Heavy Industry
AI that prevents shutdowns and keeps your HSE record clean
You get predictive maintenance agents, safety monitoring, environmental compliance workflows, and operational intelligence — all running on your cloud with full audit trails and governance built in.
Built on your AWS, Azure or GCP tenant. HSE-compliant. Regulator-ready.
Proof of concept
To measurable ROI
AWS, Azure or GCP
In heavy industry, the cost of a missed signal is measured in millions and lives
Unplanned shutdowns in steel, mining, and construction cost millions before the first repair crew arrives. Safety incidents generate regulatory exposure that compounds for years. Environmental compliance failures can suspend operating licences.
Your assets already generate the data that predicts these events. We build AI agents that connect your SCADA, sensor, and ERP systems, act on signals in real time, and create the safety and compliance documentation your HSE team and regulators need.
typical cost of a single unplanned steel mill shutdown
accuracy in predicting equipment failures 3–6 weeks ahead
average annual maintenance cost reduction per machine
reduction in unplanned downtime reported by Tata Steel AI deployment
From asset sensor to compliance record — in one workflow
We connect your SCADA, IoT, ERP, and safety systems to AI agents that predict failures, monitor safety compliance, track environmental obligations, and generate the documentation your HSE, legal, and regulatory teams require — all on your cloud
SCADA, ERP, sensor and safety systems
- Approval gates
- Human-in-loop
- Audit logging
AWS, Azure or GCP
- Maintenance records
- Safety reports
- Compliance logs
What we deliver for heavy industry operators
Agents built for high-consequence, compliance-critical environments
Predictive asset maintenance
Predict equipment failures 3-6 weeks before they happen.
ROI
Process
Ingest asset telemetry
Vibration, temperature, and operational data from crushers, conveyors, mills, and furnaces
Predict failure windows
ML models forecast degradation 3–6 weeks ahead with component-level confidence scoring
Schedule maintenance
Prioritised work orders with parts requirements and risk-based justification for planners
Sources
Originally published: AWS Case Study — Tata Steel; Azure Case Study — ArcelorMittal — reproduced for illustrative purposes
Safety monitoring and PPE compliance
Monitor PPE compliance and hazards via site cameras.
ROI
Process
Analyse camera feeds
Site CCTV and wearable camera streams processed by computer vision models in real time
Detect unsafe conditions
AI identifies PPE violations, unsafe practices, and hazardous zone incursions
Alert and document
Real-time alerts to supervisors with timestamped incident records for HSE reporting
Sources
Originally published: Azure Case Study — BHP; AWS Case Study — Rio Tinto — reproduced for illustrative purposes
Environmental compliance monitoring
Track emissions and flag variances before they become incidents.
ROI
Process
Monitor environmental data
Emissions, discharge, dust, and noise sensors streaming data against regulatory thresholds
Detect threshold breaches
AI flags variances before they become reportable incidents, with trend analysis
Generate compliance records
Automated environmental reports structured for regulator submission and audit
Sources
Originally published: GCP Case Study — Glencore; Azure Case Study — Vale — reproduced for illustrative purposes
Production optimisation
Recommend adjustments that improve output and cut input costs.
ROI
Process
Collect process data
Temperature, pressure, flow rate, and energy consumption from furnaces, mills, and production lines
Optimise parameters
AI identifies optimal settings to maximise throughput while minimising energy and material costs
Implement adjustments
Recommendations reviewed by operators, approved changes tracked with performance metrics
Sources
Originally published: AWS Case Study — ArcelorMittal; Azure Case Study — ThyssenKrupp — reproduced for illustrative purposes
HSE reporting automation
Generate shift reports and regulatory submissions automatically.
ROI
Process
Aggregate HSE data
Incident reports, near-miss logs, inspection results, and safety observations collected automatically
Generate structured reports
AI compiles shift safety reports, incident summaries, and trend analysis from raw data
Submit to regulators
Reports formatted for HSE and environmental regulator requirements, ready for review and filing
Sources
Originally published: GCP Case Study — Anglo American; Azure Case Study — Fortescue — reproduced for illustrative purposes
Fleet and equipment management
Predict failures and optimise deployment across fleet and plant.
ROI
Process
Connect fleet telematics
GPS, engine diagnostics, and usage data from haul trucks, loaders, and mobile plant
Optimise utilisation
AI recommends deployment patterns, route optimisation, and predictive maintenance schedules
Track compliance
Maintenance compliance, operator certification, and inspection status monitored across all assets
Sources
Originally published: AWS Case Study — Caterpillar; Azure Case Study — Komatsu — reproduced for illustrative purposes
Quality and process control
Flag quality deviations and generate root-cause analysis.
ROI
Process
Monitor quality parameters
Chemical composition, temperature, thickness, and surface quality measured in real time
Flag deviations
AI detects out-of-spec conditions and identifies root causes from process parameter correlations
Guide corrective action
Root-cause analysis and recommended adjustments delivered to process engineers
Sources
Originally published: GCP Case Study — Tata Steel; AWS Case Study — Nucor — reproduced for illustrative purposes
Contractor and permit management
Track contractor permits and site access compliance.
ROI
Process
Track contractor data
Qualifications, certifications, inductions, and permit-to-work status monitored continuously
Validate compliance
AI flags expired certifications, missing inductions, and permit conflicts before site access
Automate approvals
Compliant contractors approved automatically; non-compliant entries escalated to site managers
Sources
Originally published: Azure Case Study — Rio Tinto; GCP Case Study — BHP — reproduced for illustrative purposes
Audit and regulatory documentation
Answer regulatory inquiries in hours with full audit trails.
ROI
Process
Capture decision data
Every AI-assisted operational and safety decision logged with data inputs and model outputs
Structure for audit
Records organised by regulation, asset, and time period for rapid retrieval during inquiries
Enable rapid response
HSE team can answer regulatory inquiries in hours with complete traceability chains
Sources
Originally published: AWS Case Study — Vale; Azure Case Study — Anglo American — reproduced for illustrative purposes
CEO & Managing Director, Tata Steel
T.V. Narendran
"AI and advanced analytics are at the heart of our digital transformation at Tata Steel. By deploying machine learning across our blast furnaces, rolling mills, and supply chain, we have achieved first-time quality success rates above 90 percent and unlocked over $1.4 billion in cumulative value — with a proven return of 10 times on every dollar invested in AI"
Originally published: Tata Steel Press Release — AI & Digital Transformation — reproduced for illustrative purposes
How we work in high-consequence industrial environments
We do not deploy AI into heavy industry environments without first understanding what a failure means — in production terms, in safety terms, and in regulatory terms. We map your use case to your HSE obligations and operational constraints before we write code.
Every agent runs on your cloud tenant. Your SCADA data, your sensor data, your operational records — none of it leaves your environment. Your safety officers, your insurers, and your regulators can inspect the system directly.
01
Operational risk mapping
We identify your use case, assess its HSE and compliance implications, and define the governance framework before any build begins.
02
30-day proof of concept
A working AI agent on your cloud, connected to your SCADA and operational systems, with demonstrable output in 30 days.
03
Governance by design
Human-in-the-loop controls, audit trails, and explainable outputs — built so your safety board and regulators can sign off.
04
Scale in 12–18 weeks
From one validated workflow to measurable operational ROI — with HSE documentation your compliance team can present to regulators.
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