Capabilities
The point is not allegiance to a single stack. The point is using the right tools to deliver secure, scalable systems that actually move the work forward.
What this means in practice
Scalable hosting, deployment, reliability, and environment design.
Pipelines, analytics, high-volume processing, and operational reporting.
Event-driven systems, service coordination, and platform connectivity.
Responsive interfaces, workflow-heavy apps, and modern frontend delivery.
APIs and application services designed for speed, maintainability, and scale.
Secure defaults, sensible controls, and architecture that respects risk.
Technology breadth
From cloud infrastructure and web platforms to data systems and AI tooling, these are the stacks we regularly work with.
Representative projects and platform directions that show how the right mix of technologies can support real-world delivery.
A transportation analytics platform that combines a high-performance crash-data API, Snowflake-backed reporting, and a modern mapping-heavy frontend.
TForce V2 splits the system cleanly between a .NET 9 analytics API and a React 19 + Vite frontend built for geospatial exploration, filtering, and operational reporting around crash and inspection data.
An AI-assisted recipe product built around request processing, asynchronous generation, and commerce-aware application services.
QuicklyCook uses a .NET 8 backend and supporting worker pipeline to turn recipe prompts into structured outputs, persist user data, and move generation work through AWS and messaging-backed processing paths.
A telehealth-oriented application MVP covering patient intake, triage, consult workflows, payments, and pharmacy-related backend operations.
This product combines an Angular frontend with a .NET 8 API layer and healthcare-style workflow modeling for patient data, consults, payment steps, pharmacy support, and admin operations.
A draft-analysis platform that combines live Sleeper data, VORP-based valuation, cached analytics, and a modern UI for real-time decision support.
This project is built as a full-stack analysis platform: Angular 20 on the frontend, FastAPI and Python services on the backend, PostgreSQL for core fantasy data, Redis for performance-sensitive caching, and Docker-oriented deployment planning.
A full-stack crash reporting and scorecard product that combines an Angular frontend with a Snowflake- and S3-backed .NET API for ETL processing, tracking, and operational visibility.
This system pairs an Angular 18 application with a .NET 9 Web API designed to ingest state crash datasets from AWS S3, validate and process large CSV and ZIP inputs, load Snowflake tables, and surface scorecard-style reporting workflows.
A set of production-style ingestion pipelines for federal and state transportation data, built to move large files from S3 through validation, transformation, archive handling, and Snowflake loading.
Beyond the scorecard app itself, this work includes multiple ETL pipelines for state crash data, federal inspection feeds, and federal crash datasets, all built around repeatable ingestion flows, error handling, logging, and operational recovery.
Let's talk through the product, workflow, or operational challenge and map it to a practical delivery plan.
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