[For Post-Golden Week Turnover Prevention] Visualize True Sentiments with Zero Response Burden. AI Organizational Survey "SakiYomu" Beta Version Now Available.
SakiYomu Inc. has launched the beta version of "SakiYomu," an AI-powered organizational survey that detects signs of employee turnover from communication data, offering a new HR tech solution.
📋 Article Processing Timeline
- 📰 Published: May 12, 2026 at 20:10
- 🔍 Collected: May 12, 2026 at 11:31
- 🤖 AI Analyzed: May 13, 2026 at 09:42 (22h 10m 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.
It eliminates the "employee response burden," a common problem with traditional questionnaire-based engagement surveys, and goes beyond mere alert notifications by having AI propose "concrete improvement actions."
This solves the challenge of "not knowing what to talk about in interviews" for turnover prevention, accelerating human capital management.
"True sentiments are visible because we don't ask" - AI Organizational Survey "SakiYomu" Beta Version Now Available
■ Why is turnover prevention based on behavioral data necessary now?
May, after the Golden Week holidays, is a period when "May sickness" (a decline in motivation) and "sudden resignation consultations" due to reconsidering careers during the holidays rapidly increase. 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 by the time we notice."
SakiYomu solves this "time lag until discovery" and "difficulty in seeing true sentiments" by detecting early signs of distress in real-time from daily communication data.
■ The enormous cost and chain reaction of organizational collapse caused by a single employee's departure
In modern times of declining working populations, the departure of an ace employee causes more management damage than just a labor shortage.
Economic loss: The cost of one employee leaving 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.
■ "True sentiments are visible because we don't ask" - Three features of SakiYomu
Condition measurement with zero response burden
AI automatically analyzes communication data on Slack and Teams, which are used daily. Employees do not need to answer questionnaires or log into apps, eliminating any burden.
Visualization of "6-indicator engagement score" by AI
Organizational conditions are quantified from multiple angles using six indicators: "vitality, attachment, mind/body, job content, human relations, and workload." Objective conditions can be grasped without relying on subjective answers.
Proposal of "improvement actions" and "interview scenarios" by AI
It not only detects changes in scores but also 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 content at all.
The analysis targets only temporary data that is discarded immediately after AI analysis, and metadata extracted from it, such as "reply speed," "activity hours," "reaction rate," and "emotional 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
To commemorate the beta release, a free trial slot (limited to the first 3 companies) is available to experience all features. If you wish to participate, please contact us via email or website below.
We welcome inquiries 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 prevention 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 Features: Condition scoring with 6 indicators, AI-powered turnover prediction alerts, personalized care action proposals.
It eliminates the "employee response burden," a common problem with traditional questionnaire-based engagement surveys, and goes beyond mere alert notifications by having AI propose "concrete improvement actions."
This solves the challenge of "not knowing what to talk about in interviews" for turnover prevention, accelerating human capital management.
"True sentiments are visible because we don't ask" - AI Organizational Survey "SakiYomu" Beta Version Now Available
■ Why is turnover prevention based on behavioral data necessary now?
May, after the Golden Week holidays, is a period when "May sickness" (a decline in motivation) and "sudden resignation consultations" due to reconsidering careers during the holidays rapidly increase. 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 by the time we notice."
SakiYomu solves this "time lag until discovery" and "difficulty in seeing true sentiments" by detecting early signs of distress in real-time from daily communication data.
■ The enormous cost and chain reaction of organizational collapse caused by a single employee's departure
In modern times of declining working populations, the departure of an ace employee causes more management damage than just a labor shortage.
Economic loss: The cost of one employee leaving 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.
■ "True sentiments are visible because we don't ask" - Three features of SakiYomu
Condition measurement with zero response burden
AI automatically analyzes communication data on Slack and Teams, which are used daily. Employees do not need to answer questionnaires or log into apps, eliminating any burden.
Visualization of "6-indicator engagement score" by AI
Organizational conditions are quantified from multiple angles using six indicators: "vitality, attachment, mind/body, job content, human relations, and workload." Objective conditions can be grasped without relying on subjective answers.
Proposal of "improvement actions" and "interview scenarios" by AI
It not only detects changes in scores but also 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 content at all.
The analysis targets only temporary data that is discarded immediately after AI analysis, and metadata extracted from it, such as "reply speed," "activity hours," "reaction rate," and "emotional 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
To commemorate the beta release, a free trial slot (limited to the first 3 companies) is available to experience all features. If you wish to participate, please contact us via email or website below.
We welcome inquiries 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 prevention 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 Features: Condition scoring with 6 indicators, AI-powered turnover prediction alerts, personalized care action proposals.