StoreFlow: Scheduling & Attendance

What the company appears to be: StoreFlow: Scheduling & Attendance is a mobile-first Progressive Web App (PWA) designed for small retail teams. The author states it unifies employee scheduling, Wi-Fi and QR attendance, shift swaps, payroll support, store announcements, and push reminders into one workflow.

What changed: The project was built during the OpenAI 2026 hackathon using AI coding assistants like Codex and GPT-5.6. It emerged from the author's own operational pain points in running a retail store in Seoul.

Single most important open question: Is there evidence of real-world usage or customer feedback beyond the author's personal store? The description states the app is connected to a real operational problem, but does not provide data on adoption, retention, or revenue.

The analysis is based entirely on self-reported information from the project description. No independent verification exists for any claims about traction, customers, revenue, or actual deployment beyond the author's own store.

The description states that StoreFlow is a mobile-first workforce management app for small retail teams. It is described as a Progressive Web App (PWA) built with React, TypeScript, Vite, Supabase, PostgreSQL, Edge Functions, and deployed on Vercel.

Key features mentioned by the author:

• Employee scheduling
• Wi-Fi-first attendance check-in
• Rotating QR backup verification
• Shift swap requests
• Payroll support
• Store announcements
• Push reminders

The app supports manager and employee roles with specific functionalities for each. The system includes schedule management, attendance records, payroll-related data review, and store board posts.

Claim: StoreFlow is a practical tool that can be used in small retail stores, not just a demo.

Inference: The author positions the product as solving real operational problems from their own store experience. They state it addresses issues like "paper schedules, spreadsheets, chat messages, attendance notes, and manual payroll calculations."

Claim: The app is built for non-technical staff while still providing managers with necessary tools.

Inference: The author claims to have kept the app simple for non-technical users while maintaining functionality for managers.

Evolution: The positioning appears to have evolved from a personal solution (running their own store) to a potential product for other small retail teams, as evidenced by the "What's next" section mentioning improvements for "other small retail teams."

Claim: StoreFlow targets small retail teams.

Inference: The author explicitly states this is a workforce management tool for small retail teams. They also mention that their own store is in Seoul, suggesting a geographic focus.

Claim: The target includes both managers and employees of small retail operations.

Inference: The app supports both manager and employee roles with distinct functionalities for each group.

ICP: Based on the description, the ICP appears to be:

• Small retail store owners or managers
• Staff working in small retail environments (10-50 employees)
• Non-technical users who need simple workforce management tools

Claim: StoreFlow provides workforce management services for small retail teams.

Inference: The author describes the app as a unified platform for scheduling, attendance, payroll support, and announcements.

No pricing or business model evidence provided: The description does not contain any information about:

• Revenue streams
• Pricing tiers
• Subscription models
• Freemium structure
• Transaction fees
• Licensing costs

Technology stack: React, TypeScript, Vite, Supabase, PostgreSQL, Edge Functions, PWA technology.

Deployment: Deployed on Vercel with Supabase handling authentication, database access, role-based permissions, and backend functions.

AI integration: The author states they used Codex and GPT-5.6 as coding partners during development, helping with architecture design, code generation, debugging, UI improvements, and database logic.

Delivery approach: Built during a hackathon (OpenAI 2026) using AI tools while running the store full-time.

Not evidenced: The description does not contain any information about:

• Customer adoption or usage
• Revenue generation
• User base size
• Retention rates
• Product maturity metrics
• Market traction indicators

Inference: The author states they built and improved the app while running their store full-time, suggesting some level of real-world testing but no quantified results.

Not evidenced: No information provided about:

• Direct competitors
• Market size or positioning
• Competitive advantages
• Industry benchmarks
• Market gaps being addressed

Inference: The author positions StoreFlow as addressing the "extra administrative work" and inefficiencies of using multiple tools (paper schedules, spreadsheets, chat messages) for workforce management in small retail.

Risk 1: Lack of independent verification

• All evidence is self-reported and unverified
• No third-party validation of claims or product functionality

Risk 2: Limited real-world usage data

• The only confirmed usage appears to be the author's own store
• No evidence of broader adoption or customer feedback

Risk 3: Unclear monetization strategy

• No pricing, revenue model, or business plan details provided
• Unclear path to profitability

Open Question 1: Single-person development team

• Team size listed as 1 person (YERI KIM)
• No evidence of additional team members or support structure

Open Question 2: Hackathon origin

• Built during a hackathon suggests early-stage product
• No indication of post-hackathon development or scaling efforts

1. What specific operational problems in your store did you solve with StoreFlow?
2. How many stores are currently using the system beyond your own?
3. What is your plan for monetization and revenue generation?
4. How do you intend to scale from one store to multiple stores or customers?
5. What are the key technical challenges that remain unresolved?
6. How do you plan to handle data privacy and security compliance?
7. What feedback have you received from users beyond yourself?
8. What is your timeline for product development and market expansion?