Safety Intelligence
MSHA
Benchmark AI
Every US mining company files safety data with MSHA. Most never look at it systematically. We automate the entire process — internal benchmarking, peer comparison, and AI models grounded in published research — updated weekly.
Three Capabilities
Internal. Peer. Academic.
All Three at Once.
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Internal Benchmarking
Compare performance across your own sites. Which mines have the highest S&S violation rates? Which shifts have the best safety records? Which equipment categories drive the most MSHA citations? Your own data analyzed systematically — not just reported. Identify your best sites and understand what they're doing differently.
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Peer Benchmarking
How does your TRIR compare to Peabody? Your violation rate per million tons vs Arch Resources? MSHA Benchmark AI pulls public filing data for every operator in your commodity and region and builds a normalized comparison. Production-adjusted metrics ensure you're comparing like for like — not just raw counts.
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Research Operationalization
MSHA data is the most studied mine safety dataset in the world. Decades of peer-reviewed research have produced published models linking violations, inspections, and production to injury outcomes. Our LLMs read these publications, domain experts curate the findings, and we operationalize the models against your data. Your operation measured against published science — not just competitors.
Platform Workflow
1 — Industry Dashboard
Industry Dashboard — Live MSHA Data
Live KPIs and trend charts from 20+ MSHA datasets, updated weekly.
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2 — Internal Data Integration
EHS Integration — Internal records matched against MSHA government data
Your internal EHS records merged with MSHA government data into one picture.
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3 — Reconciliation
Violation Reconciliation — Internal citations matched against MSHA records
Match internal citations against MSHA records — identify gaps in both systems.
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4 — Peer & Internal Benchmarking
Peer Comparison — TRIR and DART rate trends across companies
Compare your sites internally and against every peer in the MSHA registry.
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5 — Deeper Analytics
Predictive Analytics — ML-powered risk scoring and violation forecasts
Predictive models, research-backed tools, and AI anomaly detection.
Data Foundation
20+ MSHA Datasets.
Integrated With Yours. Weekly.

MSHA Benchmark AI doesn't just pull government data — it merges it with your internal EHS records to create a single, reconciled safety picture. All 20+ MSHA datasets are downloaded, cleaned, validated, and joined with your company's incident tracking, citation logs, and production systems into a unified dimensional model. Every week, automatically. Historical data back to the year 2000 available for trend analysis.

DatasetCoverage
Mine Violations (S&S and non-S&S)All US mines, 2000–present
Accident & Injury ReportsMSHA Part 50 filings
Inspection RecordsRegular and spot inspections
Annual Production DataTonnage by commodity and mine
Employment & HoursFor injury rate normalization
Penalty AssessmentsCitation severity and penalty amounts
Mine Status & OperatorsActive, idle, abandoned mines
Pattern of Violations (POV)MSHA POV program status

EHS Integration & Reconciliation

Your internal EHS records matched against official MSHA government data — identifying gaps in both systems, classification mismatches, and unmatched citations. Violation reconciliation ensures your internal tracking aligns with what MSHA has on file, while production records, shift data, and equipment logs are joined for normalized metrics and root cause analysis that goes beyond what either system can show alone.

AI Anomaly Detection

Automated detection of unusual spikes in violations, injury rates, or inspection intensity relative to your historical baseline and peer group. Flags emerging issues before they reach regulatory attention.

POV Early Warning

Predictive risk scoring that identifies mines trending toward Pattern of Violations status. The POV program results in elevated inspection frequency and significant operational constraints — early warning gives you time to act.

Natural Language Reports

AI-generated executive summaries of your benchmarking position, updated weekly. Board-ready safety intelligence without requiring an analyst to compile it.

Peer Pool
Know Where You Stand
Against Your Competitors

Default peer comparison pools include the major US coal operators. Custom peer groups can be built from any subset of the MSHA registry — by commodity, state, mine type, production volume, or any combination.

Peabody Energy Arch Resources Alpha Natural Resources Alliance Resource Partners Warrior Met Coal Foresight Energy CONSOL Energy Any operator in the MSHA registry
Key Metrics Tracked
TRIR Total Recordable Incident Rate
S&S Rate Significant & Substantial violation rate
Inspection Intensity Inspections per mine per year
Violations / MT Per million tons produced
Injuries / 200K hrs MSHA standard normalization
Penalty Severity Average penalty per citation
Most-Cited Standards CFR citation frequency analysis
POV Risk Score AI-predicted POV trajectory
Trend Direction 12-month rolling vs prior period
Safety Analysis
The Most Studied Safety Data
in the World. Operationalized.

MSHA data is the most heavily studied mine safety dataset on Earth. Decades of peer-reviewed research have produced published models linking violation patterns, inspection frequency, production variables, and workforce factors to injury rates, penalty exposure, and fatality risk.

Most of that research sits in journals no safety manager will ever read. We change that. Our LLMs ingest peer-reviewed publications, extract the statistical models and analytical frameworks, and — with human curation from domain experts — operationalize them into tools that run against your data. Every analysis in the Safety Analysis Suite is grounded in published science, not proprietary guesswork.

Research-to-Operations Pipeline

Peer-reviewed papers are read by LLMs that extract methodology, statistical models, and key findings. Domain experts curate and validate the extraction. The models are then implemented as live analytical tools that run against MSHA public data combined with your internal records — turning academic research into actionable safety intelligence.

Published Sources Include

Each analysis cites its source research directly in the application:

NIOSH Safety Pays (2019) Yilmaz (2011) Gernand (2014) Grayson (2009) Milam (2020) Li (2022) Beeche (2023) Amoako (2021) Yedla (2020) Groves (2007)
Safety Analysis Suite — 8 Research-Backed Tools
Safety Analysis Suite — Research-backed analytical tools for mine safety professionals
Injury Cost Calculator — Based on NIOSH Safety Pays
Injury Cost Calculator — Financial impact modeling using NIOSH Safety Pays methodology
Penalty Exposure Estimator
Penalty Exposure Estimator — Based on Yilmaz (2011) and Li (2022)
Contractor Risk Assessment
Contractor Risk Assessment — Based on Amoako (2021) and Groves (2007)
Request a Benchmarking Report

We can produce a sample peer benchmarking report for your operation using publicly available MSHA data — no internal data required to get started. See what the analysis looks like before committing to anything.

Request a Sample Report How We Work →