【For Post-Golden Week Turnover Prevention】Visualize True Sentiments with Zero Response Burden. AI Organizational Survey "SakiYomu" Beta Version Launched.
SakiYomu Inc. has launched the beta version of "AI Organizational Survey SakiYomu," an AI-powered system that automatically detects signs of employee turnover from communication data. It visualizes true sentiments with zero response burden and proposes concrete improvement actions, supporting measures against increased turnover risks after Golden Week.
📋 Article Processing Timeline
- 📰 Published: May 12, 2026 at 20:10
- 🔍 Collected: May 12, 2026 at 11:31
- 🤖 AI Analyzed: May 13, 2026 at 09:34 (22h 2m after Collected)
SakiYomu Inc. (Headquarters: Meguro-ku, Tokyo; Representative Director: Rina Kotaki) has officially released "SakiYomu," a next-generation AI for predicting employee turnover that automatically detects changes in employee conditions from information in daily communication tools.
While eliminating the "employee response burden," a common pain point of traditional questionnaire-based engagement surveys, the AI not only provides alert notifications but also proposes "concrete improvement actions."
This solves the problem of "not knowing what to discuss in interviews" for turnover prevention and accelerates human capital management.
"Seeing True Sentiments Without Asking" AI Organizational Survey "SakiYomu" Beta Version Launched
■ Why is turnover prevention based on behavioral data necessary now?
May, immediately after the Golden Week holidays, is a period when a decrease in motivation due to "May sickness" and a sharp increase in "sudden resignation consultations" due to career reconsideration during the holidays are common. However, monthly questionnaire-based surveys adopted by many companies often fail to capture rapid changes in sentiment, leading to frequent situations where "it's too late when noticed."
SakiYomu solves this "time lag until discovery" and "difficulty in seeing true sentiments" by detecting signs of distress in real-time from daily communication data.
■ The enormous cost and chain reaction of organizational collapse caused by one employee's departure
In modern times of a declining workforce, the departure of an ace employee causes more management damage than just a labor shortage.
Economic loss: The cost of loss when one employee leaves is generally said to be 1.0 to 2.0 times their annual salary (approximately 5 million to 15 million yen). This includes not only new recruitment costs but also training man-hours, loss of know-how, and opportunity loss due to decreased productivity.
Organizational risk: "Ace-level departures" reduce the psychological safety and sense of belonging among remaining members, carrying the risk of triggering a chain of resignations.
Recruitment difficulty: The effort and cost to fill vacancies after departures are increasing year by year, making investment in "retention management" to protect existing talent the most efficient management strategy.
■ Three features of "SakiYomu" - "Seeing True Sentiments Without Asking"
Zero-burden condition measurement
AI automatically analyzes communication data on Slack and Teams, which are used daily. Employees do not need to answer questionnaires or log into an app, eliminating any burden.
Visualization of "6-indicator engagement score" by AI
The organization's state is quantified from multiple angles using 6 indicators: "Vitality, Attachment, Mind & Body, Job Content, Human Relationships, Workload." This allows for an objective understanding of conditions, independent of subjective responses.
Proposal of "improvement actions" and "interview scenarios" by AI
Beyond detecting changes in scores, the system proposes "who, when, and what to talk about" with urgency levels. Since AI advises on specific ways to initiate interviews, accurate follow-up is possible regardless of the manager's experience.
Example of a personal score page, also showing engagement trends and recommended actions.
■ Consideration for privacy
"SakiYomu" does not save message bodies at all.
The analysis targets only temporary data, which is discarded immediately after AI analysis, and metadata extracted from it, such as "reply speed," "activity time zone," "reaction rate," and "sentiment score (frequency of exclamation marks and apology words)." While strictly adhering to employee privacy, it safely visualizes only the organization's health status.
【Campaign】Free trial for the first 3 companies only
To commemorate the beta version release, we are offering a free trial slot (limited to the first 3 companies) where all functions can be experienced. If you are interested, please contact us via the email or website below.
We look forward to hearing from companies facing challenges such as difficulty in detecting organizational changes with traditional surveys, not knowing how to respond after an alert, or wanting to implement efficient turnover measures.
■ Company Overview
Company Name: SakiYomu Inc.
Website URL: https://service.sakiyomu.com/
Location: Meguro-ku, Tokyo
Representative Director: Rina Kotaki
Contact: info@sakiyomu.com
Note: https://note.com/sakiyomu_kotaki
■ Product Overview
Service Name: AI Organizational Survey "SakiYomu"
Implementation Method: Slack/Teams integration (setup completed in as little as 30 minutes)
Main Functions: Condition scoring with 6 indicators, AI-driven turnover prediction alerts, personalized care action proposals.
While eliminating the "employee response burden," a common pain point of traditional questionnaire-based engagement surveys, the AI not only provides alert notifications but also proposes "concrete improvement actions."
This solves the problem of "not knowing what to discuss in interviews" for turnover prevention and accelerates human capital management.
"Seeing True Sentiments Without Asking" AI Organizational Survey "SakiYomu" Beta Version Launched
■ Why is turnover prevention based on behavioral data necessary now?
May, immediately after the Golden Week holidays, is a period when a decrease in motivation due to "May sickness" and a sharp increase in "sudden resignation consultations" due to career reconsideration during the holidays are common. However, monthly questionnaire-based surveys adopted by many companies often fail to capture rapid changes in sentiment, leading to frequent situations where "it's too late when noticed."
SakiYomu solves this "time lag until discovery" and "difficulty in seeing true sentiments" by detecting signs of distress in real-time from daily communication data.
■ The enormous cost and chain reaction of organizational collapse caused by one employee's departure
In modern times of a declining workforce, the departure of an ace employee causes more management damage than just a labor shortage.
Economic loss: The cost of loss when one employee leaves is generally said to be 1.0 to 2.0 times their annual salary (approximately 5 million to 15 million yen). This includes not only new recruitment costs but also training man-hours, loss of know-how, and opportunity loss due to decreased productivity.
Organizational risk: "Ace-level departures" reduce the psychological safety and sense of belonging among remaining members, carrying the risk of triggering a chain of resignations.
Recruitment difficulty: The effort and cost to fill vacancies after departures are increasing year by year, making investment in "retention management" to protect existing talent the most efficient management strategy.
■ Three features of "SakiYomu" - "Seeing True Sentiments Without Asking"
Zero-burden condition measurement
AI automatically analyzes communication data on Slack and Teams, which are used daily. Employees do not need to answer questionnaires or log into an app, eliminating any burden.
Visualization of "6-indicator engagement score" by AI
The organization's state is quantified from multiple angles using 6 indicators: "Vitality, Attachment, Mind & Body, Job Content, Human Relationships, Workload." This allows for an objective understanding of conditions, independent of subjective responses.
Proposal of "improvement actions" and "interview scenarios" by AI
Beyond detecting changes in scores, the system proposes "who, when, and what to talk about" with urgency levels. Since AI advises on specific ways to initiate interviews, accurate follow-up is possible regardless of the manager's experience.
Example of a personal score page, also showing engagement trends and recommended actions.
■ Consideration for privacy
"SakiYomu" does not save message bodies at all.
The analysis targets only temporary data, which is discarded immediately after AI analysis, and metadata extracted from it, such as "reply speed," "activity time zone," "reaction rate," and "sentiment score (frequency of exclamation marks and apology words)." While strictly adhering to employee privacy, it safely visualizes only the organization's health status.
【Campaign】Free trial for the first 3 companies only
To commemorate the beta version release, we are offering a free trial slot (limited to the first 3 companies) where all functions can be experienced. If you are interested, please contact us via the email or website below.
We look forward to hearing from companies facing challenges such as difficulty in detecting organizational changes with traditional surveys, not knowing how to respond after an alert, or wanting to implement efficient turnover measures.
■ Company Overview
Company Name: SakiYomu Inc.
Website URL: https://service.sakiyomu.com/
Location: Meguro-ku, Tokyo
Representative Director: Rina Kotaki
Contact: info@sakiyomu.com
Note: https://note.com/sakiyomu_kotaki
■ Product Overview
Service Name: AI Organizational Survey "SakiYomu"
Implementation Method: Slack/Teams integration (setup completed in as little as 30 minutes)
Main Functions: Condition scoring with 6 indicators, AI-driven turnover prediction alerts, personalized care action proposals.