AI & operations
Restaurant Operations Platform
A fully AI-backed server platform with desktop, web, and mobile surfaces for restaurant operations: ordering, inventory, reservations, and management, with intelligent import of whole restaurant datasets from mainstream platforms.
Reduced migration friction by allowing restaurants to transition existing operational data instead of rebuilding everything manually.
- Desktop · Web · Mobile
- AI-backed import & operations
- SQL Server · SQLite · AWS
Overview
A fully AI-backed server platform with desktop, web, and mobile surfaces for restaurant operations: ordering, inventory, reservations, and management, with intelligent import of whole restaurant datasets from mainstream platforms.
Business Problem
Multi-location restaurant teams need consistent operational tools, but moving from existing POS, ordering, or management systems often forces owners to re-enter menus, inventory, reservations, and reporting data from scratch.
Constraints
Fully implemented on AWS with AI-backed server logic for data import and daily operations, developed with AI coding-agent assistance under architecture, prompt, review, and acceptance-criteria control.
My Role
Architect/developer guiding server architecture, AI-backed import workflows, multi-surface product delivery, and acceptance criteria for agent-assisted implementation.
Architecture
AWS-hosted server platform with AI-backed data normalization, operational modules, SQL Server/SQLite persistence, and desktop/web/mobile application surfaces.
Technical Decisions
Built an AI-backed server platform that imports restaurant data from mainstream platforms using server-side AI logic, then populates ordering, inventory, reservation, and management workflows across desktop and mobile without manual rebuild.
Implementation
Fully implemented on AWS with AI-backed server logic for data import and daily operations, developed with AI coding-agent assistance under architecture, prompt, review, and acceptance-criteria control.
Results
Reduced migration friction by allowing restaurants to transition existing operational data instead of rebuilding everything manually.
Lessons / Tradeoffs
The public project record describes the implementation and outcomes above. Detailed alternatives, internal decision records, and retrospective findings are not published here.
Technology
AWS · AI-backed server · Data import · C# · ASP.NET · .NET MAUI · SQL Server · SQLite