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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

Vendor
Azimuth1
Year Launched
2013
Location
1751 Pinnacle Dr, McLean, VA 22102
Deployment
cloud
Training Options
documentation
Countries Served
All Countries
Languages
English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic, Turkish, Hindi, Bengali, Punjabi, Urdu.
Users
Environmental Engineer, Hydrogeologist, Remediation Consultant, Site Assessment Specialist, Environmental Project Manager, Regulatory Reviewer, Risk Assessor, Environmental Compliance Officer, Litigation/Expert Witness Consultant, ESG/Environmental Due Diligence Analyst
Industries Served
Environmental Consulting, Energy and Utilities, Oil and Gas, Manufacturing, Mining, Real Estate and Land Development, Transportation and Infrastructure, Government and Regulatory Agencies
Tags
Environmental, EnviMetric

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

Free trial
Free version
Request a quote
Promo Offer

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.

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