About EnviMetric
EnviMetric is a advanced remediation analytics software from Azimuth1 that provides a data-driven approach to site investigation. It combines AI-powered analysis, environmental sciences solutions, and public safety applications so users can effectively assess and manage remediation efforts. This software eliminates the need for additional soil and groundwater sampling, helping to reduce costs and simplify processes. EnviMetric is designed to aid decision-makers in various sectors, including environmental sciences and national security. Key capabilities: AI-powered analysis remediation analytics environmental sciences solutions public safety applications site investigation tools Best for: environmental professionals that need data-driven insights for effective remediation planning.
EnviMetric is an AI-powered environmental remediation analytics platform designed to forecast soil and groundwater contamination using only existing site data, eliminating the need for new sampling and dramatically reducing both time and cost compared to traditional site investigation methods. Delivered as a cloud-based SaaS solution, it applies machine learning trained on tens of thousands of historical contamination sites to generate predictive conceptual site models that estimate plume footprint, depth, concentration, and potential migration paths. The platform is built for defensibility, providing not just a model output but a full statistical explanation of the logic behind it, which is especially valuable during regulatory review or litigation. Its usability is geared toward environmental engineers and regulators who need verifiable evidence rather than black-box predictions, and the visualizations it produces make complex geospatial and statistical relationships easier to interpret. Performance is measured through efficiency and predictive accuracy, helping users move quickly from assessment to remediation planning without prolonged field campaigns.
Pros & Cons
- +EnviMetric uses AI and machine learning to forecast soil and groundwater contamination spread.
- +Eliminates the need for additional field sampling by leveraging existing site data.
- +Synthesizes insights from thousands of prior LNAPL, DNAPL, and PFAS sites for predictive modeling.
- +Provides open, defensible evidence with full transparency on model derivation and data relationships.
- +Saves time and cost compared to traditional conceptual site model development.
- +Supports planning, monitoring, and litigation with statistically contextualized outputs.
- +Developed by Azimuth1, a team of experts in geospatial analytics and environmental data science.
- −Accuracy depends on the quality and completeness of existing site data.
- −May not fully replace on-site investigation for highly complex or novel contamination scenarios.
- −Machine learning models require ongoing validation and updates to remain reliable.
- −Users may need technical expertise to interpret model outputs and statistical context.
- −Limited to contamination types and geographies represented in the training dataset.
- −Regulatory acceptance may vary depending on jurisdiction and model transparency requirements.
Features
Key features
- AI-Powered Site Investigation
- Uses artificial intelligence (machine learning) to forecast the spread of soil and groundwater contamination.
- Data-Driven Predictive Modeling
- Compiles and synthesizes data from thousands of prior LNAPL, DNAPL, and PFAS sites into a predictive contamination model.
- Eliminates New Field Work
- Generates the contamination model without requiring any new, costly soil and groundwater sampling or field work.
- Open and Defensible Evidence
- Provides a complete explanation of model derivation and statistical data for use in planning, monitoring, and litigation.
- Conceptual Site Model Generation
- Forecasts the contamination's footprint, concentration, depth, and movement, which are critical inputs for a conceptual site model.
- Efficiency and Cost Reduction
- Replaces many hours of analysis and days of field work, significantly reducing the expense of site investigation modeling.
Additional features
- AI-Powered Site Investigation
- Uses a machine learning approach to create a forecast for how widespread the contamination is after a leak or spill is discovered.
- Data-Driven Approach
- Compiles discoveries and learning from thousands of prior LNAPL, DNAPL, and PFAS sites into a predictive model.
- No Additional Sampling
- Generates the contamination forecast without requiring any new field work or soil and groundwater sampling.
- Open and Defensible Evidence
- Provides a complete explanation, data relationships, and statistical data to support planning, monitoring, and litigation.
- Contamination Spread Forecasting
- Creates a forecast for how widespread the contamination of soil and groundwater is.
- Footprint Estimation
- Determines the overall footprint of the effected area.
- Concentration Estimation
- Estimates the concentration of the material beneath the surface.
- Depth Estimation
- Predicts how deep the contamination goes.
- Movement Assessment
- Determines if the contamination is moving.
- Machine Learning (AI)
- Synthesizes millions of bits of information from years of site investigation to fit the target site.
- Leverages Existing Site Data
- Uses what has already been learned about the target site for comparison and modeling.
- Downloads (Whitepaper/Case Study)
- Provides resources to download a whitepaper and a case study related to the software.
Pricing
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About EnviMetric
EnviMetric is a advanced remediation analytics software from Azimuth1 that provides a data-driven approach to site investigation. It combines AI-powered analysis, environmental sciences solutions, and public safety applications so users can effectively assess and manage remediation efforts. This software eliminates the need for additional soil and groundwater sampling, helping to reduce costs and simplify processes. EnviMetric is designed to aid decision-makers in various sectors, including environmental sciences and national security. Key capabilities: AI-powered analysis remediation analytics environmental sciences solutions public safety applications site investigation tools Best for: environmental professionals that need data-driven insights for effective remediation planning.
EnviMetric Details
EnviMetric's In-App Market Place
Does EnviMetric have an in-app market place?
Yes
How many Mini-Apps in the marketplace?
1
Mini Apps
No
Pricing Options
Accepted Payment Currencies
USD ($), EUR (€), GBP (£), CAD ($), AUD ($), JPY (¥), CNY (¥), INR (₹), BRL (R$)
Pros & Cons
- EnviMetric uses AI and machine learning to forecast soil and groundwater contamination spread.
- Eliminates the need for additional field sampling by leveraging existing site data.
- Synthesizes insights from thousands of prior LNAPL, DNAPL, and PFAS sites for predictive modeling.
- Provides open, defensible evidence with full transparency on model derivation and data relationships.
- Saves time and cost compared to traditional conceptual site model development.
- Supports planning, monitoring, and litigation with statistically contextualized outputs.
- Developed by Azimuth1, a team of experts in geospatial analytics and environmental data science.
- Accuracy depends on the quality and completeness of existing site data.
- May not fully replace on-site investigation for highly complex or novel contamination scenarios.
- Machine learning models require ongoing validation and updates to remain reliable.
- Users may need technical expertise to interpret model outputs and statistical context.
- Limited to contamination types and geographies represented in the training dataset.
- Regulatory acceptance may vary depending on jurisdiction and model transparency requirements.
EnviMetric's Support Options
Email Address
contact@azimuth1.comContact
703-618-8866EnviMetric's Alternatives
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