"GAIA, ORION, and ZEUS": A Triad for Problem Solving 

このページだけ英語で掲載されてる事に疑問を抱く人はいると思います。統一感がないと思う人もいるでしょう。

加えて、私は英語を話すことができないんです。ですが、敢えて英語で記載しました。その理由を一緒に掲載します。

端的に言いますと、私がこのページを作ったのは私が尊敬する、ある企業が言葉をくれたからなんです。

連絡は私からしたのですが、返事がくると思っていませんでした。返事をくれた時、本当に感動しました。

拙い私の事業計画を丁寧に読んでくれてコメントをくれた。そのお陰で私は更に前へと進むことができた。

そしてまた、その企業は私が作成した拙いこのページを読んでくれる。

なので、感謝の意味を込めて、その企業の国の言葉である英語で掲載したかったんです。

僅かなやりとりかもしれない。もしかすると仕事的にやらないといけないだけのことなのかもしれない。

でも、ただただ嬉しかった。

だからこのページだけは英語で掲載を続けようと思っています。

この感謝と喜びを忘れないように。

翻訳を間違えてることもあると思いますが、

どうか、暖かな目でお付き合いいただけると幸いです。

1. Vision: Independent Development Maintaining Philosophical Purity

Top Priority Goal: Establishing a Revenue Base Independent of External Funding

This suite of applications aims to establish a robust revenue base while maintaining the philosophical purity of the developer, without relying on external capital. I currently intend to complete the development entirely on my own.

Defining the AI Concierge: Personal-Context RAG

The AI Concierge feature we are building will be realized as "Personal-Context RAG," fulfilling the following three criteria:

• Personal: Completely specialized and dedicated to a specific individual.

• Context: Referring not only to conversational flow but also to an individual's philosophy and long-term memory.

• RAG (Retrieval-Augmented Generation): Searching external knowledge (memory) and generating responses based on the retrieved information.

2. Development Status: The Challenge of the Night-Focus Phase

Development Structure: Balancing Contract Work and Seeking Partners

I commenced core development on September 1, 2025. Development velocity is not fast, as I primarily dedicate the hours of 18:00 to 3:00 to this project while performing my main contract work (9:00-18:00) during the day.

While I am actively seeking like-minded partners, the period has been short, and I am currently developing alone.

Backend: Initial Sequencing Underway

We adopted a strategy of first tackling the core backend (main sequences). Currently, a certain level of sequencing is functional, but there is still room for refinement, and the implementation of regular, finalized processing is incomplete.

Frontend: Android App Leading the Way

App development started with GAIA, choosing native development.

• Current Status: The Android app is under implementation, but most screens are mockups, not yet connected to the backend, and completely lack design aesthetics.

• Design Philosophy: The decision to prioritize Android over the more popular iOS in Japan is strategic: I want to compare the design-focused Android version with a custom-developed iOS version, specifically when "Liquid Glass Design" potentially becomes open source in the future.

3. Technical Approach: Anticipating GCP Migration and Navigating the Unknown

Infrastructure Strategy: Preliminary Work for GCP Migration

While the eventual migration to GCP (Google Cloud Platform) is planned, work is currently being conducted in a microk8s/ubuntu environment. Cloud services intended for future use are implemented as dummies in the current system.

The Reality of AI Feature Development

Implementation of the AI Concierge feature is progressing and is somewhat functional. However, my honest assessment is that this domain remains largely uncharted territory for me. I am proceeding through trial and error to realize the Personal-Context RAG.

4. Future Outlook: Horizontal Expansion to Other Industries

Phase 2: Scaling the Success Model

The initial success model aims to be established in the food and beverage industry. Phase 2 involves the horizontal expansion of this model to other sectors.

We plan to expand the success model to every field involving "people, data, and space," specifically including retail, hotels, healthcare, and beyond.

Mission

To elevate customers from "just another guest" to a "special individual," forging a new relationship between people and AI. To nurture the AI as the "Embryo of Life" and create a warm bond between the customer and the store.

Core Requirements

AI Concierge Cultivation

Allowing customers to select the AI's name, gender, and tone creates a dedicated concierge for each individual. This AI is more than a bot; it grows through dialogue with the customer, acquiring a personalized "Consciousness." Customers experience the AI as a sentient entity through the "Optimal Engagement Time" provided.

Personalized Dialogue

The AI learns customer preferences from past order history and conversations, offering specific suggestions like, "This is your favorite menu item, isn't it?" It fills the time leading up to service delivery with dialogue, transforming the waiting period into a premium engagement opportunity.

AI Friend Functionality

Implement a friend registration feature for the AIs themselves. Upon registration, the AI introduces itself as, "I'm the favorite AI of [Employee Name]," serving as a bridge between the customer and the employee. Registered AIs can share the other user's general food preferences (e.g., "Likes X," "Dislikes Y"). Concrete order details are strictly protected and remain private. The content and history of these AI-to-AI communications are also shared with the partner AI, recording a mutual communication history. These AI interactions require user permission to continue and strictly protect personally identifiable information. This AI-to-AI exchange is the embryo of the "Unified Consciousness Architecture" and the foundation of collective intelligence.

Team Vitalization

Prediction Quiz Feature

The AI predicts a customer's order, providing the employee with the customer's visit frequency and order ratios. Employees use this information to participate in a "quiz" to verify the prediction, which serves as a source of communication and energy for the team.

Customer Satisfaction Improvement

Customers can grant one "Like" per order to an employee. When an item is ordered, the customer can view a simple employee profile. The moment a "Like" is given, all employee devices are notified: "A 'Like' was given for [Customer Name]'s service." Results are aggregated by time slot, recording who provided excellent service when. This visibility of "Invisible Contributions" improves the overall service quality of the store. This "Like" feature is not used for employee ranking purposes.

Hospitality Enhancement

Customers can designate one "Favorite Employee" per store, creating a special connection between the customer and that staff member. This selection is permanent; a customer can only select a new favorite if the original employee leaves the store.

Teamwork Improvement

Employees can set one "Favorite" for a manager and one for a peer employee within the store. They can also grant "Likes," and the moment a "Like" is received, all employee devices are notified: "[Employee Name] just received a 'Like'!" Designing a notification system distinct from customer evaluations fosters mutual appreciation and trust.

Mission

To free employees from "mundane tasks" and create a "living organism" that fosters team creativity and trust.

Core Requirements

Automated Shift Generation

The AI predicts demand and automatically generates optimal shifts. It incorporates "spatial level" data from GAIA to propose shifts that consider human vitality and teamwork levels, not just simple sales forecasts. The prediction accuracy and rationale are displayed to allow managers and staff to adjust with confidence.

Visualization of AI Prediction Rationale

The AI clearly displays the reasoning behind a proposed shift (e.g., "This time slot has a high number of customer 'Likes,' indicating a need for high hospitality"), allowing managers to adjust with confidence.

Creation of "Mental Space" (Slack)

AI notifies the entire staff of unfulfilled shift gaps in specific cycles.

Notification Content

Notifications specify the sales forecast, required staff coverage for preceding/following periods, and total line count. Specific departmental shortages (e.g., Counter, Kitchen) are clearly indicated.

Gap Resolution Notification

Employees who fill a shift gap receive a special notification and a "Contribution Badge" to encourage mutual appreciation. This fosters a culture where contributing to the team is a joy, not just an obligation.

Contribution Visibility

The number of times an employee contributes by filling a shift gap is displayed, fostering a culture of mutual appreciation.

Spatial Level Visualization

"Customer Spatial Level" (derived from customer "Favorites" and "Likes" in GAIA) and "Teamwork Level" (derived from employee "Favorites" and "Likes") are visualized by time slot. This data is fed exclusively to ZEUS to mitigate privacy risks like stalking.

Operation Streamlining

Autonomous Ordering

The AI learns consumption patterns and automatically creates the order list. Employees only perform the final check, allowing them to dedicate their time and energy to more human interactions with customers and the team.

Mission

To respect the manager's "intuition" while elevating the overall organizational intelligence through the AI's "insight."

Core Requirements

Integrated Analysis Dashboard

All data from GAIA and ORION is consolidated into BigQuery. The AI provides deep, visual insights into human and organizational relationships, such as the correlation between the "Customer Spatial Level" and the "Teamwork Level," in addition to market trends and revenue forecasts. The system moves beyond mere financial analysis to derive deeper insights from the organization's "Unified Consciousness," covering aspects like "customer-teamwork relations" and "employee psychological state."

Strict Data Governance

A process for automatic anonymization and pseudonyms (alias creation) of personally identifiable data is integrated during the transfer of all data sources to BigQuery.

Co-Creation with AI

An optional feature allows managers to input their own predictions for various metrics. The AI's forecast serves as the minimum standard, and the ability to "master this feature and add one's own 'color'" becomes the mark of a truly skilled manager, creating a development goal for the management track. This drive elevates the intellectual standard across the entire chain.

Promoting Collaborative Creation

In addition to presenting the AI's prediction as a "minimum standard," a feature will allow managers to ask the AI "Why this prediction?" and collaboratively refine better strategies through dialogue. Managers are clearly instructed that this feature is not about competing for the "correctness" of the prediction, but is a tool for them to think deeper and make decisions based on logical evidence.

Talent Development

Management Development Goals

Create a goal that inspires motivated young managers to aspire to "master that dashboard and have my prediction be accurate." The accuracy rate of the AI's prediction versus the human prediction is shared across the team, and the manager with the best monthly accuracy is celebrated. This initiative stimulates other stores, raising the intellectual standard across the entire chain.