Case study
Improving Sprint Predictability Through Capacity-Based Planning
Improved sprint reliability by intentionally planning below full capacity, allowing teams to absorb unexpected work without jeopardizing commitments.
Lens
Building scalable web, mobile, SaaS, and AI-powered product experiences.
Case study
Improved sprint reliability by intentionally planning below full capacity, allowing teams to absorb unexpected work without jeopardizing commitments.
Case study
Converted commonly used mobile functionality into reusable components supported by documentation and a demo application, improving consistency and accelerating delivery.
Case study
Built an extensible engineering portfolio platform with a dedicated admin experience, structured case studies, and AI-assisted insights. What started as a simple personal website evolved into a knowledge system designed to continuously document, analyze, and communicate engineering growth.
Case study
Built a hybrid AI-powered CV matcher that combined privacy-focused local LLM execution with cloud-based task processing. What began as a personal productivity tool evolved into designing reliable asynchronous workflows using AWS queues and distributed workers.
Case study
Adopted AI-assisted development practices to accelerate delivery, explore new product ideas, and build practical LLM-enabled applications.
Case study
Built a lightweight Python application that replaced a Windows/Electron-based kiosk solution, enabling Linux deployments and reducing licensing costs while maintaining the same business functionality.
Case study
Improved performance, maintainability, test coverage, and release reliability for data-heavy React applications.
Case study
Designed and built an AI-powered platform for resume optimization, job matching, and report generation.
Case study
Delivered React Native applications, reusable UI libraries, mobile release workflows, and kiosk applications.
Case study
Built backend services, RESTful APIs, MongoDB migrations, and third-party integrations.