Mentioned in 59 AI use cases across 11 industries
This is like a smart weather-and-power crystal ball: it looks at recent weather and production data and uses machine learning to predict how much solar and wind power will be generated in the next few hours.
This is like a smart mechanic for fleets and industrial equipment. It constantly “listens” to machines, spots early warning signs of failure, and tells you when to service them before they break down.
Think of a smart city as a city with a digital nervous system. AI is the brain that helps it see traffic jams, power usage, crime hotspots, and public service demand in real time, then quietly adjusts lights, signals, and services to keep everything running smoother and safer.
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 field guide to all the ways computers can learn from medical and pharma data—like a tireless junior doctor and data analyst rolled into one—to help spot diseases earlier, pick better treatments, and run hospitals and clinical trials more efficiently.
This is like a smart assistant that reads a patient’s electronic medical record and quietly taps the doctor on the shoulder to say, “Based on all this history and lab data, this patient looks like they’re at high risk for X in the next few hours—here’s why and what to watch out for.”
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.
Think of this as a smart traffic conductor for Bangkok: cameras and sensors watch the roads, an AI brain predicts where jams will form, and then it recommends how to adjust traffic lights and routes so cars and buses flow more smoothly.
This is like giving ER triage nurses a smart calculator that looks at a patient’s vital signs and symptoms and helps decide how urgent their case is, so the sickest people are seen first and fewer patients are mis-prioritized.
Think of this as a digital mechanic that constantly listens to your vehicles, trains, or equipment, predicts when something is about to break, and tells you exactly when to bring it in for service so you avoid breakdowns and warranty fights.
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.
Kayros is like a super-smart air traffic controller for your factory. It constantly looks at all your machines, orders, and constraints, then automatically figures out the best possible production plan and schedule—and keeps adjusting it when things change.
This is like giving factory robots a brain that learns from experience, so they can move faster, make fewer mistakes, and adapt when something on the production line changes—rather than just blindly following a fixed script.
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 giving doctors a super-smart assistant that has read millions of medical cases and guidelines, then quietly whispers, “Here are the likely diagnoses and what to check next” while the doctor is still seeing the patient—especially to catch diseases earlier than usual.
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.
This is like giving your factory a smart air-traffic controller that constantly looks at all your machines, workers, and orders, then automatically decides the best sequence of jobs so everything ships on time with minimal idle time and overtime.
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.
This is like giving your building’s heating and cooling system a smart autopilot. It watches how your building behaves, learns patterns of occupancy and weather, and then constantly tweaks HVAC settings to keep people comfortable while cutting energy waste and emissions.
This is a blueprint for turning today’s hospital workflows from paper-and-phone based routines into a mostly digital, AI-assisted assembly line for patient care. Think of it as redesigning how doctors, nurses, and staff work together so computers do the repetitive checking, routing, and documentation, while humans focus on medical decisions and patient interaction.
This is like giving every doctor an always‑on digital colleague that has read every medical textbook, guideline, and journal article, and can quickly suggest possible diagnoses and treatments while the doctor is seeing a patient.
This is like giving ER doctors a super-fast, data-driven second opinion that watches the patient’s information in real time and quietly flags risks or suggests next steps, without replacing the doctor’s judgment.
Think of this as giving the emergency department a very fast, very experienced digital assistant that helps doctors and nurses notice critical problems sooner, choose better tests and treatments, and move patients through the system more efficiently — especially when things are chaotic and time-sensitive.
This is like giving an AI a chest X-ray or MRI scan and having it write the first draft of the radiologist’s report, instead of the doctor starting from a blank page. The doctor still reviews and edits, but the AI does the heavy lifting of describing what it sees.
Think of this as a smart co‑pilot for nurses: it watches patient data, compares it to what’s happened with thousands of similar patients before, and then suggests what to watch out for and what actions might be needed—while the nurse stays in full control.
Think of AI in oil and gas as a super-smart control room operator that never sleeps. It constantly watches wells, pipes, and equipment data, predicts when something will break, and suggests how to squeeze more oil and gas out of the ground at lower cost and risk.
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.
Think of this as a smart co‑pilot for radiology departments: it sits on top of imaging systems, helps route and prioritize scans, spots patterns, and surfaces the right information so radiologists and hospitals can move faster and make fewer mistakes.
Think of this as a smart scheduling assistant for hospital operating rooms that learns from past data and live conditions (staffing, emergencies, cancellations) to constantly reshuffle the theatre list so more patients get treated on time with fewer last‑minute surprises.
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.
Think of Cohesion as a digital command center for large office or mixed‑use buildings. It connects elevators, HVAC, security, access control, and occupancy data into one intelligent system so building operators can see what’s happening in real time and let software make many of the small adjustments people used to make manually.
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.
Think of a large office building as a living body. In the past, the heating, cooling and lighting were like organs running on fixed schedules, whether people were there or not. AI turns the building into a “smart body” that can sense where people actually are, how hot or cold it is, what energy costs right now, and then automatically adjusts everything in real time to stay comfortable while using far less energy.
This is like giving radiologists a super-smart assistant that looks at heart MRI scans and automatically measures how well the heart is working, then flags patterns that match different heart diseases—much faster and sometimes more consistently than a human reading every image by hand.
Think of generative design as an AI-powered junior architect/engineer that, instead of drawing one design, generates hundreds or thousands of options that all meet your rules—like budget, materials, safety codes, and space limits—then shows you the best ones to choose from.
Think of today’s big AI models as brilliant general doctors who know a little about everything but aren’t yet safe or precise enough to treat complex, high‑risk patients. This paper is about how to retrain and constrain those general doctors so they can safely become top‑tier specialists in specific medical tasks, like reading scans, summarizing patient records, or supporting treatment decisions.
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 a smart co-pilot for buildings: it watches how your facilities are used, how equipment behaves, and what work orders come in, then suggests what to fix first, when to schedule maintenance, and how to run the building cheaper and smoother.
Think of a city where every bus, traffic light, and parking space can talk to each other in real time, and an AI ‘traffic conductor’ continuously listens and adjusts things so people and goods move faster and more safely with less waste.
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 like a supercharged planning sandbox for delivery routes and vehicle schedules: you can try different ways of assigning trucks and drivers to trips on a computer, see how each plan performs, and then pick the best one before you spend real money on the road.
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.
Think of a city’s road network as a giant, messy orchestra. This use case is about putting an AI ‘conductor’ in charge that can see what’s happening on the roads in real time (via cameras and sensors), predict where jams and accidents might happen, and then adjust traffic lights, signals, and routing instructions to keep everything flowing smoothly.
This is about using machine learning as a smart ‘check engine’ light for factories and vehicles. Instead of waiting for a part to fail or doing maintenance on a fixed calendar, models watch sensor data (vibration, temperature, voltage, etc.) and warn you ahead of time when something is likely to break so you can fix it before it causes downtime.
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.
This is like hiring a smart mechanic that constantly listens to all your vehicles and equipment, predicts what’s about to break, and schedules repairs before anything actually fails or delays service.
This is a big-picture review of how modern software, sensors, automation, and AI are changing mines—from how ore is found and extracted to how equipment is run and energy is used. Think of it as a roadmap showing how a traditional mine can become a data-driven, semi-autonomous factory under the ground and in open pits.
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.