About

Twenty years of .NET work across enterprise integrations, legacy migrations and custom applications. The work has concentrated in two industries: healthcare and law. The technical problems vary, but the underlying patterns — disconnected systems, data quality gaps, outdated platforms that still run something critical — repeat across both.

Healthcare engagements have included data visualization platforms for federally-mandated registries, interactive clinical reporting tools and patient-facing outcome interfaces. These applications present complex statistical data to non-clinical audiences and require careful handling of confidence intervals, cohort sizes and display logic — the same underlying data needs different treatment depending on whether the end user is a clinician reviewing center performance or a patient evaluating treatment options.

Legal engagements have included AmLaw 200 firms, document management integrations and full-text search platforms built for legal research workflows. The constraints in that domain are consistent: strict confidentiality requirements, complex entity relationships between clients, matters and documents, and users operating under time pressure who need tools that work without friction.

Working across multiple organizations in the same industry builds a kind of pattern recognition that's hard to get any other way. The same integration problem that shows up at one law firm has usually shown up at a few others in a slightly different form. That means less time relitigating decisions that have clear answers and more time on the problems that are actually specific to your situation.

Parka Software is me. When you reach out, you're talking to the engineer who will do the work. The person who scopes the engagement is the person who executes it. For projects that need more capacity, I bring in engineers I've worked with directly — you know who is on the project and what they are responsible for.

AI tooling is part of how the work gets done. It compresses timelines and changes what's achievable within a given scope — work that used to take weeks can often be done faster. That efficiency doesn't change the judgment layer: the architecture decisions, the domain knowledge, the understanding of what a specific client's environment will actually support. It just means more of the work gets done.

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