Non-Developer Deploys Production Golf App Using AI-Assisted Development

Gregor Suttie, an Azure MVP and cloud specialist, recently completed GolfScorer – a production-ready golf scoring application deployed on Azure. Unlike traditional development paths, Suttie collaborated with AI assistants throughout the entire process, from initial concept to Azure deployment. This case study reveals significant shifts in how cloud applications can be developed when combining domain expertise with AI co-development.

The Development Process

Suttie started with plain English descriptions of his requirements: hole-by-hole scoring, course management, and performance analytics. Through iterative dialogue with AI, he defined a technical architecture using .NET 10 with Blazor Server for the frontend, Entity Framework Core for data access, and ASP.NET Core Identity for authentication. The persistence layer uses SQL Server, hosted entirely on Azure.

Key development phases included:

• Data modeling: Deciding between denormalized scoring storage versus real-time calculations
• UI design: Implementing an "Augusta Dark" theme using Playfair Display fonts and golf-inspired color palettes
• Analytics: Building statistical dashboards tracking putting accuracy, green-in-regulation percentages, and hole-specific performance

Azure Deployment Architecture

The deployment pipeline showcases Azure's low-barrier cloud adoption. Suttie used AI-generated Bicep templates for infrastructure-as-code and PowerShell scripts for CI/CD workflows. The application runs on Azure App Services with integrated SQL Database, demonstrating a cost-effective PaaS approach. Explore the live application here.

Comparative Advantage

Traditional application development would require months of specialized .NET expertise. By contrast, Suttie's AI-assisted approach delivered comparable results in significantly less time through:

1. Reduced coding overhead: AI handled boilerplate Entity Framework migrations and Blazor component generation
2. Cloud optimization: Automated Bicep scripts configured optimal Azure resource tiers
3. Design-system implementation: Themed UI components were generated against specifications

Strategic Implications

This project signals a broader shift in cloud development economics:

• Democratized development: Domain experts can now build tailored solutions without deep coding expertise
• Accelerated prototyping: AI collaboration compresses design-to-deployment cycles
• Cloud adoption enabler: Non-developers can directly implement Azure solutions using natural language

Suttie notes: "You don't need to be a developer to build software anymore, but you must think systematically about data relationships and user flows." The complete source code is available on GitHub for technical review.

For organizations, this approach enables faster creation of niche business applications without expanding developer teams. The model proves particularly valuable for custom internal tools where commercial SaaS solutions often over-serve requirements while under-delivering on specific workflows. As AI development tools mature, we anticipate more non-technical domain experts shipping production applications directly to cloud platforms.