Keyhole Software partnered with a national manufacturing organization to modernize and automate core production and order-management workflows. What began as a targeted automation initiative has, due to measurable results and delivery quality, been extended multiple times over 2.5 years and is still ongoing. The effort replaces manual, spreadsheet-driven processes with modern applications that improve efficiency, reduce errors, and provide near …
Security Dashboard Modernization & Cloud Integration
What began as a short-term engagement evolved into a long-term partnership, with the contract extended multiple times over 2.5 years—a testament to the trust, results, and collaboration built with the client. Keyhole Software partnered with a leading national engineering consulting firm to modernize and expand its Security Dashboard application—a mission-critical platform that consolidates security data into a unified, actionable view. …
Empowering Through DevOps Enablement & Cloud-Native Transformation
Keyhole Software partnered with a national grocery distribution organization to accelerate the modernization of its legacy COBOL systems by implementing a cloud-native microservices architecture on Microsoft Azure. In an environment with limited DevOps maturity, Keyhole played a pivotal role in establishing both the technical foundation and cultural framework for scalable, developer-driven software delivery and ongoing digital transformation. Our Approach A …
Best Practices for Proposing Improvements to Your Development Team
Working within a software development team has tremendous benefits, but it also comes with its share of complications. One of those complications is inertia – the more people who get used to a process or a certain set of tools, the more difficult it can be to introduce changes. Maybe this manifests in a positive way, where repetition and familiarity …
Deploying ML Models to Edge Devices with TensorFlow Lite and WebAssembly
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 …




