
Intelligence for the machines
you already own.
Retrofit smart monitoring onto existing factory equipment. Real-time machine health, energy data, and predictive insights— without replacing a single machine.
Reduce Downtime
30-50% downtime reduction. Predictive alerts before failure occurs. Real-time FFT/RMS analysis.
Cut Energy Waste
10-20% energy waste reduction. eWatch retrofits in under 15 minutes. Machine-level energy data.
Meet ESOS Compliance
Automated reporting for UK energy regulations. SECR and Net Zero ready. Scope 1 & 2 emissions tracking.
How We Work
Core principles that guide everything we do
Retrofit First
Most industrial equipment has 20-40 years of useful life remaining. We add intelligence without asking you to replace working machinery. Retrofit sensors onto existing machines, interface with multiport controllers, and push data wirelessly. Start with one production line, prove value, expand incrementally.
Protocol-Agnostic
Modbus RTU/TCP, OPC-UA, MQTT, BACnet, Zigbee Mesh, BLE 5.0, HL7/ASTM, DLMS/COSEM. Any sensor, any protocol. Custom drivers for legacy PLCs and proprietary medical devices. No vendor lock-in, ever.
Proven Results
30-50% downtime reduction with predictive maintenance. 10-20% energy waste identification. Up to 15% energy waste reduction with eWatch. 47% average downtime reduction. 18-month average ROI. Measured outcomes, not marketing claims.
UK Presence & Support
UK offices with local manufacturing expertise and business hours support. Site visits same-week. Global project experience including UAE (Abu Dhabi Municipality flood monitoring pilot), Saudi Arabia (Riyadh water quality, government fire systems), and India smart city initiatives.
End-to-End Solutions
From predictive maintenance sensors (motor temp, vibration, oil analysis, flow, pressure, furnace temp) to energy monitoring (eWatch with split-core CTs), carbon compliance (8-in-1 sensor for PM2.5, PM10, VOC, CO2, Methane, NO, Temp, Humidity), and complete process automation.
Flexible Controller Architecture
Plug & play base board with daughterboards for analog (0-10V, 4-20mA), digital, pulse sensors. Supports wired or wireless output. Protocol routers: BLE to Zigbee/WiFi/4G, Zigbee to 4G, WiFi to Ethernet, any protocol to Modbus RTU/TCP.
AI-Driven Analytics
SensEye Analytics Platform for real-time FFT/RMS analysis, predictive maintenance alerts, energy intelligence, machine utilization tracking (load/idle/standby/shutdown), cost per customer order, and carbon reporting (SECR/ESOS Phase 3).
Legacy System Integration
Extract data from existing PLCs with Modbus TCP/RTU, BACnet, OPC. Retrofit existing smart meters with wireless adaptors using optical port or RS232/485. Smart Meter Adaptor converts existing meters into wireless smart meters.
Predictive Maintenance Sensors
eWatch Sensor Models

How to reduce downtime in manufacturing? How to increase asset utilization? These are the two core questions we answer. Implementing IoT systems results in a total shift from manual data logging to precise, timely real-time data from streaming sensors. Predictive analytics allows forecasting system failures and machine breakdowns, saving a lot of money.
shiju@tibsglobal.com | +44 7899 617831
Designed for Your Role
Every decision-maker in manufacturing and industry
Plant Manager / Operations Director
Unplanned downtime, rising energy costs, pressure for Industry 4.0, machine utilization mystery, energy cost per customer order
Engineering Lead / Maintenance Manager
Reactive maintenance costs, incompatible legacy systems, manual monitoring of oil levels/temperature/pressure, undetected machine anomalies
Finance Director / Procurement Manager
Capital budget constraints, unclear ROI, vendor lock-in risk, rising energy bills, unknown energy cost allocation
Sustainability / ESG Manager
Carbon reporting requirements (SECR/ESOS), Scope 1 & 2 emissions measurement, pressure for Net Zero
Production Planning Manager
Machine utilization unknown, idle vs run time tracking, production bottlenecks, capacity planning challenges
Energy Manager / Facilities Manager
Energy leakages hard to identify, peak usage unknown, machine-level energy data unavailable, power factor optimization
