Accelerate your media processing with ultra-low-latency, AI-enhanced video solutions, optimized for edge performance where milliseconds matter.
For applications requiring immediate data analysis and low latency—such as real-time monitoring, autonomous vehicles, and intelligent surveillance—processing information locally is far more effective than relying on centralized cloud servers.
At Fluendo, we combine our expertise in multimedia processing, hardware acceleration, and artificial intelligence to create solutions that deliver exceptional performance and reliably on-edge devices.
Our edge-focused approach ensures we meet the high-performance demands of industries that operate seamlessly on edge devices, catering to sectors that demand real-time, reliable data analysis.
For applications requiring immediate data analysis and low latency—such as real-time monitoring, autonomous vehicles, and intelligent surveillance—processing information locally is far more effective than relying on centralized cloud servers.
At Fluendo, we combine our expertise in multimedia processing, hardware acceleration, and artificial intelligence to create solutions that deliver exceptional performance and reliably on-edge devices.
Our edge-focused approach ensures we meet the high-performance demands of industries that operate seamlessly on edge devices, catering to sectors that demand real-time, reliable data analysis.
A successful implementation demands advanced knowledge across media processing frameworks (FFmpeg, GStreamer, DirectX), AI frameworks (TensorFlow, PyTorch, ONNX, TensorRT, OpenCV), multi-threading, and hardware accelerators, making it challenging to build, benchmark, deploy, and maintain these solutions.
With cloud, edge, desktop, and mobile devices leveraging a diverse array of CPUs, GPUs, and VPUs (from NVidia, Intel, AMD, ARM Cortex-A, ARM Mali, and more), adapting solutions across these platforms is no small task.
The diversity of development and production hardware makes the “program once, deploy anywhere” model unattainable without customized approaches to integrating pipelines across various AI frameworks.
A successful implementation demands advanced knowledge across media processing frameworks (FFmpeg, GStreamer, DirectX), AI frameworks (TensorFlow, PyTorch, ONNX, TensorRT, OpenCV), multi-threading, and hardware accelerators, making it challenging to build, benchmark, deploy, and maintain these solutions.
With cloud, edge, desktop, and mobile devices leveraging a diverse array of CPUs, GPUs, and VPUs (from NVidia, Intel, AMD, ARM Cortex-A, ARM Mali, and more), adapting solutions across these platforms is no small task.
The diversity of development and production hardware makes the “program once, deploy anywhere” model unattainable without customized approaches to integrating pipelines across various AI frameworks.
At Fluendo, we are dedicated to delivering innovation and quality in our multimedia Edge AI solutions. We invite you to connect with us to explore how our cutting-edge technologies can elevate your projects to new heights. We’ve compiled the most frequently asked questions to help you better understand how we work.
Fluendo offers comprehensive solutions for real-time media processing, from building robust MLOps infrastructure to the deployment and optimization of AI models on specific devices. We ensure your solutions are production-ready, with ongoing maintenance and performance optimization to guarantee the best results for your business, whether you’re working with embedded systems or complex edge environments.
Absolutely! We start by understanding your needs to design and develop a custom AI model tailored to your challenges. After evaluating its performance in real-world scenarios, we deploy it to ensure optimal performance and reliability.
We have extensive AI and hardware optimization expertise, enabling seamless adaptation and deployment of your AI model to embedded systems. We also offer Raven AI Engine, a versatile AI runtime designed for optimized inference across various platforms (NVIDIA, AMD, etc.) and programming languages (C++, C#, and more).
Of course! Our AI development team excels in refining models that don’t meet specific KPIs. Using techniques such as data filtering, synthetic data generation, model fine-tuning, and optimization, we help enhance your model’s accuracy and achieve the desired outcomes.
We specialize in enhancing the performance of AI applications. Thanks to our expertise in hardware acceleration and efficient runtime environments, we can drastically reduce inference times, making your model suitable for real-time applications.
Fluendo has developed a powerful in-house MLOps infrastructure to ensure traceability throughout the AI lifecycle. From model creation to production monitoring, our system tracks datasets, models, and reports, aligning with the latest EU AI Act regulations and ensuring transparency and compliance.
From the code to the action
Developments that bring real-world results. These case studies show how our solutions help your business achieve goals and enhance user experiences.
A client in the sports technology sector, operating across football, rugby, and hockey, required a real-time sports video analysis solution capable of running multiple AI models in parallel for tasks such as object detection, player tracking, and camera calibration. The system needed to deliver high-performance processing and improved accuracy …
A hardware manufacturer required an AMD-optimized application that utilized dedicated GPUs for game rendering and the integrated GPU for executing AI tasks. The implemented solution employed Edge AI and GStreamer to perform real-time facial gesture extraction, background removal, and 3D avatar rendering from live webcam feeds. This architecture …
The company specializes in drone-based sensing technologies for precise aerial data collection, integrating advanced video and sensor analytics for applications such as surveillance, environmental monitoring, and industrial inspection. To improve efficiency and scalability, the company partnered with Fluendo to migrate its computer vision …
The client partnered with the team to enhance a CNN-based vehicle detection model, a core component of their smart parking slot monitoring and eco-friendly parking guidance solution. The company provides simple, cost-effective parking guidance systems for shopping centers, strip malls, mega malls, department stores, and other large commercial …
Concepts with real-world potential
These use cases showcase the ideas, technologies, and approaches we’ve developed to solve real industry challenges.
Historic sports footage represents a valuable asset for broadcasters, clubs, sports federations, and media archives. However, many classic matches were recorded in low resolutions, analog formats, or early digital standards, resulting in blurry visuals, limited detail, and reduced viewing quality on modern displays. Manually restoring and enhancing …
Sports clubs and academies increasingly produce live video streams, interviews, commentary shows, and behind-the-scenes content for digital platforms and social media. These broadcasts often take place in training grounds, stadiums, or mixed media areas, where children, staff members, and spectators may appear in the background. This creates …
Sports organizations generate and store vast amounts of video recordings, match reports, scouting notes, press releases, and analytical documents. Over time, these collections grow into extensive archives that are difficult to navigate, especially when metadata is incomplete or inconsistently structured. Traditional search systems rely on manual …
Sports clubs, broadcasters, and analytics teams increasingly rely on data-driven insights to understand player performance, tactical behavior, and match dynamics. Traditionally, these metrics are collected using dedicated tracking systems or manual annotation workflows, which can be costly, complex to deploy, and difficult to integrate into …
Bits & Bytes
Explore our blog, one byte at a time. Our team unpack our latest news, industry insights and in-depth articles to connect you with the multimedia world.
IA·Veu is a professional AI voice technology in Catalan, designed for the audiovisual industry with a strong focus on legality, ethics, and content traceability.
Fluendo and Spyrosoft BSG partner for AI sports video analysis.
Solving video playback artifacts and latency in mobile applications.
Redefining real-time AI inference with Raven's GPU-driven architecture.