I enjoy solving problems in ways that make the final solution feel obvious in hindsight.
Throughout my career, I've found that optimal solutions require more than technical expertise. They require
discovering what stakeholders need, understanding the history that shaped the current system, and seeing how it
all fits together. As the larger picture comes into focus, a coherent model emerges. The software can then be
designed to faithfully express that model while preserving the ability to refine it as understanding and needs
grow.
My approach is to deeply understand the situation before committing to a solution. Rather than treating
requirements as independent problems, I look for underlying concepts that enable multiple requirements to be
satisfied simultaneously. Those concepts become the foundation of the architecture. When the underlying structure
is right, the software becomes easier to understand, easier to extend, and better aligned with the problem it was
built to solve.
That way of thinking has guided my career. I’ve co-founded software companies, architected enterprise platforms,
and taken products from concept to commercial success. Whether working in startups or publicly traded companies,
modernizing legacy systems or building greenfield platforms, the objective has remained the same: understand the
problem well enough that the software feels like the natural solution.
Notable Achievements
Pledged (New System and Company)
Co-founded Pledged and served as Chief Architect for an institutional relationship intelligence platform
built for higher education. Led the technical vision, architecture, and development of the platform from
concept to production.
Architected the overall system, designed the data architecture, and developed the backend services and React
frontend. Established engineering standards, software architecture principles, and development practices to
support a scalable, maintainable platform.
Designed and implemented the company’s cloud architecture across AWS and Azure, established DevOps and CI/CD
practices, and built the engineering foundation needed to support long-term growth.
Championed the adoption of AI-assisted software development throughout the organization, integrating emerging
tools and workflows to improve engineering productivity, software quality, and development velocity.
Accumulus (New System and Company)
Co-founded Accumulus and led the architecture and development of a cloud-native subscription billing and
management platform built on Microsoft Azure. The platform was commercially available at the launch of Azure,
making it one of the early SaaS solutions built on Microsoft’s cloud platform.
Architected the overall system, developed core platform components, and helped build a scalable browser-based
application for subscription management and billing.
Extended the platform to mobile by designing and developing companion credit card processing applications
for iOS and Windows Phone.
Inovaware (New System and Company)
Co-founded Inovaware and co-architected PRISM, an enterprise subscription billing and management platform
deployed in customers’ own data centers. Played a leading role in the architecture and development of the
platform, contributing extensively to its design and implementation.
Developed a rich Windows desktop application that provided a comprehensive interface for managing
subscriptions and business operations.
PRISM was adopted by enterprise customers including Salesforce and j2 Global. At j2 Global, the platform
supported the company’s growth from 13,000 to 13 million customers over a ten-year period while scaling to
meet the demands of a publicly traded NASDAQ business.
GRADVISE (New System)
Designed and developed a graduation advisement system for Honolulu Community College. The software’s success
led to its adoption by every Hawaii community college before being acquired and commercialized by the vendor
providing the statewide student information system.
Elastic system monitoring (Patent)
Co-authored a granted U.S. patent for an adaptive system monitoring methodology that dynamically adjusts
monitoring detail to maximize diagnostic insight while minimizing runtime overhead.
