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.
our case studies
Developments that bring real-world results, these case studies show how our solutions help your business achieve goals and enhance user experiences.
The client needed to replace manual and error-prone ad spotting with a high-accuracy AI-powered advertisement detection system that could run privately at the edge. Key requirements included precise ad recognition in both video and audio, support for multiple languages, and offline processing to minimize bandwidth usage, protect sensitive multimedia content, and reduce operational costs.
The solution was delivered as a self-contained Docker application with a full CLI, ensuring portability, easy maintenance, and scalability. Designed for robustness and future-readiness, this AI-driven advertisement detection solution empowers businesses to automate ad tracking while maintaining efficiency, privacy, and cost control.
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 while maintaining low latency.
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 delivered expressive, low-latency overlays without impacting gameplay performance, ensuring smooth operation even during high-resolution, resource-intensive gaming sessions.
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 application from an OpenCV-based prototype to a GStreamer-based multimedia solution.
The collaboration included dedicated consulting support, with in-depth pipeline analysis, performance optimization, latency reduction, and enhanced metadata management. Fluendo also provided tailored training and hands-on guidance, empowering the client’s team to confidently maintain and evolve their high-performance multimedia architecture for real-time aerial sensing applications.
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 facilities, helping reduce congestion and environmental impact.
After deployment, real-world conditions caused performance degradation in vehicle presence detection. Through an end-to-end AI deployment and optimization process, including model conversion validation and preprocessing improvements, we restored accurate, production-ready performance across all environments, ensuring reliable AI-powered parking management at scale.
our use cases
These use cases present conceptual examples of how our ideas and technologies could address real-world 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 these recordings is complex, time-consuming, and often requires specialized post-production workflows. As demand grows for remastered sports content, documentaries, digital archives, and modern streaming platforms, improving the quality of historic footage has become increasingly important.
With advancements in AI-based superresolution, computer vision models can reconstruct missing details and upscale legacy sports videos to higher resolutions. By processing video frames automatically, such systems can transform historic recordings into clearer, sharper versions suitable for modern screens while preserving the authenticity of the original footage.
Youth and academy sports organizations increasingly record matches and training sessions for performance analysis, coaching review, and player development. However, these recordings often include minors, spectators, and staff members, creating important privacy and regulatory challenges, particularly under frameworks such as GDPR and child protection policies.
Manually anonymizing individuals in sports footage is time-consuming and difficult to scale, especially when clubs, academies, or federations manage large volumes of recorded matches, training sessions, and archived content.
With advances in AI-based person detection, tracking, and segmentation, computer vision systems can automatically detect individuals appearing in sports video and apply anonymization techniques such as face blurring or body masking. At the same time, the system can preserve useful movement and positional metadata, enabling coaches and analysts to study gameplay, tactics, and player behavior without exposing personal identities.
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 important privacy and safeguarding challenges, particularly in youth sports environments where minors must not be publicly identifiable without explicit consent.
Manual editing or post-production anonymization is not feasible for live broadcasts or real-time streaming, where video must be processed instantly before distribution.
With advances in AI-based person and face detection, video processing systems can automatically identify individuals appearing in the background of a live stream and apply anonymization techniques such as face blurring or masking in real time. This allows sports organizations to safely broadcast interviews, live shows, and training content while protecting the identity of children and other individuals present in the scene.
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 tagging and keyword-based queries, which often fail to capture the contextual meaning of the information stored in these archives. As a result, valuable knowledge remains difficult to retrieve, slowing down research, media production, and editorial workflows.
Recent advances in Large Language Models (LLMs) and semantic search systems enable a new way to explore large multimedia and document collections. Instead of relying on keywords, users can query archives using natural language questions and retrieve results based on semantic relevance.
By combining AI-powered document understanding, vector search, and contextual reasoning, sports organizations can explore their archives more efficiently, accelerate research workflows, and unlock valuable insights across historical content.
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 existing video infrastructures.
Recent advances in AI-based computer vision enable sports analytics to be derived directly from video. By analyzing match footage frame by frame, AI models can estimate player positioning, movement trajectories, and spatial relationships across the field.
These insights can be transformed into valuable performance metrics such as distance traveled, top speed, positioning patterns, and heatmaps, enabling coaches, analysts, and media teams to better understand player behavior and team dynamics.
When combined with modern multimedia pipelines, these AI-generated insights can be embedded directly into the video stream as structured metadata, enabling downstream systems to access analytics data in real time without requiring separate data pipelines.
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.
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.