AI-Driven Intelligencefor Gas Infrastructure
> designed to detect operational anomalies
An industrial technology platform combining IoT, connected equipment, machine learning and predictive analytics to help natural gas distribution businesses monitor infrastructure, detect operational anomalies, improve asset utilisation and optimise performance.
An AI-enabled industrial intelligence and automation platform for the natural gas industry
GasNet applies machine learning to the data a gas network already produces: dispenser sales, compressor health, metering and LCV Filling Post data, captured live through IoT and connected equipment.
Its models are designed to learn how each asset and station normally behaves, flag what deviates, estimate what is likely to fail and size where revenue is leaking. They run on a platform that already handles station operations, live telemetry and remote dispenser rate change.
Five AI capabilities on one connected platform
Each capability carries a label showing where it stands, so it is clear what runs in the platform today and what is planned or under development.
Connected Industrial Data Platform
Collect and consolidate operational data from CNG dispensers, compressors, metering equipment and related station infrastructure.
See how it worksML-Based Anomaly Detection
Identify unusual dispenser behaviour, abnormal sales patterns, equipment underperformance and deviations from historical operating profiles.
See how it worksPredictive Asset Maintenance
Analyse operating trends and equipment history to estimate failure risk and prioritise preventive maintenance.
See how it worksIntelligent Sales and Loss Analytics
Compare actual and expected sales, identify reconciliation exceptions, and estimate operational or revenue improvement opportunities.
See how it worksNetwork Performance Optimisation
Forecast station demand, benchmark sites and recommend operational improvements across distributed CNG infrastructure.
See how it worksProposed ML enhancements and product-direction items are functionality planned or under development.
Every AI flag explains itself
An alert nobody trusts gets ignored. GasNet's ML layer is designed so each flag shows the signals behind it, the probable cause and the next step, and learns from how the team responds.
- Signal-level drivers, ranked by how much each one contributed
- Probable cause written in the language of station operations
- One-click next step: O&M ticket, reconciliation exception or preventive maintenance
- Not-an-issue feedback that improves the next version of the model
Questions the models are built to answer
Each team gets answers from the same connected data, in the form it already works with.
Proposed ML enhancementsHead office & management
Demand forecasts, site benchmarking and revenue-loss estimates across the network.
O&M & control rooms
A failure-risk ranking of every asset, with the trend behind each score.
Finance & marketing
Sales anomalies and reconciliation exceptions, sized in kg and ₹.
CNG stations
Fewer manual checks. Flags arrive with a probable cause and a next step.
From station signal to operating decision
Four layers, each building on the one before. Connecting and consolidating data runs in production today. The learning layer is where GasNet is heading.
Connected equipment
Edge gateways read dispensers, compressors, metering skids and LCV Filling Posts over Modbus, RS-485 and MQTT.
Existing foundationIndustrial data platform
Readings, shift entries and reconciliations land in one structured data model, from station to head office.
Existing foundationMachine learning models
Models learn each asset's normal operating profile to flag anomalies, estimate failure risk and forecast demand.
Proposed ML enhancementAlerts and actions
Threshold alerts, escalation and remote rate change today; ranked anomalies and maintenance priorities next.
Clean, live data for the models
Machine learning is only as good as its data. GasNet already streams live equipment readings and runs station operations digitally, so the models start from clean, structured history.
- Live dispenser, compressor and LCV Filling Post telemetry over secure MQTT
- Remote dispenser rate change with acknowledgement and audit trail
- Daily gas and cash reconciliation from station to head office
- Integration with SAP, SCADA and Active Directory
See what AI can find in your network
Book a walkthrough of the GasNet intelligence layer and the live platform it runs on, mapped to your own stations.