What do oil rigs and autonomous machines have in common? That may sound like a bad lead-in to a joke, but they share a common thread: they both use on-site AI solutions in conditions where access to cloud networks is limited or nonexistent.
Whether you are monitoring infrastructure on an oil rig far offshore or adapting AI models in demanding environments, waiting for data to reach a cloud resource thousands of miles away is impractical. LatentAI, a company specializing in edge AI, helps solve this challenge by enabling real-time data processing at the source, delivering swift and efficient operations where they’re needed most.
“We recognized early on that the biggest challenge wasn’t just building AI models—it was making sure those models could perform under extreme conditions,” says Jags Kandasamy, CEO and Co-founder of LatentAI. “Cloud computing has its place, but in these dynamic environments, you don’t have the luxury of waiting. You need AI that works where the action is.”
The Challenge of Industrial Edge AI: Real-Time, On-Site Processing
Imagine an oil rig, where analog gauges must be constantly monitored for safety. Data needs to be collected and processed immediately to prevent costly equipment failures or dangerous incidents. The problem? These rigs are moving farther offshore, limiting high-bandwidth internet connection. Sending that data to the cloud for analysis could introduce delays leading to failure or increased downtime.
These are the kinds of environments where industrial edge AI thrives—places where real-time adaptation and decision-making can make or break an operation.
“AI doesn’t work in isolation. AI models are only as good as the hardware they run on.”
Jags Kandasamy, CEO and Co-founder of LatentAI
Why Industrial Edge AI Needs Purpose-Built Hardware
Industrial edge AI requires the right infrastructure to function, especially in environments with physical and computational challenges. As mentioned earlier, cloud solutions are often impractical in the field especially for rugged, resource-constrained environments. LatentAI solves that constraint with its technology to design, optimize and deploy the most efficient AI models on purpose-built hardware at the edge. Industrial edge solutions require processing at the data, not just near the data.
Using the LatentAI Efficient Inference Platform (LEIP) technology, the AI model storage and computational demands are reduced through compression and compilation. This process makes the models smaller, faster and more energy-efficient— without sacrificing accuracy.
LEIP also optimizes the model to improve inference speed using all the hardware acceleration available. This allows the AI model to run on standard CPU-only hardware, or if a GPU is available, LEIP takes care of the compilation process to generate an AI runtime for on-site inference. LEIP runs on traditional x86-based workstation systems with NVIDIA RTX Ada Generation GPUs and the NVIDIA IGX Orin and NVIDIA Jetson platforms for industrial edge AI applications.
If you wanted to run it locally, LatentAI has tested LEIP technology on the Precision 5690 with the NVIDIA RTX 5000 Ada Generation Laptop GPU and found it performed with most accurate (slowest) recipe, Yolov8, up to 300 times faster than CPU training.
“People often start with the AI model,” says Kandasamy, “but the model isn’t much use if you can’t deploy it. We ensure models are optimized for the devices they’ll run on.”
By focusing on model optimization on hardware, LatentAI enables efficient AI deployment in real-world conditions.
Bringing AI to the Edge
On this, Kandasamy points out, “the key to successful AI deployment is about pairing the best model with the right devices.” And in environments like oil fields or factory floors, the hardware needs to be as capable as the software.
That’s where rugged devices find their niche. Classified into semi-rugged or fully rugged, these devices are designed to be highly durable for extreme conditions. Fully rugged devices, in particular, are built to meet tough conditions without compromising functionality. Devices like the Pro Rugged 13, Latitude 7230 Rugged Extreme Tablet, and Latitude 7030 Rugged Extreme Tablet provide laptop and tablet solutions with a fully rugged rating.
This means that these devices have been tested under MIL-STD 810G standards for drop, shock and vibrations as well as an IP65 rating for water and dust-tight sealing. Whilst on an oil rig or other harsh environments, having other features like glove-compatible touchpads and screens with up to 1400 nits of backlight to ensure viewability in direct sunlight, dual hot-swappable batteries and a high-powered CPU setup helps make sure your tech meets your situation’s needs.
In tandem with LatentAI’s LEIP and edge processing capabilities, these rugged laptops and tablets can reduce your training times to minutes instead of months.
A Collaboration for the Future of Industrial Edge AI
Working with Dell and NVIDIA, LatentAI maximizes the computational capabilities of the hardware, required for on-site environments where conventional solutions fall short. As Kandasamy says, “This isn’t just about AI models. It’s about how we make them work in the real world, right where they’re needed. …The exciting part for me is that we are at the forefront of AI. …I draw a parallel to what we were in the early days of the internet.”
The internet has transformed industries. Now, edge AI is redefining how real-time decisions are made on the ground. LatentAI is advancing solutions that move beyond cloud infrastructures to process data locally, even in the most remote or hazardous conditions.
