported the final decision to proceed.

What They Did

• Hosted the FAQ on the company's corporate website "Frequently Asked Questions" page using Helpfeel's interface, organized into six product-category sections with approximately 200 total answers
• Used Helpfeel Analytics to cluster-analyze approximately 10,000 inquiry response records accumulated over 20 years, identifying frequently occurring topics and generating FAQ article drafts
• Focused on the most recent 10 years of inquiry data to ensure consistency with current product specifications
• Built answers that include examples of commonly encountered usage conditions, with key points reinforced by repeating them with varied phrasing to ensure users can resolve questions with confidence
• Prioritized articles based on the most frequently occurring topics per product category, making inquiry trends that each staff member had only an intuitive sense of quantitatively visible

Results

The FAQ went live in October 2025 with 200 answers built from 10,000 inquiry records spanning 20 years. After the launch announcement was posted on the company's groupware platform, it immediately received a large number of "likes" from across the company, not just from the Technology Development Division. Staff in all customer-facing departments had been waiting for exactly this kind of solution.

Beyond end users looking up answers directly, customer support staff consult the FAQ when formulating responses, and sales staff reference it in real time during customer visits. The team plans to continue refining content and entry points into the FAQ based on usage patterns. The concrete effects, how much the FAQ will reduce development interruptions from inquiry handling and whether faster self-resolution improves customer satisfaction, will be verified going forward.

"I thought it would be useful someday, but honestly I'd given up on actually being able to use it. Discovering that it could be put to use in this way was a genuine revelation, and a relief that the effort of keeping those records was justified."

Noboru Yamaguchi, Executive Officer & General Manager, Technology Development Division

Looking Ahead

Implementing Helpfeel and establishing a systematic approach to handling high-frequency inquiries marks the starting line for the company's goal of "DX that converts tacit knowledge into explicit knowledge." The team continues to refine content based on usage patterns.

The company's administration division is also showing enthusiasm about applying a similar system for employee inquiries, HR policy questions and similar internal queries that currently come in by phone. From a data utilization perspective, internal systems contain coordination records and department-specific documentation that haven't yet been fully leveraged. Building on the success of this initiative with Helpfeel Analytics, the company plans to continue expanding DX efforts, including unearthing those hidden "treasures" of accumulated organizational data.

“I thought it would be useful someday, but honestly I'd given up on actually being able to use it. Discovering that it could be put to use in this way was a genuine revelation, and a relief that the effort of keeping those records was justified.”

— Noboru Yamaguchi, Executive Officer & General Manager, Technology Development Division