Mentioned in 27 AI use cases across 7 industries
This is like a smart mechanic for power-plant valve actuators: it watches sensor data, predicts when parts are likely to fail, and also explains in plain engineering terms why it thinks a failure is coming (e.g., which pressures, temperatures, or vibrations are driving the risk).
Think of this as a smart control tower for mining and other heavy industries that watches your environmental, social, and operational data in one place and uses AI to flag risks and opportunities before they impact production or reputation.
This is like giving the power grid a very smart weather forecast, but instead of predicting rain, it predicts how much electricity people will use so green energy sources can be used more efficiently.
This is like putting a smart ‘check-engine’ light on every critical asset in an oil & gas operation. Instead of waiting for something to break, software constantly watches sensor data and warns you in advance when a pump, compressor, or pipeline component is likely to fail, so you can fix it during planned downtime.
Think of this as putting a “smart brain” on top of every critical piece of oil & gas equipment. It constantly listens to sensors, learns what ‘normal’ looks like, and warns you before something breaks so you can fix it at the best possible time.
Think of this as turning the power grid into a ‘smart internet of energy’ where 5G connects all the equipment and AI acts like a traffic controller, constantly balancing where electricity should go, when to store it, and how to avoid waste or outages.
This is about using AI as a super-smart control center for factories and supply chains. It watches machines, inventory, orders, and logistics in real time, then predicts problems before they happen and suggests the best way to run production so you waste less time, material, and money.
Think of this as a ‘self-optimizing factory brain’ for mines: it watches every step of crushing, grinding, and separating ore, learns what settings give the best results, and then continuously tweaks the knobs to squeeze out more metal with less waste, energy, and downtime.
This is like a smart autopilot for renewable power plants that mixes solar, wind, and batteries. It predicts how much energy you’ll get from the sun and wind, how much your customers will use, and then automatically decides when to store, sell, or buy electricity to save money and keep the lights on.
This is like putting smart ears and eyes on your machines so they can tell you when something sounds or looks wrong—before it breaks. Small sensor boxes sit on the equipment, watch and listen in real time, and warn you early so you can fix problems during planned downtime instead of after a costly failure.
Think of this as a playbook of ways to use AI as the ‘brains’ of a modern factory—helping machines predict failures, optimize production lines, and improve quality with less human guesswork.
Think of a data center as a giant, always‑on factory plugged into the power grid. Gridmatic builds an AI "power manager" that constantly watches electricity prices, grid conditions, and the data center’s workload, then turns dials up or down so the facility uses cheaper, cleaner power without sacrificing reliability.
This is like giving your power plant or energy equipment a “check engine” light that warns you days or weeks before something breaks, instead of after it fails. Sensors continually watch vibration, temperature, pressure, etc., and machine‑learning models learn the normal patterns so they can flag early signs of trouble.
This is like giving a commercial building’s heating and cooling system a smart autopilot. It watches how energy is used, learns building patterns (people coming and going, outside weather, peak loads), and automatically tunes HVAC settings to keep tenants comfortable while using less electricity.
This is like giving your car factory a super-smart assistant that watches everything on the line, spots problems before they happen, and suggests small tweaks that make the whole plant run faster, cheaper, and with fewer defects.
This is like giving the power company a very smart weather forecast, but instead of predicting rain or sunshine, it predicts how much electricity people will use in the next few hours or days using machine learning.
Think of this as giving power plants and grids a smart brain that constantly watches operations, predicts future demand and equipment issues, and suggests optimal ways to run everything more safely and cheaply.
This is like giving the power grid a smart navigation system that can instantly reroute electricity around traffic jams and accidents so the lights stay on and the roads (power lines) don’t get overloaded or damaged.
This is about teaching factories to "take care of themselves." Machines learn to warn you before they break, adjust their own settings for quality and efficiency, and eventually coordinate with each other so the whole plant runs with less human babysitting and fewer surprises.
This is like a “health monitoring and early-warning system” for industrial equipment in energy operations. It watches sensor data from machines, predicts when something is likely to break, and suggests when to repair or adjust operations before failures happen.
A smart grid is like upgrading from an old landline to a modern smartphone for your electricity network. Instead of just pushing power one way from big plants to homes, the grid becomes two‑way, with sensors and software that can see what’s happening in real time, shift loads, use home batteries and solar panels, and prevent or shorten outages.
This is like a weather forecast, but for how much energy a building will use. It learns from past data about the building (design, materials, historical meter readings, weather) and then predicts future consumption so you can plan and optimize better.
This is like a smart air-traffic controller for a factory: it looks at all your orders, raw materials, machines, and people, then constantly rearranges the schedule so everything runs smoothly, on time, and at the lowest cost.
This is like giving every pump, compressor, and turbine in an energy plant a smart mechanic that listens to how it’s running, spots early signs of trouble, and tells your team what to fix before anything breaks.
Imagine your entire oil and gas operation—wells, pipelines, refineries—covered in smart sensors and watched by an always‑awake digital control room. That digital brain constantly learns from data, spots problems before they happen, and quietly adjusts valves, pumps, and schedules so you produce more oil and gas with less downtime, waste, and risk.
Think of this as a building’s "autopilot for energy": it constantly watches how the building is being used, how hot or cold it is, what the weather and prices look like, and then automatically adjusts heating, cooling, lighting and other systems to keep people comfortable while using as little energy (and money) as possible.
Think of a smart building as a self-driving car for energy and operations: sensors constantly watch what’s happening (people, temperature, light, equipment), and AI decides when to heat, cool, light, or ventilate each space so you use the least energy without sacrificing comfort.