"AI Will Take Over PMO Jobs."
Findy Inc. announced the launch of "Findy Context," a new product designed to reduce "decision-making costs" for managers in the AI era, aiming to improve development organization productivity and support AI-driven development.
📋 Article Processing Timeline
- 📰 Published: April 7, 2026 at 19:00
- 🔍 Collected: April 7, 2026 at 10:31
- 🤖 AI Analyzed: April 20, 2026 at 23:51 (325h 19m after Collected)
Engineers Platform Inc. (Headquarters: Shinagawa-ku, Tokyo; Representative Director: Yuichiro Yamada; hereinafter referred to as "our company") is pleased to announce the launch of its new product, "Findy Context," aimed at managers, Engineering Managers (EMs), and Product Managers (PdMs) who are responsible for development, project, and business decisions. This product is designed to reduce the "decision-making costs" that are increasing in the AI era. This product is the third in our company's new AI business, launched since January 2026, and is positioned in the area of development organization improvement and AI-driven development support. Moving forward, we aim to establish our position as an "Engineering Platform for the AI Era" by continuously creating products and services that support the resolution of all development and organizational challenges in the AI era, based on the knowledge accumulated through our company's internal AI utilization.
## Overview: The "Explosion of Coordination Costs" Behind AI-Driven Development
In an era where AI accelerates software development speed, many development organizations face new challenges. The faster development becomes, the more "decision-making" tasks such as checking dependencies, investigating impacts, and confirming progress proportionally increase. Currently, these tasks are conducted through conversations in chat applications like Slack, meetings, and ticket comments on platforms like Jira, consuming a significant amount of time for Tech Leads, EMs (Engineering Managers), and PdMs for investigation. Furthermore, having AI write code without understanding past context or "organizational context" increases the risk of technical debt with unclear origins. "Findy Context" is a product that continuously retains the context of requests and confirmations arising from conversations and transforms them into reusable "assets."
## "Findy Context" Solutions to Save Field Fatigue (Before / After)
"Findy Context" provides a mechanism for "context" to naturally accumulate within the business workflow.
* **【Before】A "Consumption" Flow of Repeated Investigations and Confirmations**
Costs are incurred from continuously searching Slack or Jira with questions like, "Does anyone remember the context from that time?" Even if logs exist, if the context is broken, they are merely noise, and Tech Leads and PdMs were caught in a cycle of the same investigations and confirmations daily.
* **【After】AI Connects Context, Reducing Coordination Costs by Simply "Selecting"**
When you query "Findy Context" for an Issue created on GitHub or other platforms, it presents past similar Pull Requests (PRs) or related Slack discussions with high-quality data linking "questions, materials, and conclusions." By retaining the context generated from conversations, it reduces repetitive investigations and confirmations, significantly cutting down organizational costs associated with team coordination.
## Empowering EM and PdM for Lean Operations and Cultivating Future "Organization-Specific AI"
This mechanism enables organizational management that maintains high development capabilities with fewer personnel. Additionally, the "Gold Data (sets of decisions and materials)" accumulated in "Findy Context" will serve as the best teacher data (Few-Shot examples) for future autonomous AI agents. By accumulating the current decision-making processes, it...
## Overview: The "Explosion of Coordination Costs" Behind AI-Driven Development
In an era where AI accelerates software development speed, many development organizations face new challenges. The faster development becomes, the more "decision-making" tasks such as checking dependencies, investigating impacts, and confirming progress proportionally increase. Currently, these tasks are conducted through conversations in chat applications like Slack, meetings, and ticket comments on platforms like Jira, consuming a significant amount of time for Tech Leads, EMs (Engineering Managers), and PdMs for investigation. Furthermore, having AI write code without understanding past context or "organizational context" increases the risk of technical debt with unclear origins. "Findy Context" is a product that continuously retains the context of requests and confirmations arising from conversations and transforms them into reusable "assets."
## "Findy Context" Solutions to Save Field Fatigue (Before / After)
"Findy Context" provides a mechanism for "context" to naturally accumulate within the business workflow.
* **【Before】A "Consumption" Flow of Repeated Investigations and Confirmations**
Costs are incurred from continuously searching Slack or Jira with questions like, "Does anyone remember the context from that time?" Even if logs exist, if the context is broken, they are merely noise, and Tech Leads and PdMs were caught in a cycle of the same investigations and confirmations daily.
* **【After】AI Connects Context, Reducing Coordination Costs by Simply "Selecting"**
When you query "Findy Context" for an Issue created on GitHub or other platforms, it presents past similar Pull Requests (PRs) or related Slack discussions with high-quality data linking "questions, materials, and conclusions." By retaining the context generated from conversations, it reduces repetitive investigations and confirmations, significantly cutting down organizational costs associated with team coordination.
## Empowering EM and PdM for Lean Operations and Cultivating Future "Organization-Specific AI"
This mechanism enables organizational management that maintains high development capabilities with fewer personnel. Additionally, the "Gold Data (sets of decisions and materials)" accumulated in "Findy Context" will serve as the best teacher data (Few-Shot examples) for future autonomous AI agents. By accumulating the current decision-making processes, it...