Accelerate your media processing with ultra-low-latency AI video analyzer 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—deploying video analysis AI locally is far more effective than relying on centralized cloud servers.
At Fluendo, we combine our expertise in multimedia processing, hardware acceleration, and AI for video analysis to create solutions that deliver exceptional performance and reliability on edge devices.
Our edge-focused approach ensures we meet the strict demands of industries that require real-time insights, enabling complex models to operate seamlessly where every millisecond counts.
For applications requiring immediate data analysis and low latency—such as real-time monitoring, autonomous vehicles, and intelligent surveillance—deploying video analysis AI locally is far more effective than relying on centralized cloud servers.
At Fluendo, we combine our expertise in multimedia processing, hardware acceleration, and AI for video analysis to create solutions that deliver exceptional performance and reliability on edge devices.
Our edge-focused approach ensures we meet the strict demands of industries that require real-time insights, enabling complex models to operate seamlessly where every millisecond counts.
Building an AI that can analyze videos at scale 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 requires highly specialized engineering.
The fragmentation of development and production hardware makes the “program once, deploy anywhere” model unattainable without customized approaches to integrating pipelines for video analysis AI across various frameworks.
Building an AI that can analyze videos at scale 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 requires highly specialized engineering.
The fragmentation of development and production hardware makes the “program once, deploy anywhere” model unattainable without customized approaches to integrating pipelines for video analysis AI across various 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.
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.
The Catalan audiovisual sector needed professional-grade synthetic voices, but existing open-source models lacked the naturalness and prosody required for high-end production.
Furthermore, AI voice cloning posed significant legal and ethical risks, including intellectual property infringement and unauthorized use of biometric data.
A secure, end-to-end multimedia solution was required to close this technological gap while ensuring compliance with the EU AI Act and protecting voice talent. The objective was to build a highly scalable, ethical platform that provided industry-leading phonetic precision without compromising data sovereignty.
our use cases
These use cases present conceptual examples of how our ideas and technologies could address real-world industry challenges.
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 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.
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.
The Catalan audiovisual sector currently faces a significant technological gap regarding high-quality, professional-grade synthetic voice solutions. Foundation Text-to-Speech (TTS) technologies work effectively for the majority of languages with large numbers of speakers, such as English, Chinese, and Spanish. Nevertheless, for regional languages like Catalan, these models do not deliver adequate performance and fail to meet the needs of the phonetic nuances and dialectal diversity required for professional dubbing and media production. Furthermore, the rise of generative AI has raised urgent concerns about the privacy of biometric data and the intellectual property rights of voice actors.
Due to these challenges, the industry would require a secure, sovereign, and ethically grounded platform to generate high-fidelity Catalan synthetic speech. It would be essential to establish a system that not only achieves naturalness through advanced Computer Vision and MLOps principles but also ensures total compliance with the EU AI Act and GDPR.
Catalan serves as a proof of concept for sovereign technology in other underrepresented European regional languages.
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.Blog
Read more about our work
IA-Veu building legal, ethical, and trustworthy AI voice technology in Catalan
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Fluendo and Spyrosoft BSG partner to develop AI-Powered solutions for sports video analysis and broadcast workflows
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Enhancing mobile video playback: Lessons from a real-world GStreamer project
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Raven: A 100% GPU-driven AI inference framework for real-time video and graphics
ai, multimedia-edge-ai, fluendo-ai-plugins, ravenRedefining real-time AI inference with Raven's GPU-driven architecture.