By Sector

It feels like AI is everywhere. Yet deploying it isn’t always simple.

You’ll find AI managing security feeds, tracking stock levels in real time, and powering predictive tools in everything from hospitals to manufacturing plants. But getting those AI systems up and running in the real world is rarely plug-and-play.

For many businesses, the challenge starts with computing infrastructure. Cloud dependency can slow things down, especially when data volumes are high or connectivity is limited. Moving large datasets back and forth burns bandwidth, adds latency, and introduces privacy concerns.

That’s where edge computing makes life easier. By placing the processing closer to the data source, AI can run directly on-site. This speeds up response times, reduces strain on cloud services, and keeps sensitive information local. The result is a system that’s faster, more responsive, and a whole lot easier to scale.

Choosing the right use case for edge AI

Running AI at the edge works best when timing, location, or privacy matter. Think of a retail chain that wants to adjust digital signage based on real-time in-store traffic. Or a manufacturing facility that needs to spot product defects in real time. In both cases, sending everything to the cloud adds friction. Processing it locally clears the bottleneck.

Good edge use cases usually share a few traits. There’s a clear input, like video footage or sensor data. The model needs to make quick decisions, like flagging a safety issue or detecting low stock. And ideally, you want to keep that data close for compliance or speed.

Let’s say you’re deploying AI-driven cameras across multiple warehouses. Instead of routing all that footage through a central server, you install compact edge systems on site. Something like Simply NUC’s extremeEDGE Servers™. They’re fanless, small enough to fit into tight spaces, and powerful enough to run inference models directly at the data source. That way, alerts go out instantly when something’s off, no cloud delay, no added bandwidth.

Picking the right use case helps you move fast without overengineering the solution. Start where edge computing adds the most value. Then scale from there.

Simplifying data processing at the edge

Raw data is messy. Inconsistent formats, duplicate entries, missing fields are the usual suspects. Before it can power anything meaningful, that data needs cleaning and shaping. Traditionally, that meant pushing everything to a cloud platform or central server. But that approach eats up bandwidth and delays results.

Running pre-processing tasks locally trims out a lot of the noise before it travels anywhere. Sensors can flag relevant events. Cameras can compress and categorize footage. Only the essential data gets stored or sent up for long-term analysis.

That’s where the right edge device makes all the difference.

By processing data locally you’re improving accuracy, reducing cloud costs, and setting the stage for more reliable AI results down the line. It’s a cleaner input, and cleaner input leads to better decisions.

Supporting AI frameworks at the edge

Running AI in the real world means working with frameworks your team already trusts, such as TensorFlow, PyTorch, OpenVINO, and others. These tools are powerful, but they also need hardware that can keep up. It’s one thing to train a model in the cloud. It’s another to run it efficiently on a device sitting behind a screen or embedded in a machine.

That’s why hardware matters. You need edge systems that handle those frameworks without slowing down or overheating. Systems that support GPU acceleration, fast storage, and flexible operating environments.

Devices like the NUC 15 Pro (Cyber Canyon) and Mill Canyon are a good fit for AI inference tasks running on-site. Whether you’re classifying images, tracking objects, or parsing text, these systems can keep models running smoothly, even across multiple endpoints.

And if your deployment is in a harsh environment or remote, the extremeEDGE Servers™ give you the same support for modern frameworks but in a fanless, sealed form factor. That’s ideal for environments where dust, vibration, or heat would knock out a typical box.

Real-world deployment made manageable

AI models might train well in the lab, but deploying them in the real world comes with its own set of challenges. You’re often working with limited space, inconsistent power, or environmental factors like dust, vibration, and heat. Add to that the need to scale across multiple locations, and things can quickly get complicated.

Edge computing helps by removing some of that complexity. Compact devices can be installed closer to the data source, eliminating the need for bulky infrastructure or constant cloud connectivity. That’s especially useful in places like manufacturing sites, retail displays, or mobile service units where you might not have the luxury of a traditional server setup.

Remote management also plays a key role. When devices are spread across dozens, or even hundreds of sites, having the ability to monitor, update, and troubleshoot them from a central location saves time and reduces downtime. Preconfiguring devices before deployment can streamline setup, and once installed, systems can get to work with minimal hands-on support.

In practice, a well-planned edge deployment makes it easier to roll out AI applications across your organization. It brings control closer to the point of use and reduces the overhead that often slows things down. That keeps your team focused on the insights AI delivers, rather than the infrastructure behind it.

Ensuring privacy, compliance, and control

In industries like healthcare, finance, and public services, how data is handled can be just as important as what it’s used for. Regulations around privacy, storage, and security should be baked into how these sectors operate. That means your AI setup needs to respect where data lives and how it moves.

Edge computing makes this more manageable. When data is processed on site, it doesn’t have to be transmitted to external servers unless there's a good reason. That reduces exposure and helps you stay aligned with data sovereignty rules and internal security policies.

You also gain more control over encryption, access, and device monitoring. Instead of relying on broad cloud controls, local systems can be locked down to fit the environment. Whether it’s a device in a hospital, a transit hub, or a regional retail branch, local compute helps keep sensitive information where it belongs.

From a compliance standpoint, this setup is easier to audit and explain. Data stays closer to its source, and you’re better equipped to apply the right protections at each location. It’s not about removing risk entirely, but reducing it in a way that feels deliberate, measurable, and practical.

Interested in cybersecurity and compliance? Read about the NIS2 requirements.

Delivering real-world results and ROI

AI is deployed to solve problems, improve efficiency, and unlock new ways of working. But for that investment to pay off, the system around it needs to be just as smart as the model itself. Edge computing helps deliver those results by simplifying everything that happens before and after the AI makes a decision.

A logistics company wants to track package movement inside their distribution centers. With AI-powered cameras and sensors installed on site, packages can be scanned, logged, and rerouted in real time. Instead of sending raw video to the cloud for processing, the system runs those analytics at the edge. That means lower bandwidth costs, quicker reaction times, and less infrastructure to manage.

The result?

Fewer delays, better tracking, and a smoother customer experience. And the payoff doesn’t stop there. By keeping the compute local, the company also reduces dependency on outside systems. That translates into more predictable performance, more control over uptime, and fewer surprises during peak hours.

This kind of return on investment isn’t limited to warehouses. Retail environments can use edge AI to monitor stock levels, optimize display content, and track customer flow through a store. In healthcare, edge systems can assist with diagnostics or patient monitoring, helping clinicians act faster without offloading sensitive data to the cloud.

What ties all these use cases together is the ability to move from proof-of-concept to production without overcomplicating the rollout. Edge computing clears a path to value by handling AI where it happens. It removes roadblocks, trims unnecessary layers, and keeps decision-making close to the action. That’s what makes it a practical, repeatable choice for teams looking to make AI part of their everyday operations.

Useful Resources:

Edge server

Edge computing solutions

Edge computing platform

Edge devices

Edge computing examples

Cloud vs edge computing

Edge computing and AI
Fraud detection machine learning

Finding the right approach for your edge deployment

When building out an edge computing strategy, one of the biggest questions is where the data should go. Should everything be routed through a single server? Or should the processing happen on-site, closer to where the data is created?

The answer depends on your environment. Centralized computing can work well in stable, controlled settings. But when you’re dealing with real-time decisions, multiple locations, or limited connectivity, a distributed model often performs better.

We build edge hardware to support both scenarios. Whether you're centralizing data for streamlined operations or distributing it across smart devices in the field, there’s a setup that fits. Understanding how these models differ, and when each one makes sense, is the first step to making your edge environment more efficient, scalable, and future-ready.

What is a distributed computing model?

Distributed computing spreads the workload across multiple devices or nodes, rather than relying on one central system. Each node has its own processing power and storage, allowing it to run tasks independently while still communicating with the rest of the network.

This setup brings a few key benefits:

• It reduces latency, because data can be processed right where it's generated.
• It also increases system reliability, if one device fails, the others keep working.
• It scales easily, you can add more nodes as your system grows, without overhauling your infrastructure.

A great example is a network of cameras in a smart city. Instead of sending all video footage to a central server, each camera can run video analytics locally. That saves bandwidth and gives operators faster access to insights like identifying congestion or spotting safety issues in real time.

Devices like Simply NUC’s extremeEDGE Servers™ are built for exactly this kind of setup. They're compact, energy-efficient, and rugged enough for remote or outdoor environments. And with remote management tools included, you can keep tabs on every node without being on-site.

When centralized computing still makes sense

Distributed systems are powerful, but centralized computing still plays a valuable role, especially when your environment is stable, connectivity is strong, and most of the processing can be handled in one place.

In a centralized computing model, a single server takes on the heavy lifting. Client devices send data to the server, which processes it and sends back instructions or results. This setup is often used in office networks, internal applications, or any situation where a controlled hub can manage the workload efficiently.

Centralized systems are typically easier to maintain. With one core location to manage software updates, security protocols, and backups, your IT team spends less time coordinating across multiple devices. This can be a smart choice when the focus is on simplicity and predictability.

Simply NUC offers several compact, high-performance options that work well with centralized environments. The Mill Canyon NUC 14 Essential, for instance, is ideal for applications like retail hubs, streaming setups, and collaboration spaces. It’s a cost-effective system that delivers solid compute power and support for up to three displays, all in a small form factor that’s easy to install and manage.

For more performance-intensive tasks, the NUC 15 Pro (Cyber Canyon) offers faster processing, enhanced graphics, and broad OS compatibility. Ideal for hosting digital signage software, managing connected point-of-sale terminals, or overseeing employee workstations, these devices give you central control with enough flexibility to scale.

Centralized computing works best when your data flow is predictable and your network is reliable. With the right hardware in place, you get the performance and stability needed to keep everything running smoothly.

Comparing architectures: Centralized vs distributed for edge

Choosing between centralized and distributed computing comes down to understanding what your system needs to do, where it needs to do it, and how quickly it needs to respond.

Centralized architecture:

• One core server handles all data processing
• Easier to maintain and update from a single location
• Lower hardware cost at the edge, since endpoints rely on the central server
• Best suited for office environments, internal systems, or any application with strong, consistent network access

Distributed architecture:

• Multiple nodes process data independently, closer to the data source
• Reduces latency and enables real-time decisions on site
• More resilient to outages or local failures
• Scales more easily across multiple locations or regions

For edge computing, distributed systems often provide better flexibility, especially when you're dealing with real-time intelligence, limited connectivity, or remote management challenges.

For example, a network of smart kiosks or manufacturing sensors can’t afford to pause every time there's a delay reaching the main server. They need to respond instantly, and that’s where processing data locally really shines.

That said, many businesses find a middle ground with a hybrid edge strategy. You might centralize certain tasks, like long-term storage or analytics dashboards, while distributing the processing of time-sensitive tasks to devices in the field.

How Simply NUC supports both models

Every edge strategy is different. Some businesses need the simplicity of centralized control. Others rely on local decision-making across multiple sites. And many fall somewhere in between. That’s why Simply NUC designs systems that can support both approaches, so you’re not locked into one way of working.

If your project calls for distributed computing, devices like the extremeEDGE Servers™ are purpose-built for the job. They deliver robust performance at the data source, whether that’s a warehouse floor, roadside cabinet, or field unit in a remote location. With fanless designs and extended temperature tolerance, they hold up in demanding environments. And with built-in remote management features, you can deploy and support them without needing a technician on-site.

For more centralized setups, where processing is handled in one location and edge devices act as terminals or data collectors, we offer compact systems like Mill Canyon and Cyber Canyon. These platforms are ideal for retail spaces, signage networks, or collaboration hubs. You still get plenty of computing power, flexible storage options, and support for modern operating systems, but in a form factor that’s easy to install, manage, and scale.

We also know that many businesses want to blend both models. That’s why Simply NUC devices are configurable. Whether you need extra I/O, custom OS images, or specialized mounting options, we can tailor each system to match your infrastructure and workload.

Useful Resources:

Edge server

IOT edge devices

Edge computing solutions

Edge computing in manufacturing

Edge computing platform

Edge devices

Edge computing for retail

Edge computing in healthcare

Edge computing examples

Cloud vs edge computing

Edge computing in Financial Services

Edge computing and AI

So your business has made the smart choice that your IT infrastructure needs faster decision-making, while cutting-costs, and keeping sensitive data secure.

Setting up an edge computing environment comes with a lot of decisions.

One of the biggest? Choosing the right edge server.

With so many options out there, and so many variables depending on where and how you’re deploying, it helps to have a clear list of what really matters.

Whether you’re managing data from factory sensors, rolling out smart signage, or powering real-time AI at the edge, here’s a practical checklist to help guide your next investment.

1. Match performance to your workload

Not every use case demands high-end specs, but if you’re running AI models, analyzing data, or supporting multiple applications at once, your edge server needs the computing power to keep up. Look for systems that handle local processing with minimal delay and can support the frameworks or software you plan to use.

When it comes to performance, it's important to keep in mind that not all workloads are created equal. Certain tasks may require more computing power and resources than others, such as AI models or data analysis. In these cases, it's crucial to have an edge server with the capabilities to handle these demanding tasks without experiencing delays or bottlenecking.

Another consideration is the ability for your server to support various frameworks and software. Make sure to research and choose a system that is compatible with the specific tools and applications you plan on using. This will ensure smooth operation and optimal performance.

Bonus tip: If you’re deploying across different environments, go for a setup that can scale so you don’t outgrow it too soon.

2. Ruggedness for real-world environments

Edge servers often live in less-than-perfect conditions. Think heat, dust, vibration, or lack of ventilation. Make sure your hardware is ready for it. Look for fanless, sealed designs and a wide thermal tolerance. A rugged build helps maintain uptime and reduces maintenance headaches in the field.

Use case: Edge AI in a factory setting

Imagine a production line with robotic arms, sensors, and AI-powered cameras working together to spot defects in real time. These systems can’t afford to pause every time the temperature spikes or the equipment kicks up dust. You need a server that can keep up. Simply NUC’s extremeEDGE Servers™ are a great fit here, with models purpose-built for industrial and outdoor settings.

They’re designed to run 24/7 in tough environments with no moving parts to fail and no vents to clog. Even when placed right next to active machinery, they stay cool, stable, and efficient.

Sincethey’re compact and mountable, you can install them exactly where the data source is, no need to route everything back to a central location. That keeps real-time processing smooth and simplifies your overall setup.

3. Compact size, without compromising performance

Space can be tight. From behind-the-scenes kiosks to mobile control units, many edge setups don’t leave room for bulky hardware. Compact servers that don’t compromise on performance help you get more done in less space.

Devices like the Mill Canyon NUC 14 Essential offer everyday reliability in a tiny footprint, perfect for light edge applications like digital signage or point-of-sale displays.

4. Remote management options

Once your systems are deployed, managing them should be straightforward, even from a distance. That’s where remote management tools come in. Features like side-band access, remote updates, and full system visibility can save your IT team time and travel.

5. Connectivity and I/O that fits your setup

Make sure the server can connect easily to the other parts of your system. That means checking the number and type of USB ports, display outputs, network options, and expansion slots. If you’re connecting cameras, sensors, or local displays, your server needs the right I/O mix to handle it all without extra adapters.

6. Security built in

When edge servers process sensitive data, security can’t be an afterthought. Look for hardware-based encryption support, secure boot options, and compatibility with trusted operating systems. This is especially important if your devices are in public or shared spaces.

7. Value that aligns with your goals

Not every project calls for premium pricing. Sometimes you need a lower price device that delivers maximum efficiency for a focused task. Other times, it's worth spending more to future-proof your setup or consolidate multiple roles into a single unit.

Simply NUC offers a range of edge servers tailored to different needs, so you can get what you actually need, not just what’s on the spec sheet.

For expert advice on the right edge-enabled device for your business, contact us today.

Useful Resources:

Edge computing solutions

Edge computing in manufacturing

Edge computing platform

Edge devices

Edge computing for retail

Edge computing in healthcare

Edge computing examples

Edge computing in financial services

Edge computing and AI

As more industries embrace automation, AI, and real-time analytics, the way we process data is shifting… and shifting fast. Edge computing allows you to process data closer to where it's generated, helping systems respond faster and more efficiently. Results include less lag, reduced bandwidth use, and better control over your operations.

Rather than sending everything to a central cloud, edge computing devices handle data locally. This makes them a smart fit for environments that rely on real-time decision-making, like factories, healthcare facilities, or smart city infrastructure.

Smart businesses are paying attention. The global market for edge computing devices is projected to hit over $43 billion by 2030, driven by growing demand for speed, security, and smarter data handling. Whether you're managing remote machinery or deploying AI at the edge, the right device can make all the difference.

Here’s how to pick the best edge computing device for your IT infrastructure.

Classify edge computing types

Not all edge computing looks the same. In fact, choosing the right device often comes down to understanding where and how it’ll be used.

Let’s start with location-based categories. You’ve got:

• Enterprise edge, which handles data near corporate servers or data centers
• Branch edge, ideal for remote offices or satellite facilities
• Mobile edge, often used in moving environments like vehicles or public transit systems

Then there’s the device-based breakdown, which looks at what kind of tech is actually doing the work:

• Device edge processes data directly on endpoints, like sensors or cameras
• Sensor edge is more specialized, built into IoT sensors for ultra-fast reactions
• Compute edge offers serious processing power just outside the cloud
• Cloud edge blends edge responsiveness with cloud scalability

Each type plays a role in making systems smarter and more responsive. For example, a sensor edge device might help a factory floor detect equipment failures instantly, while an edge server could support machine learning on-site at a logistics hub.

Understanding these categories helps match the right tool to the job. Whether you’re automating warehouses, building smarter cities, or rolling out connected vehicles, there’s an edge device designed to fit that use case.

Edge AI and computer vision

Edge AI changes how data gets used by bringing intelligence closer to where it’s created. Instead of relying on distant servers, these systems handle tasks right on the device, whether that’s a camera, sensor, or mobile unit. That means real-time insights with fewer delays and less dependence on cloud computing.

Let’s say you’re tracking shopper behavior with in store analytics, or keeping tabs on factory machinery using IoT devices. In both cases, data is processed locally, which helps reduce latency and improves control over how sensitive data is handled. It’s a smarter way to respond fast and stay secure.

Many of these edge AI applications use machine learning models or neural networks to spot patterns, flag issues, or respond to voice and image input. And when you’re working in remote locations or across multiple sites, the ability to act instantly, without sending everything back to the cloud, makes all the difference.

That’s where hardware comes in. To support this kind of real time processing, you need a setup with enough muscle to run complex AI models while staying compact and energy efficient.

You also need the flexibility to manage updates and store data reliably. A well-matched edge device gives you that balance of powerful performance and a cost effective solution.

While the edge handles immediate processing, the cloud still plays a role. Major cloud vendors like Microsoft Azure are building out tools to help businesses run machine learning workloads closer to the network edge. With options to sync across systems and integrate with existing operating environments, edge and cloud can work hand in hand to support smarter, more responsive operations.

Free ebook Cloud vs. Edge – which is right for your business?

Top edge computing devices

Choosing the right edge computing device is about finding a setup that works smoothly with your environment, whether you're managing devices across multiple locations, deploying AI models at the source of data, or scaling up with remote teams.

Let’s explore some options to give you more flexibility and maximum efficiency.

NVIDIA Jetson Xavier NX
Popular in robotics and automation, this board delivers strong computing power for running AI at the edge. It’s compact and handles real time processing well, especially for vision-based applications.

extremeEDGE Servers™
If you need serious local processing in a rugged, compact format, extreme edge is built for the job. Models like the EE-1000, EE-2000, and EE-3000 are purpose-built for remote installations, with passive cooling, extended temperature tolerance, and BMC-enabled remote management. That means less on-site maintenance and full visibility, even when your systems are off the grid.

Google Coral Dev Board
This offers a cost effective solution for lightweight AI tasks, like basic edge deployments on mobile devices or in smart home setups. It's fast, but not designed for more complex environments.

Mill Canyon – NUC 14 Essential
The NUC 14 Essential Mill Canyon is a smart choice for everyday edge applications. Built on the latest Intel® N-series processors, it delivers reliable performance in a small, flexible form factor. It’s well-suited to retail kiosks, digital signage, collaborative workspaces, and streaming setups. With support for up to three displays and a range of modern connectivity options, Mill Canyon offers a solid mix of quality, security, and usability, at a price that fits into more projects.

Find out more about Mill Canyon – NUC 14 Essential.

Raspberry Pi 4
A great prototyping tool or budget option for light workloads. It supports multiple operating systems and works well for testing basic computing models, but lacks the muscle and durability for enterprise-scale tasks.

NUC 15 Pro Cyber Canyon
When your edge use case leans toward commercial environments, like in store analytics, remote signage, or AI model testing in the field, the NUC 15 Pro Cyber Canyon strikes a balance between performance and value. It supports a range of storage options, integrates easily with Windows or Linux, and runs smoothly alongside Azure services or other cloud computing tools.

Find out more about NUC 15 Pro Cyber Canyon.

Every use case is different. That’s why having the right mix of energy efficient devices, compact builds, and scalable performance matters.

Data Sovereignty

When data is created, stored, and processed, the question of who controls it becomes a legal and operational priority.

For industries working with sensitive data, especially across multiple locations, it’s critical to know where that data lives and how it’s handled. With edge computing, data can stay closer to the source of data, which not only improves speed and efficiency but also helps organizations stay compliant with regional regulations.

Take smart city deployments, for example. Traffic cameras, sensors, and public systems generate constant streams of information. Processing data locally using edge devices ensures that real time decision-making happens quickly, without pushing everything to the cloud. It also means that the data never leaves the country or jurisdiction unless you want it to.

Maintaining control doesn’t have to slow you down. With the right setup, you can move fast and stay compliant, all while keeping your data exactly where it needs to be.

Edge Computing Examples

Edge computing is already working behind the scenes in ways you might not expect. From improving factory uptime to helping cities run more smoothly, it’s showing up wherever fast, local decisions matter.

Smart city systems are a good example. Devices like traffic cameras, environmental sensors, and public transit monitors generate constant streams of information. By processing data locally, cities can react in real time, adjusting signals, rerouting vehicles, or alerting emergency services faster than if the data had to travel to a distant cloud.

In industrial automation, edge computing helps businesses keep tabs on equipment performance. Sensors can flag maintenance issues before they lead to downtime. Since the data never has to leave the facility, decisions get made quickly and securely.

Healthcare is another area seeing big gains. Portable medical devices and diagnostic tools now use local processing to deliver faster results. This reduces reliance on cloud systems and gives healthcare providers more control over how sensitive data is handled.

Retail environments are also benefiting from edge deployments. Stores use cameras and sensors to gather data about customer behavior. This allows for real time intelligence around stock levels, queue management, and in store analytics, all without sending every frame or reading to the cloud.

Edge computing fits where real time insights are critical and where bandwidth, latency, or privacy concerns limit the use of traditional cloud models. The flexibility it offers means organizations can adapt to local needs while still connecting to broader systems when needed.

AI Applications in Edge Computing

Artificial intelligence is becoming more accessible, and edge computing plays a big part in that. By keeping processing close to where data is created, AI can work faster and more securely. This setup is ideal for situations where quick decisions matter, and where sending data to the cloud isn't practical.

Think about a warehouse using machine learning models to track inventory. With local processing, scanners and cameras can instantly spot discrepancies or flag safety issues, no internet lag, no waiting for a cloud response.

In smart city environments, edge AI helps manage traffic flow, monitor public spaces, and optimize energy use. Because the data is processed on-site, systems can react in the moment. That reduces strain on the network and improves responsiveness.

Edge AI is also making waves in autonomous vehicles. These machines rely on real-time input from sensors and cameras. With edge devices managing the workload, cars can interpret and respond to their surroundings without depending on remote servers.

In healthcare, AI tools are now analysing patient data on portable diagnostic devices. Doctors get faster results, and patient information stays closer to the source, helping with privacy and compliance.

To support these types of applications, you need compact systems that deliver strong computing power, reliable storage options, and flexible integration with existing operating systems. Devices from Simply NUC are designed to handle this kind of demand, offering scalable performance for a wide range of edge AI scenarios.

Useful Resources:

Edge computing solutions

Edge computing in manufacturing

Edge computing platform

Edge devices

Edge computing for retail

Edge computing in healthcare

Edge computing examples

Edge computing in financial services

Edge computing and AI

With Windows 10 support ending on October 14, 2025, many IT teams are facing a big question: are your current computing devices ready for Windows 11 Pro?

The feedback we’re getting is mostly… no!

The answer isn’t always straightforward. Not all PCs currently running Windows 10 meet the system requirements for an upgrade, and figuring out what to keep, what to refresh, and how to make the transition without disruption takes time and technical insight.

Luckily, that’s where Simply NUC comes in.

A smarter way to assess your systems

We understand that no two IT environments are the same, so we don’t just recommend a blanket upgrade, we help you to evaluate what you already have.

Our team can review your current hardware, identify which systems are compatible for Windows 11, and flag any devices that may need to be refreshed. If you’re a Simply NUC customer already, we’ll use your existing device list to streamline the process and offer a tailored recommendation.

No guesswork, no unnecessary replacements, just a clear view of what’s next.

Which hardware should you use for Windows 11 Pro?

We offer a full range of Windows 11-compatible systems, from everyday productivity machines to high-performance edge devices. Some of our most popular models include:

• NUC 15 Pro (Cyber Canyon): Designed for AI development, machine learning, and content creation.
• NUC 14 Essential (Mill Canyon): A cost-effective option for basic workflows, bulk deployments, and signage.
• Onyx and Onyx Pro: High-speed, secure, and customizable systems built for multitasking and heavier workloads.
• Moonstone: Powerful, modern performance for business and enterprise use.
• extremeEDGE Servers™: Compact, rugged, and remotely manageable systems built for demanding edge environments.

Every model is built with reliability in mind and can be configured to suit your storage, productivity, and processing needs.

Upgrade support that goes beyond the box

Choosing new hardware is only part of the process. With Simply NUC, you also get:

• Personalized guidance to help match the right devices to your workload.
• Pre-configured OS options to save time and reduce setup headaches.
• Remote device management with our NANO-BMC technology, ideal for IT teams managing fleets across multiple locations.

Whether you’re upgrading a few PCs or rolling out Windows 11 across your organization, we’re here to keep it simple.

Don’t wait until the last minute

Windows 10 support ends soon. The sooner you understand your upgrade path, the better prepared you’ll be to avoid downtime, reduce security risks, and keep your team productive.

Check your compatibility and explore upgrade options today at simplynuc.com/windows-10-eos

Financial institutions are under pressure to know exactly who they’re working with. Whether it’s a high-street bank, a fintech startup, or a credit union, the goal is the same: make sure customers are who they say they are and keep an eye out for anything suspicious.

KYC and AML rules are what make that possible. They’ve been written into law in different ways depending on where you operate, but the thinking behind them is shared across the board. In the US, the Bank Secrecy Act lays out the expectations for customer checks, transaction monitoring, and reporting to FinCEN. The UK’s approach follows the Money Laundering Regulations, and EU countries align with the 6th Anti-Money Laundering Directive.

At a practical level, most institutions are expected to:

• Check and record customer identities
• Identify who’s really behind legal entities
• Monitor for transactions that don’t match the customer’s profile
• Keep records in case they need to be audited later
• Flag suspicious activity through the right channels

These checks are part of the day-to-day, baked into everything from onboarding to ongoing account management. Getting them right builds trust, avoids penalties, and makes it easier to step in when something’s not quite right.

What compliance looks like in practice

Most financial institutions already have processes in place to meet KYC and AML rules, but keeping those processes both efficient, secure and up to date can be a challenge. There’s a lot to cover; identity checks, transaction monitoring, internal reporting, and regulators expect it all to be documented, traceable, and ready to review at any time.

A Customer Identification Program (CIP) is usually the starting point. This is where you verify a person’s name, date of birth, address, and identification number. If you're working with businesses, you’ll also need to identify the individuals behind the company, the beneficial owners, and make sure they check out.

Once a customer is onboarded, that’s not the end of the story. Compliance teams need to keep monitoring activity in the background. Unusual transfers, strange login patterns, or inconsistent account usage could all trigger a closer look. When something seems off, you’re expected to record it, review it, and if needed, submit a report to the right authority.

It doesn’t stop there. Records need to be stored, sometimes for several years, and teams are expected to stay up to date with internal training, system reviews, and risk assessments. Depending on the size of the organisation, there’s usually a compliance officer responsible for making sure everything stays on track.

These are the real-world tasks that sit behind every regulation. They require accuracy, speed, and good judgement.

That’s where smarter tools and local processing start to add real value.

How edge AI supports KYC and AML compliance

When customer data is processed on-site instead of being sent to the cloud, financial institutions gain more control over how that data is handled. That’s especially important when it comes to identity checks and transaction monitoring, two of the most sensitive areas in any AML or KYC program.

Let’s take identity verification. With edge AI, edge devices at a local branch or remote service point can scan and match ID documents in real time. That means fewer delays, fewer data transfers, and fewer chances for private information to be intercepted or exposed. Facial recognition, signature matching, and document validation can all happen locally, with results ready in seconds.

It also helps with ongoing monitoring. Instead of pushing every transaction to a central system, financial teams can run basic anomaly detection right where the activity takes place. If something doesn’t look right, for example a transfer breaks a known pattern, or a login happens from an unusual location, that flag can be raised straight away. There's no need to wait for it to be picked up hours later in a cloud-based batch.

This kind of setup is especially useful in places where connectivity isn’t always stable. Pop-up banking units, rural branches, and mobile service points often struggle with reliable internet access. With local edge devices, teams can keep services running and data protected even when the network drops out.

All of this makes the job easier for compliance teams. They get quicker insights, better data security, and more confidence that the right checks are happening at the right time, without relying on a constant connection to central infrastructure.

Making life easier for compliance teams

Compliance officers already juggle a lot, staying on top of regulations, making sure internal systems do what they’re supposed to, and keeping records that stand up to scrutiny. When the right tools are in place it helps reduce the risk of things slipping through the cracks.

On-device AI offers a few key advantages. First, it keeps sensitive information closer to the source. That means fewer handoffs, fewer gaps in visibility, and tighter control over how customer data is handled. It also speeds things up. Instead of waiting for data to be sent off and analysed somewhere else, compliance teams can act on insights immediately.

This is especially useful when handling alerts. If a flagged transaction or ID check can be reviewed quickly and locally, there’s less delay in responding. That responsiveness goes a long way, both in terms of customer trust and in staying aligned with what regulators expect.

Audit preparation is another area where edge systems help. Storing logs locally, maintaining secure records, and keeping reporting consistent across locations makes it easier to show that your policies are being followed. When it’s time to walk through those systems with an auditor, everything’s right there and ready.

It also means less back and forth with IT.

Edge deployments can be managed remotely, updates can be scheduled without disrupting day-to-day work, and the infrastructure can scale as needed without having to overhaul the entire setup.

The result? A setup that works with your compliance goals, not against them.

Choosing the right edge hardware for compliance work

Not every device is built to handle the demands of regulated environments. When sensitive data is being processed locally, the hardware needs to offer strong protection, consistent performance, and simple management across locations.

Small form factor PCs are a good place to start. Compact edge devices are easier to install across branch networks, kiosks, or mobile setups without reworking your infrastructure. They can sit quietly behind a counter, in a cabinet, or even inside a transport case without getting in the way.

Security features should be built in, not bolted on later. Look for devices that support encryption, secure boot, and hardware-level authentication. These features are often required to meet internal risk policies and external regulations.

Reliability matters too. If you’re running identity checks or transaction analysis on-site, downtime isn’t an option. Devices should be able to keep going without constant maintenance or manual updates. Support for remote monitoring and system health checks is also helpful, especially when IT teams are working across multiple locations.

Scalability is another key factor. Whether you’re rolling out ten systems or a hundred, it helps if setup is consistent and easy to repeat. Pre-configured units, centralized updates, and flexible I/O options all make it easier to tailor the deployment to your environment without reinventing the wheel each time.

When the hardware is the right fit, everything else becomes easier from performance to compliance to day-to-day operations.

Useful Resources

Edge Computing in Financial Services

Fraud detection machine learning

Fraud detection in banking

Fraud detection tools

Edge computing platform

Edge server

Financial institutions face a tricky balancing act. On one hand, they need to meet strict regulatory standards around how customer data is handled. On the other, they’re expected to deliver fast, seamless services without putting that data at risk.

Storing everything in the cloud isn’t always the answer.

Transmitting customer sensitive data back and forth can increase exposure, add latency, and complicate compliance.

That’s why more financial teams are turning to edge computing with built-in AI. By handling tasks locally, closer to where the data is generated, financial firms can keep things secure, stay aligned with data privacy laws, and react in real time.

At Simply NUC, we build compact, reliable computing systems that are designed for exactly this kind of environment. Whether it’s verifying customer identities, monitoring for fraud, or processing transactions in-branch, our edge-ready devices support smarter, faster, and safer financial operations.

Understanding financial compliance and data protection

Compliance in finance protects people’s data, keeping systems transparent, and making sure everything runs in line with national and international regulations. These rules, like GDPR, GLBA, and the Sarbanes-Oxley Act, set clear expectations for how financial information should be stored, accessed, and shared.

Doing business with the EU? Check out our NIS2 checklist

Data protection plays a big role in this. A single breach can lead to major fines and damage customer trust. That’s why financial institutions invest in things like encryption, access control, and routine audits. Compliance officers are the ones keeping all of this in check, working closely with IT and legal teams to stay ahead of risks and meet reporting standards.

But as systems grow more complex, and the pace of financial activity speeds up, traditional setups start to show their limits. That’s where edge computing can make a real difference. Processing sensitive data locally, on secure hardware, gives institutions tighter control and more confidence that they’re meeting the rules.

How edge AI supports compliance without slowing you down

When you're dealing with customer data, compliance and security can't be treated as afterthoughts. Regulations like GDPR and CCPA make it clear that financial data needs to be protected not just in storage, but while it's being processed too. That’s where edge AI can really help.

Instead of sending everything to the cloud for analysis, edge AI handles sensitive tasks locally.

• Think identity checks
• Document scanning
• Real-time transaction monitoring

Processing that information right where it’s captured helps financial teams avoid the risks that come with constant data transfers.

There's also a speed advantage.

Let’s say a customer is opening an account at a local branch; with edge-powered identity verification (running with the help of Simply NUC hardware… just a thought), that check can happen in real time, without relying on a distant server. It’s smoother for the customer, and safer for the institution.

Another big win is visibility. Keeping critical processes on-site makes it easier to control who has access to what. If something needs to be audited later, the data trail is clearer and the risk of missing logs or external breaches is lower.

Real-world use cases for edge AI in finance

Edge AI is already being used across financial services to solve practical challenges. It helps teams stay compliant, respond faster to threats, and manage customer data more securely, all without relying on constant cloud access.

1. Verifying customer identity (KYC)

Opening an account or applying for a loan means proving who you are. With edge AI, tasks like scanning ID documents, matching faces to photos, and checking for signs of tampering can be done locally. This avoids unnecessary data transfers and speeds up onboarding.

2. Spotting fraud as it happens

Patterns like repeated failed logins or unusual spending spikes can point to suspicious activity. By running fraud detection models locally, financial institutions can flag these events right away. It also keeps sensitive data within the environment where it was created, which supports privacy rules.

3. Supporting local branches and service points

Not every branch or kiosk has strong connectivity, but they’re still expected to meet the same compliance standards. Edge devices can handle reporting, access control, and even document processing directly on-site. This makes day-to-day operations more reliable and secure.

4. Handling data securely in remote or mobile setups

From pop-up banking units to rural service vans, many financial services now happen outside traditional offices. In these cases, edge computing gives teams a way to process and store customer data safely, even if the connection drops. Information can be uploaded later once a secure network is available.

Find out more about fraud detection.

Why edge hardware fits regulated environments

Working in a regulated space like finance means your tech choices matter. Systems need to be secure, easy to manage, and reliable enough to support critical tasks without interruption.

Who doesn’t want more control?

When you’re processing sensitive data on-site, you’re not relying on external servers or third-party cloud infrastructure. You know exactly where the data is, who has access to it, and how it’s being used. That can make audits simpler and reduce exposure to external threats.

Compact edge systems are also easier to deploy across different locations. Whether it’s a high-street branch, a temporary customer service point, or a remote office, a small, secure device can go wherever it’s needed without overhauling your setup. That flexibility is especially useful when regulations require you to keep services running consistently, even in areas with limited connectivity.

Security features

Devices built for edge computing often support encryption, secure boot processes, and other protections that help keep financial data locked down. These are all part of meeting the baseline requirements set out by data protection laws and financial regulators.

Managing a network of edge systems doesn’t have to be complicated either. With the right tools, IT teams can keep tabs on updates, monitor performance, and push changes remotely. That’s a big help when you’re working across multiple locations with limited on-site support.

In short, edge computing aligns naturally with what financial environments need: reliability, control, and built-in protection without slowing down operations.

Best practices for building edge into your compliance strategy

1) Start by pinpointing where sensitive data lives

Take a good look at the processes that involve personal or financial data, things like onboarding, ID verification, transaction monitoring, and audit reporting. Once those are mapped out, it becomes easier to spot which tasks could safely shift to local processing.

2) Loop in your legal, compliance, and IT teams early
Getting the right people involved from the start helps everyone stay on the same page. If compliance knows what’s changing, and IT understands what the rules require, you’ll avoid nasty surprises later on.

3) Make sure your internal policies reflect edge processing
If you're handling data locally, your policies and documentation should say so. That includes where logs are stored, how data flows are tracked, and how audit trails are maintained. You don’t want a gap between how things work in practice and how they’re written down.

4) Build your network to handle the unexpected
A good edge setup doesn’t fall apart when the internet goes down. Devices should be able to run independently when needed, then sync back to your central systems once everything’s connected again. That kind of resilience helps you stay compliant even when things go offline.

5) Lock things down and keep them up to date
Strong passwords and role-based access are just the beginning. Keep your systems patched and your firmware current. And if you’re offering remote access, make sure it’s secure and tightly controlled. These are small steps that go a long way.

6) Treat edge systems like part of your compliance perimeter
It’s easy to forget that edge devices need just as much attention as servers or workstations. Add them to your regular audits, run security checks on schedule, and make sure your team knows how these systems fit into the bigger picture. That shared understanding keeps your setup both secure and compliant.

Useful Resources:

Edge Computing in Financial Services

Fraud detection machine learning

Fraud detection in banking

Fraud detection tools

Edge computing platform

Edge server
Edge devices

Are you so attached to Windows 10 that you’re willing to risk security breaches, compliance failures, increased down time and all the IT maintenance costs that follow?

With Windows 10 support ending October 2025, many businesses are asking: is it worth upgrading to Windows 11 Pro?

The answer is yes.

Not just because it’s the newer system. Windows 11 Pro brings real, measurable improvements across speed, security, and ease of use. Paired with Simply NUC computing devices, it gives your business the kind of performance that helps people get more done, with less friction.

Here are the top five reasons the upgrade to Windows 11 makes sense.

1. It’s faster and more responsive

Windows 11 Pro is built to run smarter. From boot times to heavy multitasking, everything just moves more smoothly. In fact, businesses upgrading have seen:

• 42% faster completion of demanding workloads
• 50% improvement in workflow speeds

That means less time waiting and more time doing. Combine that with the performance and reliability of Simply NUC hardware, and you’ve got systems that actually keep up with the pace of your workday.

2. AI features that quietly help your team

Windows 11 Pro includes a range of AI-powered features that support productivity without getting in the way. Snap layouts make multitasking easier, the Start menu adapts to how you work, and system-level AI tools help with everything from search to focus.

The changes are subtle, but they add up to a smoother user experience, especially for teams juggling multiple applications and projects at once.

3. Security that starts from the inside

With threats becoming more sophisticated, see the UK retail giant Marks and Spencer’s recent cybersecurity breach, which could cost them £300 million.

Businesses need protection that’s built-in, not bolted on. Windows 11 Pro comes with advanced security features right out of the box, including hardware-based isolation, ransomware protection, and enhanced phishing safeguards.

The results speak for themselves: companies moving to Windows 11 Pro reported a 62% drop in security incidents. It’s peace of mind that doesn’t rely on patchwork fixes.

4. Managing devices is simpler

IT teams have enough to juggle already. Windows 11 Pro makes things easier with streamlined update tools, better remote management options, and improved compatibility with tools like Microsoft Intune and Azure Active Directory.

Pair that with Simply NUC’s flexible device configurations, and you’ve got a fleet that’s easier to deploy, monitor, and maintain, whether you’re managing five devices or five hundred.

5. It works with what you already have

Upgrading doesn’t mean starting from scratch. Windows 11 Pro is designed to support the apps and platforms your team already depends on. And if your existing hardware can’t take full advantage, Simply NUC can help you explore right-sized upgrades that deliver better value and long-term flexibility.

Get more from your next OS

Windows 11 Pro is a smarter, faster, more secure foundation for work. And with Simply NUC by your side, the migration to the latest OS to doesn’t have to be complicated.

Find out what your next step looks like at simplynuc.com/windows-10-eos

Trading doesn’t wait. A price shift, a spike in volume, or an unexpected headline can change everything in a moment. For traders, the difference between success and missed opportunity often comes down to speed and clarity.

That’s why more firms are turning to edge computing. By processing data right where it’s generated, on the trading floor, next to an exchange, or even inside a device, using edge computing solutions helps cut through the noise. It gives traders the information they need to make faster decisions, manage risk on the fly, and stay a step ahead in volatile markets.

Let’s take a closer look at how this shift is happening, and why it matters.

What smarter trading looks like

In today’s markets, reacting isn’t enough. Traders need systems that can adjust in real time, running analytics, flagging risks, and spotting trends as they unfold.

That kind of speed can’t come from a system that relies on distant data centers. When every millisecond matters, it makes sense to handle data locally. That’s where edge computing fits in.

A quick intro to edge computing

Edge computing is exactly what it sounds like… computing, at the edge. Instead of sending data off to a central server for processing, edge systems handle it closer to the source. That could be a trading terminal, a market feed handler, or a small server co-located with an exchange.

For trading teams, this cuts down latency and gives them access to real-time insights without waiting for cloud confirmation. The result is a system that feels faster, responds faster, and helps decisions happen with more precision.

How edge computing helps manage risk and reward

Faster trades, fewer delays

With edge computing, firms can analyze data and execute trades with almost no delay. That speed helps capture opportunities as they happen, not after they’ve passed.

Real-time risk analysis

Markets change fast. One moment a position looks stable, the next it’s out of balance. Edge systems can track volatility, exposure, and liquidity as it happens, making it easier to course-correct before a situation turns costly.

Smarter strategies on the fly

Edge computing enables reward-focused strategies to evolve in real time. Systems can assess patterns, respond to volume shifts, and adjust portfolios or order strategies based on live conditions.

Clearer decisions, backed by better data

When insights come in fast and without noise, decision-making improves. Traders get information when they need it, and not after a delay. This helps them act confidently, with a sharper understanding of what’s happening right now.

Where edge is being used in trading today

Dynamic portfolio adjustments

If a market condition shifts, or a threshold is hit, edge-enabled platforms can trigger an automatic rebalance or adjustment—without waiting on the cloud. This is especially useful in fast-moving environments where seconds count.

Predictive trend modeling

Edge systems can host models that blend historical and real-time data to forecast price movements or spot early signs of risk. The advantage is being able to take action before the market reacts.

Spotting fraud as it happens

Edge computing helps detect irregular activity, whether that’s an unusual trading pattern or a suspicious login attempt. Instead of waiting for centralized alerts, systems flag problems at the point of activity.

Improving order routing

Choosing where and how to route orders matters. Edge tools can analyze multiple routes in real time, choosing the one that offers the lowest cost or fastest fill. Over time, that adds up.

What makes this shift challenging

Cost and complexity

Edge computing requires new infrastructure including servers, hardware, and ongoing maintenance. For firms with global operations, that can get expensive.

Integration with older systems

Many trading platforms were built before edge was on the radar. Adding edge to the mix without disrupting existing systems takes careful planning.

Regulatory pressure

Any time financial data is handled differently, regulators take notice. Firms need to make sure local processing aligns with rules around transparency, privacy, and recordkeeping.

Security at every node

When data is handled in more places, each of those places needs to be secure. That means thinking beyond firewalls and looking closely at device-level protections and access controls.

What the future holds

Edge computing in trading is still gaining traction—but the direction is clear. Here’s what’s on the horizon:

• More AI and machine learning models running locally, powering smarter real-time analysis
• New platforms built for decentralized finance, relying on local processing for speed and privacy
• Better access to advanced tools, not just for institutions but also for individual traders
• Greener systems that reduce the energy draw of traditional infrastructure

These developments all point toward trading systems that are faster, more adaptive, and less dependent on central resources.

Trading will always carry risk. But with edge computing, traders gain a clearer, faster way to manage that risk while staying ready for the next opportunity. It’s a shift in infrastructure, but more importantly, it’s a shift in what’s possible.

If you’re exploring how edge technology can support your trading strategy, Simply NUC offers compact, powerful systems built for high-performance environments. Whether you’re optimizing order flow, building real-time risk models, or strengthening your infrastructure, we can help.

Let’s talk about where your edge starts.

Useful Resources

Fraud detection machine learning

Fraud detection in banking

Fraud detection tools

Edge computing platform

Edge server
Edge devices

In high-frequency trading, a tiny delay can cost you. A trade that arrives a millisecond late might as well not have arrived at all. This is a space where algorithms fight for position, and the fastest one often wins.

That’s why more trading firms are shifting their attention to edge computing. It allows systems to handle data close to where it’s created, cutting out delays that can make or break a decision. For high-frequency traders, this technical upgrade could be an important strategic move.

What makes high-frequency trading so demanding?

High-frequency trading, or HFT, is all about speed and volume. These systems look for small shifts in the market, make rapid decisions, and move huge amounts of capital, sometimes all within a few seconds.

To stay competitive, firms need to know what’s happening in the market immediately and act on it even faster. Any lag in data processing or order execution can hurt performance. That’s why many are turning to localized systems that remove unnecessary steps between data input and action.

A quick breakdown of edge computing

Using edge computing solutions involve processing data right where it’s generated. Instead of sending it to a central server or cloud for analysis, edge systems analyze and act on it locally.

In trading, that could mean running infrastructure inside the same building as a stock exchange. It could also mean putting processing hardware inside the office where decisions are being made.

The goal is to shorten the distance between the market and the system that reacts to it.

How edge computing helps traders move faster

Cutting out delays

By removing the need to send data across long networks, edge computing gives traders a head start. Orders get to market faster. Systems react quicker. And when volatility hits, every microsecond you save makes a difference.

Running algorithms in the moment

Edge computing also makes it easier to analyze live market data as it comes in. Instead of waiting for a central server to process everything, edge systems can make real-time decisions that help traders adjust instantly.

Sharper execution

Colocated edge servers, placed near exchange data centers, reduce the time it takes for orders to be confirmed. That helps traders execute at better prices and improves the consistency of their strategies.

Keeping data secure

Sensitive trading data doesn’t need to travel as far, which means it’s less exposed. That helps reduce the risk of cyber attacks and supports compliance with financial data protection requirements.

Real-world examples of edge use in HFT

On-site servers

Many firms now colocate their edge systems in the same facilities as the exchanges they trade on. This setup reduces the physical and network distance between their systems and the market itself.

Smarter algorithms

AI models that help with trade execution and risk analysis can now run directly on edge infrastructure. This lets them respond faster to shifts in pricing, volume, or volatility.

Arbitrage in motion

When pricing discrepancies appear across different markets, the first to spot and act wins. Edge computing helps firms react while the opportunity is still there.

Upgraded networks

Some firms combine edge systems with microwave or low-latency fiber networks. This speeds up communication between offices, exchanges, and other trading points.

What gets in the way

High upfront costs

Deploying edge systems near every major exchange isn’t cheap. It requires real estate, specialized hardware, and ongoing management. For firms operating globally, those costs can add up quickly.

Compatibility with existing systems

Many trading environments weren’t built with edge computing in mind. Integrating new hardware and software into legacy setups can be tricky and time-consuming.

Data security at more locations

Edge computing means more distributed systems. Each one needs to be protected, which increases the security workload. Firms need to make sure each site meets internal and regulatory standards.

Regulatory pressure

Speed brings scrutiny. Firms that rely on real-time technology must still meet transparency and compliance rules. As edge systems become more widespread, regulators are likely to pay closer attention to how they’re used.

What’s next for edge in finance

Smarter, faster AI

Edge computing and machine learning are already a powerful combination. Expect this to grow, with models that can adapt instantly to market signals.

Quantum possibilities

Though still early, quantum computing may one day push speed and analysis far beyond what’s possible today. When paired with edge technology, this could change how fast trades are identified and executed.

More inclusive infrastructure

Edge setups are becoming more compact and affordable. This could allow smaller firms or regional players to compete on speed, not just scale.

Focus on sustainability

New edge systems are being built with energy use in mind. That’s a welcome shift for firms looking to balance performance with sustainability targets.

High-frequency trading demands speed, but speed alone isn’t enough. Traders also need systems that can analyze risk, find opportunities, and make smart decisions—all in real time.

Edge computing helps them do that by shortening the path between data and action. It brings the market closer and makes responses faster.

If you're looking to reduce latency, protect sensitive information, or upgrade your trading infrastructure, Simply NUC can help. We design compact, powerful edge systems built to handle the demands of modern financial environments.

Let’s talk about how you can stay fast, stay sharp, and stay ready for what the market throws your way.

Useful Resources

Fraud detection machine learning

Fraud detection in banking

Fraud detection tools

Edge computing platform

Edge server
Edge devices