About

Ecovision Analytics is a technical partner for organizations where data volume, scientific rigor, and operational reality all have to meet in the same software. We build systems that turn complex operational and scientific data into scalable platforms and decision tools—not one-off scripts or fragile dashboards. See what we build and case studies.

GitHub@EcoVisionAnalyticsView repositories →

Our approach

We optimize for systems you can operate: clear boundaries between services, explicit data contracts, reproducible pipelines, and interfaces that field and office teams can trust.

Clarity: architectures and interfaces that stay understandable as complexity grows.

Reliability: validation, testing, and deployment patterns appropriate to the risk level.

Velocity: automation and ML where they remove real bottlenecks, not where they add science-project risk.

Data infrastructure

Cloud databases, PostGIS, warehousing, and ETL that keep downstream analytics stable.

Analytics & automation

Dashboards, reporting automation, QA/QC systems, and ML workflows designed for operators—not demos.

Custom software

Internal tools, SaaS, APIs, and field systems that encode how your organization actually works.

Spatial & scientific

Raster pipelines, environmental analytics, geospatial APIs, and spatial ML—integrated, not siloed.

Ecovision Analytics

Engineering partner for data-heavy technical teams

Focus

Infrastructure, software, and analytics for science and operations

Deliverables

Platforms, APIs, pipelines, ML systems, and field-ready tools

Let’s discuss your data, software, or infrastructure challenges.

Whether you are modernizing a legacy stack, standing up a new product, or unblocking a scientific workflow, we will meet you at the technical detail.

Start a conversation Services Case studies

Bio

I am a data scientist, geospatial analyst, and environmental researcher with a background in ecology, GIS, machine learning, and applied artificial intelligence. I hold a Master's degree in Data Science from Regis University and a Bachelor's degree in Biology with a minor in Geographic Information Systems from Metropolitan State University of Denver. Over the past several years, I have worked across academic, government, and private-sector projects involving ecological monitoring, predictive modeling, hyperspectral data systems, cloud-based geospatial infrastructure, and environmental analytics.

My technical work has focused heavily on machine learning, deep learning, and large-scale spatial data systems, including the development of predictive ecological models, geospatial intelligence platforms, and AI-assisted analytical tools. I have experience working with neural networks, computer vision workflows, statistical modeling frameworks, and cloud-native geospatial architectures designed to support complex environmental and scientific datasets. I have contributed to research and data initiatives with organizations including Montana State University, the National Park Service, and private environmental consulting firms, with work spanning wildlife trend analysis, large-scale spatial databases, and machine learning applications in environmental science. My work at Ecovision Analytics focuses on developing data-driven solutions centered around geospatial intelligence, environmental systems, applied analytics, and emerging AI technologies.

A lifelong outdoorsman, I have spent much of my life fishing, hiking, and exploring across Colorado, Wyoming, Idaho, Montana, and Florida. My connection to the American West and the subtropics of Florida continues to shape both my professional interests and my approach to conservation, land stewardship, and outdoor technology.