Get ready to discover why Edge AI (or local intelligence) is the key to operational and maintenance efficiency.

1. Defining concepts: What is Edge AI?

Local AI or Edge AI is the union of two powerful concepts: embedded machine learning and TinyML ( tiny machine learning ) or SLM ( small language model ).

What does "embedded" mean?

  • Embedded systems: These are the computers that control the electronics of all types of physical devices.
  • Embedded software: This is the software that runs within them.
  • Characteristics: They are usually designed to perform a specific and dedicated task, with limited resources (such as memory).
  • The art of programming them: It consists of overcoming these limitations by writing software that performs the required task while making the most of the available resources. > Source: Edge AI, Daniel Situnayake et al.

The Internet of Things (IoT)

These are all internet-connected devices that create and consume data. This includes everything from industrial and domestic sensors to urban sensors. These sensors provide different types of data: numerical, image, sound, binary, etc.

2. Putting the Pieces Together: Edge Computing

All these IoT devices are embedded systems, as they contain microprocessors with specific software. Since they are located at the "edge" of the network (where the physical action occurs), we call them edge devices .

Performing calculations directly on these devices is known as Edge Computing . * The "edge" is not a single location, but a large region. * The devices can communicate with each other and with remote servers.

Why are they important for maintenance?

Being at the edge offers critical benefits: 1. Data origin: It's our link between the internet and the physical world. 2. Exclusive access: They have access to data that no one else has in real time. 3. Decision-making power: Equipping these devices with AI allows for zero-latency decision-making, without sending data over the internet and without monthly cloud costs. 4. Security: Guaranteed privacy by not exposing data.

3. Sophie's Story: From Chaos to Efficiency

The Problem: Reactive Maintenance

Sofia, a maintenance manager at a factory, lived putting out fires. * Daily nightmares: Unexpected failures in machines, motors, and solenoid valves. Production lines stopped and elevators stuck with people inside. * Costs: Reactive maintenance was expensive, unsafe, and stressful.

The failed attempt with the Cloud: They tried to modernize with traditional IoT (sensors sending data to the cloud), but new problems arose: * Unacceptable latency: Sending terabytes of raw data prevented real-time diagnostics. * Privacy and Costs: Concerns about the security of operational data and skyrocketing bills for bandwidth and data storage that was mostly "noise."

The Solution: Edgemant Conauti to the Rescue

Sofia discovered Edgemant Conauti , an IoT + TinyML + SLM Agents suite specializing in Local Cognitive Predictive Maintenance.

It wasn't just another technology; it was a paradigm shift. Edgemant brings AI directly to microcontrollers and stand-alone computers (like Raspberry Pi), achieving: * Cost reduction. * Complete privacy. * Data security.

4. Tangible Benefits of Edgemant Conauti

Imagine achieving this in your operation:

⚡ Real-Time Predictive Maintenance

Sensors with TinyML and SLM Agents continuously monitor vibrations, temperature, and acoustics. * Detection: The system detects even the slightest deviation instantly. * Result: Failures can be predicted days in advance. * Impact: Cloud expenses can be reduced by 65% , and uptime can be improved.

🔍 Accurate and Local Diagnosis

By processing data on the device, cloud latency is eliminated. * Root Cause: Identify the problem before it becomes a costly breakdown. * Impact: Reduce downtime by up to 40% .

📈 Optimized Management

Sofia transitioned from time-based maintenance to condition- based maintenance. * Reduction in unplanned downtime: Up to 50% . * Savings in maintenance costs: Between 10% and 40% . * Extended equipment lifespan and improved workplace safety.

5. Conclusion

Edgemant Conauti enables devices to operate autonomously, even in environments with limited or no connectivity, making it ideal for buildings or industries in remote or critical locations. By processing data locally, you minimize the exposure of sensitive information and eliminate cloud costs.

👉 We invite you to try the demo or request a visit at: https://edgemant.conauti.com/