Gear Brain
Six AI systems. One team building the robots and the intelligence behind them.
Gear Brain's AI suite layers onto equipment you already run — retrofit sensors, existing cameras, your current PLC/SCADA and WMS — as well as our own manipulators, humanoids, and UAVs. Start with one solution, expand as it proves out.
Predictive Maintenance
Vibration, thermal, and acoustic sensor fusion flags failures weeks before breakdown. Bolt-on sensors — no PLC/SCADA rework.
Learn more →OEE Dashboards
Availability, performance, and quality in one live view for plant managers — the natural upsell after predictive maintenance.
Learn more →Robotic Process Optimization
AI-optimized pick-and-place and palletizing sequences for Gear Brain manipulator installs — software-only upgrade.
Learn more →Digital Twin & Simulation
Preview your line before a robot ships, and train operators safely on a simulated environment first.
Learn more →Energy Optimization AI
Load forecasting and HVAC/compressor optimization — built for UAE/KSA energy-cost and net-zero mandates.
Learn more →Warehouse & Logistics AI
Routing and inventory forecasting built for the scale of DP World, Jeddah Islamic Port, and Aramco logistics.
Learn more →AI data generated for demo purposes
From raw sensor signal to a scheduled work order.
The engine doesn't just watch one number drift outside a threshold — it models the full degradation curve for each asset class and cross-checks against your maintenance history.
Vibration, thermal, and acoustic sensors clamp onto existing equipment — no rewiring or downtime, and no need to touch your PLC or SCADA system.
Separate models per asset class — pumps, motors, gearboxes, compressors — trained on real failure signatures, not generic anomaly detection.
Each flag carries a failure mode, a confidence score, and a predicted window, so teams can triage instead of guess.
Work orders route directly into your existing maintenance system with a recommended action and parts list attached.
| Asset class | Signals monitored | Typical lead time | Common failure modes flagged |
|---|---|---|---|
| Pumps | Vibration, temperature, current | 2–4 weeks | Bearing wear, cavitation, misalignment |
| Motors | Current signature, temperature | 3–6 weeks | Winding degradation, rotor bar faults |
| Gearboxes | Vibration, acoustic, oil temperature | 2–5 weeks | Gear tooth wear, lubrication faults |
| Compressors | Vibration, thermal, pressure | 3–5 weeks | Valve wear, thermal drift, seal failure |
| Robot manipulators | Joint torque, vibration, cycle time | 1–3 weeks | Actuator wear, backlash, encoder drift |
AI data generated for demo purposes
One number that tells plant managers exactly where the line is losing time.
Availability, performance, and quality data pulled from your existing PLCs, sensors, and MES into a single live OEE view — no new hardware required if you're already instrumented.
See exactly where downtime, slow cycles, or scrap are eating capacity, updated in real time.
Every OEE dip is tagged with a cause — changeover, breakdown, starved, blocked — instead of a plant manager digging through logs.
Once sensors are on a machine for failure prediction, that same data stream feeds OEE with no extra install — low technical risk, quick to add.
AI data generated for demo purposes
The same arms, moving smarter.
AI-optimized pick-and-place and palletizing sequences for Gear Brain manipulator installs, cutting cycle time and collision risk without touching the hardware.
Path optimization typically cuts double-digit percentages off cycle time on live manipulator cells already on the floor.
Pick order re-plans automatically as pallet patterns or SKU mixes change, without a manual reprogram.
Deploys as a controller update to Gear Brain manipulators already installed — ties our hardware and software together as one upsell.
AI data generated for demo purposes
See the line before a single robot ships.
A simulated model of your line lets you preview manipulator placement, throughput, and bottlenecks before installation — and gives operators a safe environment to train on before robots go live.
Show a prospective client their own line, with proposed robots in place, before a contract is signed.
Simulation surfaces throughput constraints that would otherwise only show up after a costly physical install.
New staff train on the simulated line and controls before working alongside live equipment.
AI data generated for demo purposes
Cut energy spend without cutting output.
Load forecasting and AI-driven control for HVAC, compressors, and major plant loads — tuned to reduce energy cost and carbon intensity in line with Dubai Net Zero 2050 and Saudi Vision 2030 targets.
Predicts plant and facility demand hours ahead to shift non-critical loads off peak tariff windows.
Continuously tunes setpoints against real occupancy, production schedule, and weather data.
Tracks energy intensity and emissions reductions for ESG and regulatory reporting.
AI data generated for demo purposes
Smarter routing and inventory forecasting for high-throughput sites.
AI-driven routing and demand forecasting for ports, distribution centers, and logistics operators — built for the scale of operations like DP World, Jeddah Islamic Port, and Aramco's logistics network.
Re-sequences yard, forklift, or last-mile routes in real time as orders and congestion change.
Predicts SKU-level demand to reduce stockouts and overstock across distribution nodes.
Layers onto your current warehouse and transport management systems rather than replacing them.
From site survey to live system in four stages, for any of the six.
Site & systems survey
Our engineers catalogue equipment, existing sensors, cameras, and the control/IT systems already in place.
Retrofit & integration
Sensors, cameras, or edge units are fitted where needed — layered onto existing PLC, SCADA, MES, or WMS rather than replacing them.
Model calibration
Models run in shadow mode against your operating data to tune thresholds before anything goes live.
Go live
Alerts and dashboards route into your existing workflows, with our team monitoring alongside yours for the first 90 days.
Run a 90-day pilot on your highest-impact use case.
Start with predictive maintenance, OEE dashboards, or whichever solution maps to your biggest cost driver, and expand once it proves out.