I'm a cannabis data analyst building practical tools for one of the messiest data environments around: cannabis retail, lab testing, inventory, menus, and compliance-adjacent workflows.
My work focuses on turning inconsistent industry data into clean, repeatable systems that people can actually use.
A C# parser for cannabis Certificates of Analysis.
The goal: take messy COA PDFs and convert them into structured, testable data.
Current focus:
- Lab-specific parsing adapters
- Product type classification
- Cannabinoid and chemistry extraction
- Batch testing across real COA fixtures
- Clean CLI output for downstream workflows
A shared cannabis math library implemented across multiple languages.
Built in:
- C#
- TypeScript
- Python
The goal: consistent cannabis calculations with shared test expectations across ecosystems.
A cannabis-focused calculator/tooling project for common retail and product math workflows.
Built to support practical calculations around cannabis products, potency, weight, and retail logic.
- Clean data pipelines
- Practical automation
- Cannabis retail intelligence
- Parser reliability
- Repeatable test coverage
- Tools that reduce manual work
- Making cannabis data less painful to work with
- C#
- .NET
- Python
- TypeScript
- SQL
- PostgreSQL
- Docker
- GitHub
- CLI tooling
- Data parsing
- CSV/JSON workflows
- Retail analytics
I'm building open-source cannabis data tools that solve real operational problems: messy lab documents, inconsistent product data, fragmented menus, and manual reporting workflows.
The industry has enough chaos. The data doesn't need to add more.