Stream Analyze blog

Beyond the Hype: The New Era of Edge Computing

Stream Analyze — Thu, 30 Apr 2026 15:51:16 GMT

Uppsala, Sweden

October 15, 2024

Imagine a world where data works faster than you think—where insights aren't delayed by cloud journeys, and decisions aren't stalled by slow and expensive connections. This is not the future; it’s happening now at the edge. Edge computing is flipping the script on how businesses handle data, transforming what was once a burden into a real-time advantage. From crunching massive machine learning models in milliseconds to running autonomous operations in remote locations, the edge is where latency vanishes, costs shrink, and operations take a bold leap forward. If you think edge computing is just about lower latency, think bigger—it’s a whole new way of doing business.

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The Edge Computing Market: A Multidimensional Landscape

According to Gartner, the edge computing market is far from homogeneous. It spans eight distinct but interconnected submarkets:
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Edge Analytics and Machine Learning bring real-time intelligence directly to devices, while Edge Management and Orchestration ensure centralized control over distributed systems. Edge Vertical Industry Solutions provide tailored capabilities for sectors like manufacturing and automotive, and Edge Data Management enables fast, local processing to reduce cloud dependency.

IoT Platforms integrate and manage smart devices seamlessly, supported by Edge Communications Infrastructure like 5G networks to connect devices efficiently. Data Center and CDN Edge Services extend the cloud’s reach to the edge for enhanced performance, and Edge Computing Server Solutions deliver the necessary hardware and software for running complex workloads wherever needed. Together, these submarkets shape the multifaceted world of edge computing.

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The Evolution of the Edge Market

The edge computing market is evolving in phases. Initially, businesses were focused on highly specific use cases like factory automation or improving customer experiences in retail. Many early solutions were custom-built for single use cases, creating fragmented systems.

However, as companies realize the broader potential of edge computing, the market is shifting toward comprehensive platforms that can scale across multiple verticals and use cases. Leading research highlights the importance of extensibility and interoperable solutions to drive future growth—and that’s where Stream Analyze stands out.

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Why Stream Analyze is the Complete Edge Solution

Stream Analyze isn’t just another player in the edge computing landscape—it’s the platform redefining the space. While many vendors are busy cornering one aspect of edge computing, such as delivering custom-built vertical industry solutions or transferring usage data to the cloud, Stream Analyze provides a holistic approach. We cover multiple submarkets to deliver a true end-to-end platform that powers your edge transformation. Here’s how Stream Analyze stands apart:

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Unmatched Coverage Across Submarkets:

‍• Edge Analytics and Machine Learning: Stream Analyze enables you to run complex analytics and machine learning models directly on the device, transforming raw data into actionable intelligence, and driving faster, smarter decisions.

• Edge Management and Orchestration: Managing massive amounts of edge devices across distributed environments is a challenge—unless you have Stream Analyze. Our platform offers centralized control, automating operations and ensuring peak performance from every device.

• IoT Platforms: Connect. Manage. Optimize. With Stream Analyze, IoT devices are seamlessly integrated, allowing you to track assets, improve operational efficiency, and deliver exceptional customer experiences through real-time insights.

• Edge Data Management: Data is powerful, but only when it’s in the right place at the right time. Our platform processes, stores, and manages data locally—right at the edge—eliminating the need for constant cloud connectivity and ensuring rapid responses. Additionally, our robust interoperability allows seamless integration with virtually any external system.

• Edge Vertical Industry Solutions: One platform, endless possibilities. Whether you’re in manufacturing, automotive, health care, or any other industry, Stream Analyze is built to adapt to your specific needs, making it the enabler for both current and future use cases.

Utilize Your Existing Infrastructure: While Stream Analyze provides a comprehensive edge computing solution across multiple submarkets, there are a few areas we do not cover, and that's by design. Unlike vendors who offer hardware, servers, or communications infrastructure, Stream Analyze focuses on maximizing the value of your existing assets. Stream Analyze excels in abstracting the hardware, which means our platform adapts to and works with whatever hardware you already have. We ensure our software seamlessly integrates with your current infrastructure—enabling powerful edge analytics, AI, and orchestration without the need for costly upgrades. This means you can fully leverage edge computing capabilities on your existing devices and software, saving time and resources while still staying at the forefront of innovation.

Bringing Everything Together—No More Fragmentation: Edge computing is often riddled with fragmented solutions and vendor lock-ins, each addressing a tiny piece of the puzzle. But Stream Analyze changes the game by offering one unified platform. From data management to real-time analytics, from vision AI applications to IoT device orchestration, we eliminate silos, reduce complexity, and integrate seamlessly across the ecosystem—simplifying your edge operations like never before.

Ready for Scale, Ready for the Future: Growth should never be a limitation. As your business expands and your edge computing needs multiply, Stream Analyze is there to scale with you. Built for extensibility and adaptability, our platform is designed to grow from a handful of devices to large-scale, industrial fleets—ensuring you’re prepared for whatever the future holds.

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Strategic Playbook for Edge Computing Success

Embarking on your edge computing journey? Here are some key strategic moves to ensure your success:

• Think Big, Start Small: Tackle one edge use case at a time, but make sure your platform is built to scale. Today’s pilot should be tomorrow’s enterprise-wide solution. Select a platform with the capability to grow as you do—unlocking the full potential of the edge when you’re ready.

• Prioritize Extensibility & Interoperability: Don’t settle for a “one-and-done” solution. Choose platforms designed for flexibility and long-term growth, capable of integrating with a variety of systems, devices, and networks. Extensibility is key to keeping pace with rapid technological changes and ensuring your edge infrastructure remains agile.

• Focus on Strong Partnerships: In edge computing, no single vendor can provide all the pieces. Look for platforms with strong partner ecosystems that can offer seamless integrations, and the ability to support a variety of edge needs—no matter how complex.
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Stream Analyze checks all these boxes. By offering a platform that's scalable, flexible, and designed for full ecosystem integration, we deliver exactly what industry experts recommend: a future-proof platform that not only solves the challenges of today but sets your business up for success in the ever-evolving world of tomorrow.

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The Edge Isn't the Future—It’s Now.

Edge computing is no longer a distant promise; it's the engine driving transformation across industries. But the edge needs more than piecemeal solutions—it demands a platform that scales, adapts, and evolves at the speed of your business. That's exactly what Stream Analyze delivers: an end-to-end edge computing platform that doesn't just respond to the challenges of today but anticipates the needs of tomorrow.

By covering multiple areas—from data management to analytics and enabling vertical solutions—we’re helping organizations worldwide turn the edge into their competitive advantage. No silos, no limits—just seamless, real-time intelligence wherever you need it.

So, if you're ready to move beyond fragmented deployments and tap into the full force of edge computing, Stream Analyze is the partner to get you there.

Feel free to connect with us to explore how Stream Analyze can be the cornerstone of your edge strategy and transform your business operations.

Smarter Devices, Greener Futures: Enhancing Sustainability Through Innovation

Stream Analyze — Thu, 30 Apr 2026 15:51:15 GMT

Uppsala, Sweden

December 11, 2024

The sustainable shift with Edge Computing and AI

The surge in connected devices has unleashed an unprecedented explosion of data, most of which is processed and stored in massive cloud data centers. Yet, these facilities consume vast amounts of energy and water, making them significant contributors to greenhouse gas emissions and environmental strain.

⚡ Cloud data centers now consume as much energy as some countries, with demand projected to grow by 10% annually. Technological advancements have led us to this crossroads, but they also hold the key to solving these challenges. Edge computing and edge AI provide a transformative shift, decentralizing data processing and bringing intelligent decision-making closer to its source. These technologies have potential to reduce both energy-intensive data communication and reliance on data centers, proving that the tools of progress can also be the tools of preservation.

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🤖 What is Edge Computing and Edge AI?

• Edge Computing decentralizes data processing by performing it at or near the source, reducing the need for energy-intensive cloud data centers and minimizing data transmission.

• Edge AI builds on this foundation, enabling devices to analyze data in realtime and make intelligent decisions without relying on external systems

Together, these technologies empower industries to operate more efficiently, cut waste, and reduce environmental impact.

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📈 The Environmental Cost of the Digital Age

The digital revolution has brought unprecedented connectivity and innovation, but its environmental footprint is growing.

• ⚡ Energy Use: Typical data centers consume electricity equivalent to 50,000 homes annually, with worldwide usage reaching 200 terawatt-hours (TWh)—the energy consumption of some countries. ^1,2

• 🌫️ Greenhouse Gas Emissions: Cloud computing contributes 2.5%–3.7% of global greenhouse gas emissions, exceeding commercial aviation’s 2.4%.³

• 💧 Water Usage: New data centers use huge amounts of water for cooling. Google’s data centers alone consumed 5 billion gallons of fresh water in 2022, a 20% increase from the previous year.⁴

These figures underscore an urgent need for sustainable alternatives as billions of connected devices drive exponential data growth. The traditional cloud-centric approach is not sustainable.

🌟 Edge Computing and AI: A Sustainable Game Changer

Edge computing and AI address these sustainability challenges by combining decentralized data processing with intelligent decision-making. By analyzing data closer to its source—whether on a factory floor, within a vehicle, or at a remote site—these technologies can significantly reduce the need for energy-intensive data transfer and centralized processing.

Key Benefits for Sustainability:

• 📡 Reduced data transmission and energy consumption.
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• ⚙️ More efficient resource utilization.

• 🔋 Potential for extending hardware lifespans and minimizing e-waste.

• 💧 Reduced reliance on water-intensive cooling systems in large data centers.

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‍Edge computing and AI represents a shift from energy-intensive centralization to lean, localized intelligence—making it a cornerstone for sustainable digital transformation.

Stream Analyze: Sustainability in Action

Stream Analyze combines the power of edge computing and edge AI to help businesses achieve their sustainability goals. Here’s how it makes a difference:

• 🌐 Minimizing Cloud Dependence: By processing data locally, the Stream Analyze Platform reduces data transmission by over 99% compared to cloud-based solutions. This significantly lowers energy and water usage typically associated with cloud storage and processing. 5

• ⚡ Energy-Efficient Design: Stream Analyze incorporates an ultra-efficient query optimizer and compiler, utilizing 60 times fewer lines of code than comparable tools like TensorFlow Lite. This streamlined design significantly reduces energy consumption on any device or machine where it is deployed. 6

• ⏩ Rapid Model Deployment: The platform’s interactivity allows businesses to deploy AI models in minutes rather than months. This not only accelerates innovation but also reduces the environmental footprint of lengthy development cycles.

• 🔋 Extending Hardware Lifespan: By maximizing the capabilities of existing devices, Stream Analyze minimizes the need for frequent hardware replacements, helping to reduce electronic waste.

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Stream Analyze exemplifies how businesses can achieve their sustainability goals while driving efficiency and innovation.

💰 Realizing Savings: A Practical Example

Edge AI doesn’t just cut energy consumption and greenhouse gas emissions—it delivers significant cost savings. Here’s how:

📊 Cloud Analytics Costs

For 1,000 devices transmitting 1GB/day, the costs add up quickly:

• Mobile Data:

365,000 GB/year (1GB/day per device × 1,000 devices × 365 days).
At €1.00/GB, this totals €365,000 annually.

• Cloud Compute:

At €13/device per month for data storage and CPU, the cost is €156,000 annually for the fleet.
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💡 Total Annual Cloud Analytics Cost: €521,000
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🌟 Stream Analyze Savings

By reducing data transmission by over 99% and performing processing locally:

Mobile Data: Savings of approximately €361,000 annually.
Cloud Compute: Eliminates costs of €156,000 annually.

💰 Total Annual Savings: €517,000

This is sustainability in action: smarter data, smaller footprint, and real financial impact.

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About Stream Analyze

The Stream Analyze Edge AI Platform streamlines how you develop, deploy, run, and manage analyticalAI models on fleets of edge devices. We envision a world where every device, machine, and asset are powered by edge AI, and Stream Analyze Platform will be considered the industry standard for enabling this transformation.

Our platform stands out for its minimal resource footprint, real-time interactivity, and compatibility with any hardware or software environment. This makes it exceptionally efficient for processing vast amounts of data rapidly and at scale. Backed by 30 years of academic research, Stream Analyze is driven by our customers' success on the edge.

Learn more about our ground-breaking solutions at streamanalyze.com.

Sources:

1. https://www.carbonanalytics.com/blog/data-centers-and-greenhouse-gas-emissions
2. https://objectbox.io/why-do-we-need-edge-computing-for-a-sustainable-future/
3. https://www.climatiq.io/blog/measure-greenhouse-gas-emissions-carbon-data-centres-cloud-computing
4. https://e360.yale.edu/features/artificial-intelligence-climate-energy-emissions
5. https://cdn.prod.website-files.com/63c8f9c6f62f256a682759f4/66193576011c3dc94848e920_ICIT24-000025-final.pdf
6. Benchmarking performed on a Conv1D neural network across varying kernel & input sizes. Performance advantages increase with kernel & input size. Chart values are the mean of kernel size

A Leap Forward in Industrial IoT: Unveiling the "Interactive Deployment of Forklift Activity Recognition"

Stream Analyze — Thu, 30 Apr 2026 15:51:14 GMT

Uppsala, Sweden

March 25, 2024

Introduction

The industrial sector is currently witnessing a paradigm shift, thanks to the Internet of Things (IoT) and machine learning (ML). The potential to enhance operational efficiency, safety, and adaptability through data-driven methods is immense, yet transitioning these theoretical models into practical, real-world applications remains a challenge. Addressing this gap, the paper "From Publication to Production: Interactive Deployment of Forklift Activity Recognition," authored by Kunru Chen, Jonas Klang, and Erik Zeitler, introduces a novel approach that promises to substantially improve the lifecycle management of ML methods in operational settings.
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A Milestone Achievement at the 25th IEEE International Conference on Industrial Technology

Launched at the prestigious 25th IEEE International Conference on Industrial Technology, this paper not only showcases innovative research but also signifies a pivotal moment for industrial automation. The authors, representing a synergistic collaboration between academia and industry—Halmstad University, Toyota Material Handling Europe, and Stream Analyze—bring to light a successful implementation of ML models on forklift trucks through interactive deployment.

The crux of this work lies in its approach to bridging the often-vast gap between laboratory-developed ML models and their application in the unpredictable and dynamic environment of industrial operations. Through the utilization of the Stream Analyze Platform, the project demonstrates an interactive method that facilitates real-time adjustments and optimization of ML models directly on the forklift's telematics unit. This capability not only enhances the accuracy and reliability of the models but also introduces an unprecedented level of adaptability to operational variations.

Streamlining Edge AI Model Development

The Stream Analyze Platform refines AI model development, significantly condensing the time from validation to deployment, as depicted in the illustration below. It enables an interactive, iterative process that supports model adjustments in live environments. This approach allows for direct experimentation with live data on edge devices and enables models to dynamically evolve with immediate feedback. The platform facilitates swift deployment without the conventional requirements for manual coding or firmware updates, emphasizing efficiency in operational model refinement.
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Optimizing Operational Efficiency and Cost

1. 52% Accuracy Boost in Just 2 Hours: The project achieved a dramatic 52% improvement in model accuracy by incorporating expert-driven decision rules. Remarkably, these adjustments and the subsequent model re-deployment were completed in just two hours by a TMHE domain expert. This efficient process strongly advocates for the benefits of interactive deployment over traditional methods, emphasizing speed and agility in operational enhancements.

2. Efficiency in Model Deployment: The study highlights the practical benefits of the Stream Analyze platform's capacity for interactive model deployment. This approach eliminates the need for firmware over-the-air (FOTA) updates or other complex deployment strategies, thereby enhancing the model's agility and responsiveness to operational needs.

3. Dramatic Reduction in Data Transmission Costs: The project's strategic move to process data on the forklifts themselves led to a remarkable reduction in the need for data transmission, slashing it from a potential 3 GB per month and forklift to just 250 kB per month and forklift. This edge processing approach effectively reduced data transmission costs by a factor of 10,000, addressing key concerns of scalability and economic feasibility essential for broad-scale deployment.

4. Model Adaptability to Real-World Conditions: One of the paper's core strengths is its demonstration of the model's adaptability to the dynamic and varied conditions of real-world forklift operations. Thanks to the Stream Analyze platform, the deployed models can be swiftly adjusted to correct erroneous predictions and adapt to different operational scenarios. Furthermore, such decision rules may be used to collect data for fine tuning of the ML models deployed.

Conclusion

The launch of "From Publication to Production: Interactive Deployment of Forklift Activity Recognition" at the 25th IEEE International Conference on Industrial Technology not only demonstrates the practical application of advanced data-driven methods in industrial settings but also sets a new standard for operational efficiency, cost-effectiveness, and adaptability.

Looking Ahead

The success of this project underscores the relevance of collaborative efforts between academia and industry in driving technological advancements. As the industrial sector continues to evolve, the principles and methodologies outlined in this paper will inspire further research and innovation.

Querying the Mines...

Stream Analyze — Thu, 30 Apr 2026 15:51:14 GMT

Uppsala, Sweden

December 9, 2025

Swedish researchers and industry partners have proven that robust edge AI can operate 700 meters underground — paving the way for smarter, safer, and more sustainable mining.

After two years of collaboration, the FREEPORT project, funded by Vinnova, has reached its conclusion. And the ambition has been bold: to move AI out of the lab and into the field — onto the machines, into the mines, and closer to the decisions that matter.

The project brought together RISE, Boliden, Volvo, Stream Analyze, AI Sweden and Halmstad University, along with researchers and engineers exploring one shared goal: to understand how AI, edge computing, and cyber-secure data communication can be enabled in industrial operations.

From use cases to real impact

Throughout the project, the partners explored several industrial AI applications, all aimed at improving efficiency, sustainability, and safety in production environments.

The work focused on three main areas: predictive maintenance, enabling early detection of anomalies before failures occur; energy prediction, understanding how energy is consumed and recovered; and AI on the edge — running machine-learning models directly on vehicles and systems, even in extreme industrial settings.

Each use case demonstrates how moving intelligence closer to the data source can shorten feedback loops and enable new levels of operational awareness.

A demo 700 meters below ground

On June 3, 2025, the team gathered in Boliden, Västerbotten, to put their ideas into action. An electric mining truck began its descent — nearly 700 meters below ground — while AI algorithms onboard monitored how the vehicle consumed electricity on the way down, how it regenerated energy on the way up, and how driving style affected overall efficiency.

Senior AI researcher Sepideh Pashami from RISE explains that the team has been testing AI functionality by following the truck all the way down into the mine. They’ve been able to measure how the battery behaves, how energy is consumed and recovered, and how driving style impacts performance.

What makes this setup unique is that the AI doesn’t run in a distant cloud. It operates directly on the truck, and on a Volvo-installed logger, using Stream Analyze software to process the data on the edge — in real time, underground.

How the demo worked

The demonstration combined live data, on-board analytics, and real-time visualization. Data streams were exported from the edge devices to external systems using MQTT, a lightweight communication protocol widely used in industrial IoT.

This setup enabled seamless integration between Volvo’s systems and Boliden’s operations center, allowing engineers at both sites to monitor the same data live. The data flow — what was shared and how — was securely controlled by Volvo analysts, ensuring safe and traceable information handling.

In the Boliden control room, researchers and engineers from RISE, Volvo, Stream Analyze, Halmstad university and Boliden could see the data visualized in real time and discuss what it revealed about the truck’s performance underground.

As Erik Zetler from Stream Analyze describes it:

“We execute models specified by our users on the edge fleet. The results of these models are streamed to external systems and integrated with various environments. In this case, we’re running models specified by Volvo on their truck, and the operations center at Boliden is feeding on that same result data stream.”

It was, in essence, a complete AI infrastructure in action — from edge to cloud, from code to metal, and from research to reality.

Insights from the depths

The demonstration validated that AI on the edge works — even in one of the harshest environments imaginable. It showed that advanced analytics and machine-learning models can be built, tested, and deployed on real industrial vehicles without long lead times.

The team learned how to deploy and iterate models directly on the edge, cutting development cycles from months to minutes. They also developed methods for predicting energy consumption and battery charge state dynamically based on real driving patterns — and for integrating real-time analytics across multiple industrial systems securely and reliably.

The project also underlined the importance of standards and interoperability. Discussions during the demo highlighted Sparkplug B for MQTT as a promising way to harmonize data structures across partners — paving the way for scalable industrial applications.

Slawomir Nowaczyk from Halmstad University noted:

“It’s one thing to make a model work on your desktop — it’s another to make it run reliably on a fleet of trucks, deep underground.”

Beyond technical achievement, the results point toward significant long-term benefits. By understanding how electric trucks consume and regenerate energy underground, the team uncovered ways to fine-tune charging cycles and driving patterns — potentially charging can plan and regenerative energy can be considered to avoid overcharging.

Toward an intelligent future

With the project now complete, the FREEPORT partners see vast opportunities ahead. The methods and tools developed for this demo can now be extended to other industrial domains — from construction and logistics to renewable energy and manufacturing.

They also open the door to smarter, greener, and safer operations where data, connectivity, and intelligence work hand in hand. Future explorations will focus on deepening edge integration, refining data standards, and scaling real-time AI analytics across entire production ecosystems.

Beyond the technical achievements, the partners highlight the collaborative spirit that made FREEPORT stand out.

Dennis Forslund, Boliden representative, describes it as:

“A true team effort — blending engineering and analytics with real-world testing and making every step a joy.”

From Volvo’s perspective, the collaboration proved how industry and research can push innovation together:

“Edge computing and AI analytics are clearly part of the future. Seeing it live on our customer trucks was a great experience — collaborative, innovative, and impactful.”

For RISE, FREEPORT marks an important milestone in applied AI. Sepideh Pashami reflects:

“This project has shown what happens when we bridge research and reality. When AI moves closer to the machines, innovation moves faster.”

The FREEPORT project may have come to an end — but its results signal a beginning. Combining electrification, AI, and secure edge computing could enable heavy-industry operators like Boliden to dramatically cut emissions while improving safety and uptime — a model for future smart, sustainable mining

Stream Analyze Recognized as One of STL Partners' "Top 100 Edge Companies to Watch in 2024"

Stream Analyze — Thu, 30 Apr 2026 15:51:13 GMT

Uppsala, Sweden

March 15, 2024

Stream Analyze, a leader in edge AI technology, is proud to announce its inclusion in STL Partners' prestigious list of "Top 100 Edge Companies to Watch in 2024". This recognition underscores Stream Analyze's commitment to innovation and excellence in the rapidly evolving edge computing industry. "STL is delighted to feature Stream Analyze as one of the top edge companies of 2024. Distributing workloads to run close to where the data is being generated will be key to scaling artificial intelligence - and Stream Analyze is at the forefront of this," stated Tilly Gilbert, Director at STL Partners.‍‍

‍‍‍As edge computing transforms industries by bringing computation and data storage closer to the location where it is needed, Stream Analyze has positioned itself at the forefront of this technology wave. The company's cutting-edge solutions enable businesses to harness the power of edge AI, improving operational efficiency, reducing latency, and unlocking new possibilities for data analysis and application performance.

‍"Our edge AI platform has truly set a new standard this year, becoming the most compact and quickest solution globally," remarked Jan Nilsson, CEO of Stream Analyze. "We've pushed the boundaries of inferencing speeds and broadened the accessibility of edge AI, ensuring a wider array of industries can now leverage this transformative technology. Recognition by STL Partners not only highlights our innovative contributions to the edge computing landscape but also propels us forward as we continue to develop solutions that meet the future needs of our clients."‍‍

STL Partners, renowned for its insights and research in the technology sector, compiles the "Top 100 Edge Companies to Watch" list to spotlight companies making significant impacts within the edge computing space. Stream Analyze's inclusion in this list is a significant milestone that celebrates the company's achievements and its potential for future growth and innovation.

For more information about Stream Analyze and its edge AI solutions, visit streamanalyze.com.

AI in a chainsaw? Stream Analyze launches in the U.S. to make ‘stupid’ devices smarter

Stream Analyze — Thu, 30 Apr 2026 15:51:12 GMT

San Francisco, USA

April 26, 2024

In a significant move signaling its entry into the U.S. market, Stream Analyze, a leader in edge AI solutions, announced a partnership with Microchip and other American companies to bring its innovative technology stateside — such as putting AI in chainsaws.

Founded in Uppsala, Sweden in 2015, and buoyed by decades of academic research, Stream Analyze is not just crossing geographical boundaries: it’s setting out to redefine the capabilities of hardware and equipment typically used with minimal or no software — yes, that includes chainsaws, but also lawnmowers, mining rigs, forklifts and other commonplace tools and devices.

“What we’re trying to achieve here is to make sure that our customers are doing what we call edge analytics, or edge AI,” said Daniel Spahr, Stream Analyze’s chief operations officer, whose LinkedIn bio neatly summarizes his mission as “Making stupid things smart,” in a video interview with VentureBeat conducted earlier this week.

Who needs an AI chainsaw?

What are the benefits of doing this? After all, who really wants or needs an AI-equipped chainsaw?

Well, according to Stream Analyze, the benefits are actually pretty easy-to-understand and significant: imagine a fleet of loggers dispatched to a clear a forest.

Wouldn’t it be nice for their manager or boss to know which chainsaws were running low on gas, how they were performing, if there was wear and tear and if one or several were likely to fail? That way, arrangements could be made to have one ready to switch out, reducing downtime.

It’s a big pitch for edge AI devices in general, including infrastructure sensors offered by rival Eloque, a company spun off of Xerox when I worked for the company a few years ago: detecting likely problems before they happen, improving efficiency, saving time and cost, and keeping operations running smoothly.

Another potential rival might be Sima.AI, which offers a no-code platform for edge AI and machine learning (ML) deployment, but is geared for potentially even more computational heavy devices such as military and reconnaissance drones. (That company just raised $70 million, showing the entire edge AI space to be one of potential interest to investors and VCs.)

The benefits of Stream’s end-to-end edge AI/ML platform

But Stream Analyze believes its tech is superior to others because it is an “end-to-end platform for machine learning op[erations]” according to Spahr, one that identifies the correct data to record and upload from the field back to cloud servers, without capturing needless data that would increase the cost, computational and energetic requirements.

“The cost for offloading data and storing it and using the processing centrally has become expensive,” Spahr explained to VentureBeat. “So you want to push things out locally to have that hybrid solution.”

There’s also the fact that in many edge AI applications, wireless connectivity itself may not even be an option — think about areas with poor mobile service coverage such as the bottom of a mine or out at sea.

“You might have connections sometimes and sometimes you have not,” Jan Nilsson, Stream Analyze’s co-founder and CEO told VentureBeat in the same video interview. “And in the meantime, the product is still being used. So the wear and tear is continuous. So you need to do some kind of analysis directly on the machine, on the device. We are fully independent of communication infrastructure.”

‍The suite of products offered by Stream Analyze includes the SA Engine, SA Studio, SA Staging, and SA Federated Services, which together provide a comprehensive platform for deploying AI models at the edge.

“We provide certain templates and models out of the box to the customer, but usually they build their own models,” using the SA Studio, said Nilsson.

The AI is typically deployed on specific hardware spec’d to the industry or company in question, often microcontrollers, which Stream Analyze designs for by consulting reference designs and working with partners in the semiconductor industry.

This system allows for real-time data processing and is designed to be user-friendly so not only data scientists but also analysts and engineers can effectively manage and deploy AI solutions.

According to Spahr, other rival technologies “often require an embedded programmer or firmware updates or something similar, which is either risky or slow or both.”

Rapid deployment and fine-tuning

In addition Stream Analyze claims its technology enables for rapid development, adjustment and maintenance of AI models directly on devices.

“It’s really easy to push out new models instantaneously onto a device, and this means that your time to market is sped up substantially compared to other technologies out there,” said Spahr.

With a footprint requiring as little as 17kB of memory—significantly smaller and faster than competitors like AWS Greengrass or TensorFlow Lite—Stream Analyze’s solutions are uniquely suited to a broad range of applications.

And Stream Analyze is content to allow its customers to tailor its technology to their uses without it ever even knowing about the end result — ensuring privacy and confidentiality.

“No customer or the other are alike,” Nilsson said. “They have different use cases, and they prioritize different things, so they use different models. There is no ‘one size fits all’ here. And in many cases, we don’t even know what kind of use cases they are addressing. They implement and deploy the platform, and then they they don’t share the models with us, so we don’t actually know what kind of analytics they are running.”

As Stream Analyze steps into the U.S. market, is expects to not only increase its business but also drive significant advancements in how businesses use data and AI to make real-time, informed decisions that were impossible before.

Read Venture Beat's original article: https://venturebeat.com/ai/ai-in-a-chainsaw-stream-analyze-launches-in-the-u-s-to-make-stupid-devices-smarter/

Stream Analyze brings edge AI software to U.S. IoT market

Stream Analyze — Thu, 30 Apr 2026 15:51:11 GMT

San Francisco, USA

April 26, 2024

Just how far out on the edge can edge AI go?

A throng of technology companies are set to find out as the roll out an array of hardware and software products designed to allow AI processing and analytics in a wide range of IoT devices and end points.

The list includes Stream Analyze, an edge AI software firm based in Uppsala, Sweden that just announced an expansion into the U.S. market, where it already boasts a partnership with Chandler, Arizona-based Microchip Technology.
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Stream Analyze proposes to convert previously "stupid" machines into intelligent IoT assets with its SA Engine software platform for edge analytics that the company says allows AI models to be developed, deployed, and evolved on any device. If you are wondering about the list of devices that might include, let your imagination wander to not just connected vehicles and smart factory equipment, but also beyond that to items like lawn mowers and even chainsaws. If that sounds strange, consider that those machines, like many others, have engines and other components that are sources of device health data, and that can benefit from predictive maintenance.

Almost any machine nowadays can be fitted with sensors and connectivity to gather and transmit data off-device to be analyzed, but Stream Analyze told Fierce Electronics that some devices are just to small, and that AI capabilities are hard to deploy in a scalable and economically sustainable way, particularly when data needs to be sent to the cloud for AI analysis. Doing so can result in energy, computing, and storage costs that far exceed the cost of the hardware itself. Installing Stream Analyze’s SA Engine and relevant AI models on edge devices will allow companies to reduce those costs, while letting them brainstorm new business models that can leverage the presence of edge AI, the company said.

The platform can be used on devices and microcontrollers already deployed in the field, provided they have the computing resources. “Such devices include Arm Cortex M4 and M0,” Stream Analyze said via email. “Even though others have successfully deployed neural networks etc on these, Stream Analyze is unique in that our software SA Engine enables interactive queries and model lifecycle management. This gives our users the opportunity to see, understand, and model their data more effectively. The interactive model evolution ability enabled by SA Engine helps our users accelerate their edge AI projects to market. Applications include acoustic and vibration analysis, as well as motor control and powertrain anomaly detection.”

SA Engine is not the only edge AI software platform around for IoT devices and applications. For example, AWS Greengrass, which Stream Analyze clearly has honed in on as the competition, also offers analytics capabilities via cloud and edge. But, according to Stream Analyze, SA Engine requires only 17kB of memory, making it 5,600x smaller than AWS Greengrass, while averaging 2x faster inference than TensorFlow Lite. Maintaining compact code and faster inference speeds will allow to leverage the value of analytics, the company said.

As for Stream Analyze’s full approach, SA Engine is the main component installed on each edge device, while SA Studio is the company’s front-end development environment. There is also SA Staging, a testing environment for model scaling simulations, and SA Federated Services, which facilitates integration with customers' existing infrastructure.

The nine-year-old Swedish firm already has several large international customers in sectors like automotive and manufacturing, including Volvo Group, Iveco, Toyota Material Handling, Autoliv, Husqvarna, and ifm, and is now looking to the U.S. to lengthen its list of partners and customers.

"Expanding into the U.S. market is a milestone for Stream Analyze, as it opens doors to a hotbed of innovation and technology," said Jan Nilsson, co-founder and CEO of Stream Analyze. "Our edge AI solutions are ideally positioned for sectors at the forefront of digital transformation, like the automotive, manufacturing, and chip industries, spurred by advancements like Tesla's disruption and Industry 4.0. This move isn't just about growing our footprint; it's about catalyzing a wave of innovation, making edge AI accessible, and driving significant business advancements through data insights.”

Read Fierce Electronic's original article: https://www.fierceelectronics.com/iot-wireless/integrating-sensors-edge-ai-enhanced-iot-solutions

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