What We Do
We provide bespoke technical services at the intersection of environmental science, data engineering, and applied machine learning.
Data Science & Statistical Modeling
We design and execute rigorous statistical and machine learning workflows tailored to environmental, ecological, and agricultural data. Our analyses meet standards required for peer-reviewed publication.
- Geospatial and remote sensing analysis (land cover, climate analogs, species distribution)
- Machine learning: random forest models, gradient boosting, neural networks (CNNs, RNNs), time-series models
- Ecological and agricultural statistical analysis (mixed models, GAMs, dimensionality reduction)
- Reproducible R and Python workflows; HPC pipeline development
- Data integration from diverse sources (satellite imagery, field data, administrative records)
Software Engineering & MLOps
We build the infrastructure that makes data science work in production — from automated data ingestion to deployed, monitored ML models accessible through web interfaces.
- Automated ETL and data pipeline architecture
- Cloud infrastructure on AWS and GCP, deployed with Infrastructure-as-Code (Pulumi)
- ML model deployment, automated retraining, and version management
- R Shiny application development with AWS-hosted backend workflows
- Web application development (Svelte, FastAPI) for presenting ML outputs to end users
- DevOps: Docker/Singularity, CI/CD, GitHub Actions, DVC, automated testing
Scientific Research & R&D
We provide analytical services that satisfy the rigorous standards of academic and federal research environments, including support for grant-funded projects and formal vendor procurement processes.
- Environmental, ecological, and agricultural R&D
- Review and synthesis of scientific literature
- Grant writing support and funded R&D collaboration
- Peer-reviewed publication support
- University and agency vendor onboarding (experienced with university procurement processes)