Implementation of temporal analysis (week-over-week, month-over-month and 7-day moving windows).

Automatic detection of business trends (growth, decline, stability) based on real order data.

Identification of operational patterns such as: • High and low demand days • Peak order hours • Revenue concentration in few days or products

Calculation of key metrics: • Total revenue • Average ticket • Order volume • Best-selling products

Automatic generation of business insights from operational data.

Actionable business-oriented recommendations: • Apply promotions on low-activity days • Boost highest-performing products • Optimize operations during peak hours

Reports with executive summary synthesizing business status, key findings and suggested actions.

Analysis adapted to low-volume data scenarios, including interpretation of early business stages.

Demo access: https://www.orderflow.uy | User: donvito@gmail.com | Password: 12345678

OrderFlow is a web platform designed to centralize and optimize restaurant order management, including in-house delivery operations.

Multi-tenant architecture allowing multiple restaurants to operate independently, each with their own products, orders, customers, and configurations.

Integration of multiple order channels (WhatsApp, phone and in-person) into a unified and structured data system.

Design and implementation of relational data models for managing orders, customers, products, and sales data.

Development of analytical dashboards and reporting systems to track key business metrics such as revenue, order volume, top-selling products, and customer behavior.

Implementation of an analytical layer with time-based comparisons (week-over-week, month-over-month), trend detection, and pattern recognition to support data-driven decision-making.

Automatic generation of business insights and actionable recommendations based on real operational data (e.g., demand concentration, peak hours, low-activity periods).

Identification of consumption patterns and operational inefficiencies to improve restaurant performance and decision-making.

QR-based digital menu system that captures customer behavior data and automates the order flow to the kitchen.

Real-world validation with restaurant users, including real data collection for analysis and continuous product iteration.

Designed with scalability in mind, enabling future integration with Business Intelligence tools and advanced analytics pipelines.