Fontaine dbMax Solutions, Inc.
Hydrogeologic understanding. Practical analytical tools.
About Fontaine dbMax Solutions
We bring together hydrogeology, groundwater modeling, machine learning, and software development to support hydrogeologic decision-making.
Based in Colorado, Fontaine dbMax Solutions works with environmental consulting firms and project teams to interpret groundwater conditions, evaluate alternatives, and plan further investigation. Our goal is to connect hydrogeologic understanding with practical tools and analyses that teams can review, update, and use.
Hydrogeologic Experience Behind the Tools
Russ Fontaine founded Fontaine dbMax Solutions to bring together his experience in hydrogeology, numerical groundwater modeling, and software development.
His background includes work as a principal hydrogeologist, with experience in site characterization, conceptual site model development, and numerical groundwater flow and contaminant transport modeling. He has led technical teams across the environmental, mining, and energy sectors.
Russ focuses on connecting site observations and hydrogeologic interpretation with analytical tools that help project teams evaluate conditions and make informed decisions. His database and software experience supports this work by organizing complex information and making modeling workflows easier to review, repeat, and update.
Connect with Russ on LinkedIn (opens in a new tab) →Our Approach
Hydrogeologic Understanding
We start with the conceptual site model, physical processes, and available observations. These provide the foundation for selecting methods, interpreting results, and identifying gaps in site understanding.
Model Evaluation & Interpretation
We assess prediction performance, spatial patterns, sensitivity, and data limitations. Model interpretation and professional judgment help explain what the results support and where further investigation may be needed.
Repeatable Workflows
We connect data management, modeling, and visualization through organized, repeatable workflows. This makes analyses easier to review, communicate, and update as new information becomes available.
Current development
Hybrid Machine Learning for Groundwater Plume Characterization and Monitoring Design
Russ is developing and evaluating a hybrid machine learning workflow that combines monitoring observations, hydrogeologic information, and physics-informed predictors to support plume interpretation and identify promising locations for additional monitoring.
Using a controlled synthetic test bed, this work compares predictions with a known reference plume and evaluates performance using withheld monitoring locations. Development and evaluation are ongoing, including assessment of monitoring benefits and potential field applications.
Explore the Featured Study on Our Homepage →Professional Involvement
Russ serves on the Interstate Technology and Regulatory Council's AI/ML committee, contributing to its use-case subgroup. He collaborates with fellow members to identify and describe practical applications of artificial intelligence and machine learning in environmental investigation, analysis, and decision-making.
He presented the hybrid groundwater plume study at the committee's October 2026 in-person meeting in San Antonio.
Credentials & Technical Background
Professional Credential & Education
- Licensed Professional Engineer — Colorado
- Petroleum Engineering — Texas A&M University
- Geological Engineering — Colorado School of Mines
- Certification in Machine Learning - Standford Online
Technical Capabilities
- Site characterization and conceptual site models
- Groundwater flow and contaminant transport modeling
- Applied machine learning and spatial prediction
- Database design, data integration, and GIS workflows
- Custom software development — Blazor, .NET, and Python
- Cloud data platforms — Azure App Services and Azure SQL
Let’s Discuss Your Groundwater Project
We welcome opportunities to collaborate on groundwater modeling, applied machine learning, and supporting software solutions.