Equipment Maintenance AI Agent 'EMLink Intelligence' Significantly Updated — Now Capable of Autonomous Execution of Complex Tasks and Building Custom RAG in as Little as 1 Minute Using In-house Knowledge
Setsubi Hozon Sogo Kenkyusho has significantly updated its AI agent 'EMLink Intelligence' for equipment maintenance, adding autonomous execution of complex tasks and the ability to build a custom RAG in as little as 1 minute. This aims to reduce maintenance work time by up to 95% and support solutions for unique challenges.
📋 Article Processing Timeline
- 📰 Published: June 12, 2026 at 18:10
- 🔍 Collected: June 12, 2026 at 09:21
- 🤖 AI Analyzed: June 12, 2026 at 16:52 (7h 31m after Collected)
Setsubi Hozon Sogo Kenkyusho Co., Ltd., a provider of the equipment maintenance cloud service 'EMLink' for factories and infrastructure, has significantly updated its AI agent function, EMLink Intelligence. It has evolved from an 'AI that answers questions' to an AI agent that autonomously executes multiple steps by decomposing tasks simply by being given a goal. It also supports graphical display of analysis results and reporting output to Excel and PDF.
Furthermore, it will be possible to easily build a custom RAG by simply incorporating the company's standard manuals and glossaries (scheduled for release), and by combining it with the autonomous agent, it will derive company-specific answers (unique solutions) that are not general theories. It autonomously executes everything from search, aggregation, graphing, analysis, to report creation, and in our verification, it reduces maintenance work time by up to 95%.
It supports the entire process from information gathering necessary for preventive maintenance to action plan formulation and prioritization integrating past know-how.
Limitations of Single-Instance Analysis Using Generative AI
Until now, the use of generative AI in the maintenance field has primarily been 'inquiry-based,' where users read Excel or CSV files and receive one answer per question. However, maintenance field operations consist of a series of multiple steps such as data normalization, search, aggregation, graphing, analysis, and reporting. With conventional AI utilization, humans had to continuously provide detailed instructions for each step.
Additionally, the difficulty in verifying the basis of AI responses and their consistency with internal maintenance procedures and standards has been a factor hindering adoption in the field.
EMLink Intelligence Update
Main Updates
1. Evolution to an 'AI Agent' that Operates Autonomously
By simply communicating a goal, the AI autonomously decomposes the tasks to be performed and sequentially executes multiple steps: search → aggregation → graphing → analysis → report creation. The execution progress and results of each step can be confirmed on the screen, allowing humans to focus on the final decision.
2. Graphical Display of Analysis Results
Aggregation and analysis results are visualized in various formats such as bar graphs and matrix diagrams. Since they can be grasped as diagrams rather than mere numbers, trends and priorities can be intuitively understood.
3. Reporting Output (Excel/PDF)
Generated tables and analyses can be output and shared as Excel (multiple sheets) or PDF (A4 report). It also supports copying to the clipboard, allowing direct use in internal reports and reviews.
4. Easily Build Custom RAG — Combine with Agent for 'Unique Solutions' (Scheduled for Release)
A custom RAG can be easily built by simply incorporating the company's standard manuals, maintenance procedures, various standards, and glossaries. By combining this with Intelligence's autonomous agent, it derives 'unique solutions' that align with the company's site and standards, rather than general theories.
Perspective
Generic AI
EMLink Intelligence (Custom RAG x Agent)
Response
Stays within general theories
Unique solutions based on company manuals and standards
Technical Terms
Prone to misinterpretation
Correctly interprets abbreviations, common equipment names, and model numbers using glossaries
Basis for Response
Unclear
Specifies the source (which document, which part), useful for audits and training
Construction
(Supplemented with prompts as needed)
Builds custom RAG in 1 minute or more just by importing company documents (PPT, WORD, PDF, TXT)
For example, if asked, 'Referencing the vibration standards for centrifugal pumps from our company's maintenance procedures, check if there are any dangerous signs in the inspection results from the past year,' it will provide an answer with the relevant section cited as the basis.
These tasks can be autonomously executed just by 'assigning' them:
From an RBI (Risk-Based Inspection) perspective, calculate risk from impact (CoF) x probability of occurrence (PoF) and graph it using a risk matrix.
For equipment used over a long period, compare replacement and continued repair using NPV/IRR (Defender–Challenger) and present the top 5 replacement priorities.
Organize failure modes from past defects, inspection history, and accumulated trouble cases within the company, and extract equipment that requires重点 inspection.
Aggregate maintenance costs for the past few years by equipment type and identify outliers and abnormal costs.
For inspection results of specific machinery, cite the company's maintenance procedures and determine OK/NG with a basis (RAG).
Identify instances where spare parts inventory falls below safety stock levels and create an order proposal for long-lead-time parts.
Reduce Work Time by Up to 95%
These analyses and report creations traditionally required the person in charge to go through steps like 'search → filter → aggregate → graph → consider.' By having EMLink Intelligence autonomously execute these series of tasks, our verification shows a reduction in work time of up to 95%. This allows personnel to focus on 'decision-making' tasks rather than 'creation' tasks.
What is EMLink?
'EMLink,' an Asset Management Cloud, is an equipment management service for plants and factories. Aimed at cost reduction, resource efficiency, and strengthening management systems, it is being introduced nationwide, primarily to process manufacturing industries such as chemicals, materials, pharmaceuticals, and food, as well as infrastructure operators. As a 'platform-type EAM for manufacturing sites' that connects site data with management data, it can be introduced starting from 120,000 yen per month per factory (50 users).
About Setsubi Hozon Sogo Kenkyusho Co., Ltd.
EML is a maintenance tech company that provides the asset management cloud 'EMLink' for plants, factories, and infrastructure. With the mission 'to support social and industrial infrastructure from behind the scenes,' since its establishment, it has provided 'EMLink' to over 100 leading companies in sectors such as oil, chemicals, automotive, steel, power infrastructure, and local governments.
Company Overview
Company Name: Setsubi Hozon Sogo Kenkyusho Co., Ltd.
Established: September 2022
Representative: Shogo Aihara, Representative Director & CEO
Headquarters: 3-18-16 Megurohoncho, Meguro-ku, Tokyo
Business Description: Development and sales of the asset management system 'EMLink' for factories and infrastructure, and related consulting.
Website: https://em-labo.co.jp/
Furthermore, it will be possible to easily build a custom RAG by simply incorporating the company's standard manuals and glossaries (scheduled for release), and by combining it with the autonomous agent, it will derive company-specific answers (unique solutions) that are not general theories. It autonomously executes everything from search, aggregation, graphing, analysis, to report creation, and in our verification, it reduces maintenance work time by up to 95%.
It supports the entire process from information gathering necessary for preventive maintenance to action plan formulation and prioritization integrating past know-how.
Limitations of Single-Instance Analysis Using Generative AI
Until now, the use of generative AI in the maintenance field has primarily been 'inquiry-based,' where users read Excel or CSV files and receive one answer per question. However, maintenance field operations consist of a series of multiple steps such as data normalization, search, aggregation, graphing, analysis, and reporting. With conventional AI utilization, humans had to continuously provide detailed instructions for each step.
Additionally, the difficulty in verifying the basis of AI responses and their consistency with internal maintenance procedures and standards has been a factor hindering adoption in the field.
EMLink Intelligence Update
Main Updates
1. Evolution to an 'AI Agent' that Operates Autonomously
By simply communicating a goal, the AI autonomously decomposes the tasks to be performed and sequentially executes multiple steps: search → aggregation → graphing → analysis → report creation. The execution progress and results of each step can be confirmed on the screen, allowing humans to focus on the final decision.
2. Graphical Display of Analysis Results
Aggregation and analysis results are visualized in various formats such as bar graphs and matrix diagrams. Since they can be grasped as diagrams rather than mere numbers, trends and priorities can be intuitively understood.
3. Reporting Output (Excel/PDF)
Generated tables and analyses can be output and shared as Excel (multiple sheets) or PDF (A4 report). It also supports copying to the clipboard, allowing direct use in internal reports and reviews.
4. Easily Build Custom RAG — Combine with Agent for 'Unique Solutions' (Scheduled for Release)
A custom RAG can be easily built by simply incorporating the company's standard manuals, maintenance procedures, various standards, and glossaries. By combining this with Intelligence's autonomous agent, it derives 'unique solutions' that align with the company's site and standards, rather than general theories.
Perspective
Generic AI
EMLink Intelligence (Custom RAG x Agent)
Response
Stays within general theories
Unique solutions based on company manuals and standards
Technical Terms
Prone to misinterpretation
Correctly interprets abbreviations, common equipment names, and model numbers using glossaries
Basis for Response
Unclear
Specifies the source (which document, which part), useful for audits and training
Construction
(Supplemented with prompts as needed)
Builds custom RAG in 1 minute or more just by importing company documents (PPT, WORD, PDF, TXT)
For example, if asked, 'Referencing the vibration standards for centrifugal pumps from our company's maintenance procedures, check if there are any dangerous signs in the inspection results from the past year,' it will provide an answer with the relevant section cited as the basis.
These tasks can be autonomously executed just by 'assigning' them:
From an RBI (Risk-Based Inspection) perspective, calculate risk from impact (CoF) x probability of occurrence (PoF) and graph it using a risk matrix.
For equipment used over a long period, compare replacement and continued repair using NPV/IRR (Defender–Challenger) and present the top 5 replacement priorities.
Organize failure modes from past defects, inspection history, and accumulated trouble cases within the company, and extract equipment that requires重点 inspection.
Aggregate maintenance costs for the past few years by equipment type and identify outliers and abnormal costs.
For inspection results of specific machinery, cite the company's maintenance procedures and determine OK/NG with a basis (RAG).
Identify instances where spare parts inventory falls below safety stock levels and create an order proposal for long-lead-time parts.
Reduce Work Time by Up to 95%
These analyses and report creations traditionally required the person in charge to go through steps like 'search → filter → aggregate → graph → consider.' By having EMLink Intelligence autonomously execute these series of tasks, our verification shows a reduction in work time of up to 95%. This allows personnel to focus on 'decision-making' tasks rather than 'creation' tasks.
What is EMLink?
'EMLink,' an Asset Management Cloud, is an equipment management service for plants and factories. Aimed at cost reduction, resource efficiency, and strengthening management systems, it is being introduced nationwide, primarily to process manufacturing industries such as chemicals, materials, pharmaceuticals, and food, as well as infrastructure operators. As a 'platform-type EAM for manufacturing sites' that connects site data with management data, it can be introduced starting from 120,000 yen per month per factory (50 users).
About Setsubi Hozon Sogo Kenkyusho Co., Ltd.
EML is a maintenance tech company that provides the asset management cloud 'EMLink' for plants, factories, and infrastructure. With the mission 'to support social and industrial infrastructure from behind the scenes,' since its establishment, it has provided 'EMLink' to over 100 leading companies in sectors such as oil, chemicals, automotive, steel, power infrastructure, and local governments.
Company Overview
Company Name: Setsubi Hozon Sogo Kenkyusho Co., Ltd.
Established: September 2022
Representative: Shogo Aihara, Representative Director & CEO
Headquarters: 3-18-16 Megurohoncho, Meguro-ku, Tokyo
Business Description: Development and sales of the asset management system 'EMLink' for factories and infrastructure, and related consulting.
Website: https://em-labo.co.jp/
FAQ
What specific tasks can the AI agent in EMLink Intelligence perform?
By simply communicating a goal, it autonomously executes a series of tasks including data search, aggregation, graphing, analysis, and report creation, allowing personnel to focus on final decision-making.
What are the benefits of building a custom RAG for my company?
By incorporating your company's manuals and glossaries, you can obtain accurate 'unique solutions' based on your company's standards, rather than general theories. This also clarifies the basis for the answers.
To what extent does this update improve the efficiency of maintenance operations?
According to our verification, the autonomous execution by the AI agent is expected to reduce maintenance work time by up to 95%. This allows personnel to focus on higher value-added tasks.
In which industries can EMLink Intelligence be utilized?
It is expected to be utilized in a wide range of industrial fields, including manufacturing industries with factories and infrastructure, chemical, materials, pharmaceutical, food industries, and power infrastructure operators.
Are there any considerations when considering the implementation of EMLink Intelligence?
Integration with existing systems and training for employees to improve AI utilization skills may be necessary. For details, please contact Setsubi Hozon Sogo Kenkyusho Co., Ltd.