retAIl – Hackathon README

1. Market and Customer Analysis

1.1 Target Segment

• Audience: Business-to-business (B2B) retailers
• Scale: Mid-market chains and enterprise-level operations
• Geographic Scope: International

Customer profile includes retailers with inefficient product placement, missed revenue opportunities, and limited analytical capabilities. Current practices are manual and reactive, with minimal integration of data science. Procurement typically follows enterprise contracting and SaaS subscription models.

1.2 Market Context

• Existing tools are manual or partially automated, lacking measurement of layout impact on margins.
• There is no clear link between product placement and profitability in current solutions.
• Market trends include increased digitalization, adoption of AI, and a growing focus on revenue optimization.

2. Product Definition

2.1 Problem Statement

Retailers often manage product layouts manually, influenced by supplier negotiations or staff intuition. This results in suboptimal placement and up to 30% loss in potential sales revenue.

2.2 Value Proposition

retAIl is a web-based application that uses AI to optimize product placement interactively. It enables retailers to increase revenue through data-driven layout decisions.

Key benefits:

• Up to 30% increase in sales revenue
• Real-time optimization using machine learning
• Interactive planogram and section views
• Intuitive drag-and-drop interface requiring no training

3. Core Features and Scope

3.1 Minimum Viable Product (MVP)

• Planogram interface with three store sections
• Section-level zoom and interaction
• Manual drag-and-drop product arrangement
• Real-time metric updates based on layout changes
• One-click AI optimization for layout recommendations

3.2 Exclusions

• Mobile application
• Loyalty program integration (reserved for future phases)

3.3 User Workflow

Planogram → Section View → Product Arrangement → Metric Feedback → AI Optimization

3.4 Integration Roadmap

Future integration with retail POS and ERP systems.

4. Technical Architecture

4.1 Platform

Web-based application.

4.2 Technology Stack

• Frontend: React, Next.js, TypeScript, Shadcn UI
• Backend: Python with FastAPI
• Database: PostgreSQL

4.3 Scalability and Security

• Cloud-native architecture
• GDPR compliance
• Role-based access control

5. Data Requirements

5.1 Data Sources

• Historical sales data
• In-store traffic heatmaps
• Product metadata and margin information

5.2 Integration Targets

• POS, ERP, and CRM systems (future roadmap)

5.3 Analytics Capabilities

• Placement effectiveness
• Revenue impact analysis

5.4 Privacy and Retention

• GDPR-compliant data handling
• Anonymized datasets
• 12-month retention policy

6. Infrastructure

• Hosting via Railway with Git-based autodeployment
• Integration with cloud analytics APIs and visualization libraries
• Performance optimized for mid-market and enterprise scale
• Daily backups and disaster recovery protocols

7. Development Roadmap

1. Hackathon MVP: Planogram, drag-and-drop, AI optimization
2. Pilot with small retailers to validate ML impact
3. Beta release with integrations and dashboards
4. Production SaaS: Multi-tenant architecture and analytics suite
5. Expansion: Enterprise features, predictive analytics, global deployment

8. Business Model

8.1 Revenue Strategy

• SaaS subscription model

9. Go-to-Market Strategy

• Sales Channels: Direct B2B, SaaS marketplaces
• Marketing: Case studies, live demos at retail conferences
• Acquisition: Targeted outreach to retail chains
• Strategic Partnerships: POS and ERP vendors
• Launch Timeline: Hackathon → Pilot → Beta (6 months) → Production