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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 forests, 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, Data Engineering & Machine Learning Operations

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
  • Web application development (Svelte, FastAPI) for presenting ML outputs to end users
  • DevOps: Docker/Singularity, CI/CD, GitHub Actions, DVC, automated testing

Scientific Research & Publication

We provide analytical services that satisfy the rigorous standards of academic and federal research environments, including support for a variety of grant-funded projects.