Industrial Resilience Starts with Intelligent Monitoring

Prevent Industrial Leaks
Before They Start.

Next-generation, multi-sensor AI system for non-invasive leak prediction and 24/7 autonomous infrastructure monitoring.

Request a Demo View Technical Specs

96.4% Accuracy

12ms Latency

24/7 Monitoring

Pmesh autonomous monitoring rover in industrial environment

Sensor Active Node #12 — Zone A

Acoustic Level Normal — 23dB

The Problem

Industrial Monitoring
is Broken.

Every year, undetected leaks cost the global industry billions in damage, downtime, and environmental penalties. Current methods are failing.

Manual & Reactive

Traditional inspections happen on fixed schedules. Leaks that develop between cycles go undetected for weeks, escalating into critical failures.

73% of leaks found after damage occurs

Expensive & Immobile

Enterprise tools like FLIR Si2 or Fluke SV600 cost $15,000+ per unit and are either handheld or fixed — leaving coverage gaps across your facility.

$15K+ per monitoring device

High False Alarms

Single-sensor systems produce noisy data, leading to false positives that waste technician time and erode trust in monitoring systems.

40%+ false alarm rate (industry avg)

Our Solution

The Pmesh Advantage

An integrated AI-powered monitoring system that combines multi-sensor fusion, edge computing, and predictive analytics into one autonomous platform.

Multi-Sensor Fusion

Combining acoustic microphone arrays with thermal imaging cameras for comprehensive leak detection. Dual-modality verification eliminates false alarms — if the sound says leak, the heat confirms it.

Acoustic Arrays Thermal Imaging Zero False Alarms

Edge AI Processing

All inference runs locally on Raspberry Pi and Jetson Nano hardware. With 12ms latency, decisions are made at the sensor — no cloud dependency, no privacy concerns.

12ms Latency On-device AI

Predictive Analytics

Deep CNN models trained on thousands of failure patterns identify degradation trends before they become critical. Move from reactive maintenance to predictive intelligence.

CNN Models Failure Prediction

Performance

Built for Precision

Real-world performance metrics from our multi-sensor AI pipeline, validated across industrial deployments.

Overall Accuracy

Combined classification accuracy across acoustic and thermal sensor inputs

Recall Rate

Leak detection recall — virtually no real leaks go undetected by the system

0ms

Inference Latency

End-to-end processing time from sensor input to classification output on edge hardware

Pmesh Dashboard — Live Monitoring

Pmesh real-time monitoring dashboard showing thermal heatmap on factory pipes

Compare

How Pmesh Outperforms

See how our integrated solution stacks up against industry incumbents.

Feature	Pmesh	FLIR Si2Handheld Camera	Fluke SV600Fixed Sensor
Detection Method	Multi-Sensor Fusion	Acoustic Imaging	Vibration Sensing
Mobility	Autonomous Rover	Manual / Handheld	Fixed Position
Coverage	Full Facility	Point-by-point	Single location
Predictive Intelligence	CNN-based Prediction	None	Basic Trending
Real-time Processing	12ms Edge AI	Manual Review	Cloud-dependent
24/7 Autonomous
Cost-effectiveness	Best in Class	$15,000+	$5,000+ per node
False Alarm Rate	<2%	Operator-dependent	~15%

Our Team

The Minds Behind Pmesh

A cross-disciplinary team of engineers and designers building the future of industrial monitoring.

Ilgar Shikhverdiyev

Hardware Lead

Designs and integrates the multi-sensor hardware platform, from acoustic arrays to thermal modules and autonomous rover systems.

Rufat Azizzade

Software & AI Lead

Architects the AI pipeline — from CNN model training and sensor fusion algorithms to edge deployment and real-time inference systems.

Kamil Rahimli

3D & Visual Design

Creates photorealistic 3D renders, technical diagrams, and the visual identity that brings Pmesh's industrial technology to life.

Get Started

Ready to Eliminate Unplanned Downtime?

See Pmesh in action. Schedule a live demo with our engineering team and discover how predictive monitoring can transform your operations.