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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 a scalable, modular system capable of handling high volumes of video streams for recording and re-streaming purposes. The solution was required to support live media ingestion, continuous recording, and dynamic re-streaming using standard protocols such as RTSP, HLS, and WebRTC. Additionally, the system had to be API-driven, containerized for flexible deployment, and designed to scale efficiently to support thousands of concurrent channels.
We implemented a containerized, Docker-based architecture with built-in support for standard streaming protocols including RTSP, HLS, and WebRTC. The system featured an API-driven configuration layer for dynamic stream management and was architected for scalability, capable of supporting over 10,000 concurrent video channels.
For a closer look at how we approach scalability and re-streaming challenges, check out our article Recasting and recording system, which expands on the techniques and architecture that underpin this project.
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.
The client transitioned from manual ad spotting to an AI-powered advertisement detection system achieving 96.36% average accuracy with minimal timing offsets across English, Spanish, Basque, Catalan, and Galician. By deploying AI at the edge, the solution eliminates constant cloud dependency, reducing costs, accelerating processing, and ensuring full control of sensitive multimedia content. This not only simplifies compliance but also minimizes manual effort and enables confident verification.
In practice, the system streamlines multimedia operations, strengthens advertiser relationships, and scales seamlessly with demand. Built to support reliable, multilingual ad detection technology, the solution reflects Fluendo’s commitment to delivering future-ready AI solutions for multimedia content analysis.
The company is a technology integrator specialized in complex engineering projects, delivering scalable video infrastructure for government and defense systems. As part of a major public-sector initiative, Fluendo expanded the client’s Recasting & Recording System (RRS) to support real-time video stream ingestion, advanced recasting and recording formats, video transcoding, and asynchronous event notifications. Leveraging a modular GStreamer-based architecture, these enhancements were seamlessly integrated in a short timeframe, ensuring high performance, long-term scalability, and reliability for mission-critical multimedia operations.
Fluendo efficiently delivered the requested features, positioning the RRS as a potential replacement for the client’s existing solution. Robust testing ensured high reliability and performance.
The company develops a modern AI-powered camera system designed to create safer workplaces and enable smarter business operations across industries. To strengthen its multimedia engineering capabilities, the company partnered with Fluendo to enhance its team’s expertise in GStreamer with a strong focus on Rust-based development.
Recognizing Rust’s growing adoption for its memory safety and high performance, Fluendo delivered a six-day, hands-on training program combining GStreamer fundamentals with modern Rust practices. Participants were guided through pipeline creation, memory-safe plugin development, interoperability with existing C APIs, and debugging complex multimedia workflows within the Rust ecosystem. The progressive structure of the sessions enabled engineers to directly apply their learnings to the company’s AI video processing products.
The engineering team gained confidence in integrating Rust into their multimedia stack with GStreamer. They developed a strong foundation in safe, high-performance multimedia development, enabling them to create more robust and efficient multimedia systems internally.
our use cases
These use cases present conceptual examples of how our ideas and technologies could address real-world industry challenges.
As video becomes central to entertainment, social media, e-learning, and news reporting, creators increasingly film in uncontrolled or sensitive environments—city streets, classrooms, hospitals, protests, and corporate settings where bystanders may appear without consent. Under GDPR and similar data-protection laws, publishing identifiable individuals without permission risks legal action and reputational harm. An automated anonymization system can detect and obscure faces in live streams or recordings without undermining creative vision.
Recent advancements in AI and real-time processing now allow scalable anonymization pipelines for post-production, livestreaming, and mobile content capture.
Anonymizer is a Fluendo AI Plugin built to fit seamlessly into today’s media production workflows, whether editing on a desktop, capturing on mobile, or livestreaming from the field. Powered by a GStreamer pipeline, it delivers real-time face blurring and frame-accurate anonymization across various video formats, codecs, and resolutions.
Designed for content creators, journalists, educators, broadcasters, and corporate teams, Anonymizer enables privacy-safe video creation in both live and post-production scenarios. Its scalable architecture supports on-device and centralized processing, allowing you to automatically anonymize video content anywhere—with minimal setup and no manual intervention—including dedicated features for children anonymization to meet stricter privacy regulations.
Our achievements
Protect identities without cloud processing. Film in sensitive areas without post-clearance delays — ensuring privacy, compliance, and faster production.
Delivers GDPR-aligned workflows by anonymizing personal data before it is stored or analyzed, ensuring regulatory compliance.
Shields against takedowns, lawsuits, and platform penalties.
Protect identities without cloud processing. Film in sensitive areas without post-clearance delays — ensuring privacy, compliance, and faster production.
Delivers GDPR-aligned workflows by anonymizing personal data before it is stored or analyzed, ensuring regulatory compliance.
Shields against takedowns, lawsuits, and platform penalties.
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.
Our solution uses deep learning models to detect individuals appearing in live video streams, including players, staff members, spectators, and children present in the background during interviews, commentary segments, or live coverage.
Each video frame is analyzed in real time to identify visible faces or people in the scene. Once detected, the system automatically applies privacy-preserving transformations such as face blurring, pixelation, or masking, ensuring that individuals cannot be identified while preserving the visual integrity of the broadcast.
The system is designed to operate within high-performance live production environments, supporting video resolutions up to 4K and 8K, and high frame rates including 60 fps and 120 fps. Thanks to an ultra-optimized multimedia AI pipeline, anonymization can be performed with very low latency, ensuring that the broadcast workflow remains uninterrupted.
The solution integrates directly into professional streaming and broadcast pipelines, enabling anonymization to occur before encoding or distribution to streaming platforms. Processing can be deployed on edge AI devices located in stadiums or production units, or in cloud-based infrastructures, depending on the production workflow.
By combining optimized AI inference with high-performance video processing, sports organizations can safely produce interviews, analyst shows, live streams, and behind-the-scenes content while protecting the identities of children, spectators, and staff—even in high-resolution, high-frame-rate live productions running on compact edge hardware.
Our achievements
Automatically anonymizes individuals appearing in the background of interviews and live streams, helping sports clubs safeguard minors and respect privacy requirements.
Applies anonymization directly within the live video pipeline, allowing clubs to broadcast interviews, commentary shows, and behind-the-scenes content without manual editing.
Processes live video streams with minimal latency, enabling anonymization to run seamlessly within existing sports production and streaming infrastructures.
Automatically anonymizes individuals appearing in the background of interviews and live streams, helping sports clubs safeguard minors and respect privacy requirements.
Applies anonymization directly within the live video pipeline, allowing clubs to broadcast interviews, commentary shows, and behind-the-scenes content without manual editing.
Processes live video streams with minimal latency, enabling anonymization to run seamlessly within existing sports production and streaming infrastructures.
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.
Implementing cryptographic video authentication in GStreamer to combat deepfakes and ensure content integrity.
Fluendo and Spyrosoft BSG partner for AI sports video analysis.
Low-latency live streaming and recording for law enforcement with RRS.
Automated AI-powered advertisement detection for multimedia content.