Advantages of Custom Software Development: A Technical Decision Framework

Keyhole Software All Industries, Articles, Keyhole Perspectives 1 Comment

The build-versus-buy decision represents one of the most consequential technology choices facing mid-size to enterprise organizations. When existing systems constrain operations, internal teams lack bandwidth, or off-the-shelf platforms cannot accommodate complex workflows, technical leaders evaluate custom software development against commercial alternatives.

Drawing on industry data indicating that 67% of failed software implementations stem from incorrect build versus buy decisions¹, this guide examines eight key advantages of custom software development through a technical and financial decision framework designed for engineering leaders, architects, and executives evaluating when custom solutions outperform commercial alternatives.

What this guide covers:

Scalability advantages and architectural control that eliminate vendor pricing constraints…

AMC Theatres Project for API Modernization and Next.js

Case Study: Modernizing AMC Theatres’ Digital Commerce Ecosystem

Lauren Fournier Bogner .NET, Application Enhancement, Azure, Case Study, DevOps, Flutter, JavaScript, Modernization, Platform & Infrastructure, Retail & eCommerce, TypeScript, Xamarin

Case Study: See how Keyhole Software helped AMC Theatres modernize its digital commerce ecosystem with .NET and API modernization, a Next.js website rebuild, Azure cloud migration, and performance improvements across web and mobile applications.

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Keyhole Software helps SaaS and technology organizations modernize platforms, scale cloud-native architectures, and build intelligent product features with 100% U.S.-based senior engineers. Our consultants accelerate roadmaps, improve performance, and deliver reliable, enterprise-grade software solutions.

Deploying ML Models to Edge Devices with TensorFlow Lite and WebAssembly

Zach Gardner Keyhole, Videos Leave a Comment

Artificial intelligence is no longer limited to large data centers and high-powered servers. Today, more organizations are deploying ML models to edge devices such as smartphones, tablets, and IoT hardware. These devices work in environments where connectivity is not guaranteed and computing resources are restricted. By running models directly on edge hardware, businesses reduce latency, enable offline functionality, and improve …