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.
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 …
Wildlife population management is critical for ecosystem balance, agricultural protection, and disease prevention. Traditional methods such as manual surveys or camera traps are time-consuming, invasive, and often limited in scope. To address these limitations, conservation agencies and land managers require a scalable, non-intrusive system to …
Underwater exploration, inspection, and marine research increasingly depend on underwater drones equipped with high-resolution RGB-D cameras. However, in deep or turbid waters, light absorption and scattering cause significant color distortion—reds and yellows disappear first—reducing visibility for human operators and degrading the performance of …
Enterprises adopting Desktop-as-a-Service (DaaS) solutions to support hybrid or remote workforces often face fragmented corporate branding during employee video conferencing. Because employees connect from virtual desktops and diverse applications, ensuring consistent branded backgrounds across all sessions becomes difficult. This inconsistency …
Enterprises operating DaaS & VDI environments face reduced meeting efficiency when global teams communicate across diverse accents, speech rates, and noisy environments. Standardizing speech clarity at the DaaS audio entry point would address comprehension gaps, repeat-backs, and longer call times while maintaining privacy and desktop …
Spyrosoft BSG and Fluendo partner to deliver production-ready AI solutions for sports video analysis and broadcast environments.
This post details a hands-on consulting engagement to resolve video playback issues in a React Native mobile app using GStreamer, with practical insights for developers facing similar challenges.
Learn how Raven redefines real-time inference with a fully GPU-driven architecture for multimedia and video pipelines.
Table of contents NMS-Raster: Post-processing bounding boxes using the “G” in GPU The bounding box problem Classical NMS NMS-Raster: Z-Aware rasterized suppression Advantages of the approach Implementation Instanced drawing into integer framebuffer Contribution histogram Threshold filtering Strengths of the design Performance NMS-Raster: Post-processing bounding boxes using the “G” in GPU In this article, we explore the challenges of bounding box post-processing in AI-powered object detection and demonstrate how our high-performance inference engine, Raven, addresses them from a novel angle.