Enclustra and MakarenaLabs have partnered to integrate the MuseBox AI orchestration layer onto Enclustra's SoC and FPGA hardware modules. The resulting platform, branded as Lira, is designed to provide real-time, on-device AI processing for industrial and edge applications without cloud dependency.
The integration of MuseBox into Enclustra hardware provides a production-ready pipeline that bypasses the latency and security risks of cloud-based processing. For the streaming and computer vision ecosystem, this move signals a shift toward localized, high-performance inference where data remains on-device. By offering a scalable framework across different SoC and FPGA modules, the partnership allows engineers to transition from simple prototypes to complex multi-accelerator pipelines without changing software environments. Watch for how this hardware-software vertical integration impacts the adoption rate of real-time video analytics in sensitive sectors like defense and medical imaging.
Enclustra and MakarenaLabs are positioning the Lira platform within a growing market of FPGA-based edge AI systems targeting real-time video and sensor processing. MakarenaLabs launched MuseBox as a no-code AI orchestration layer that runs inference pipelines directly on embedded hardware, eliminating the need for external cloud services during deployment. The partnership with Enclustra gives MuseBox access to a broad portfolio of SoC and FPGA modules that already serve industrial, medical, and aerospace customers across Europe and North America.
The business case for on-device AI orchestration is strengthening as latency, data sovereignty, and connectivity constraints push processing closer to the sensor. Bitmovin's 2026/2027 Video Developer Report found that 98 per cent of respondents now use AI or ML for video, with 46 per cent employing AI tools daily, underscoring how deeply AI has penetrated video workflows. While Bitmovin's survey focuses on cloud and hybrid pipelines, the same demand for real-time inference is driving interest in edge-native platforms like Lira that can operate without persistent network access, a requirement in defense, medical imaging, and remote industrial monitoring.
On the technical side, competing approaches to hardware-accelerated edge AI continue to emerge from both FPGA vendors and specialized software layers. MpegFlow's 2026 comparison of encoding platforms noted that Bitmovin has shipped multi-codec strategies including AV1, HEVC, and AVC in production at scale, illustrating the codec diversity that edge AI systems must handle when processing video streams locally. Streaming Media's State of Streaming Codecs 2026 report highlighted startup Deep Render's IP-centric model for neural compression, a technique that could eventually intersect with edge AI pipelines by reducing bandwidth before on-device inference. For buyers evaluating Enclustra's Lira, the key differentiator remains the tight coupling between FPGA fabric and the orchestration layer, which allows deterministic latency guarantees that general-purpose GPU edge boxes cannot always match.
Enclustra and MakarenaLabs have partnered to launch Lira, a platform integrating the MuseBox AI orchestration layer onto Enclustra's FPGA and SoC hardware. This collaboration enables real-time, on-device video and audio processing, eliminating cloud dependency. It matters because it provides a secure, low-latency pipeline for industrial, medical, and defense applications.
Lira is a hardware-accelerated Edge AI orchestration platform resulting from a partnership between Enclustra and MakarenaLabs, designed to run AI pipelines directly on embedded hardware.
No, the Lira platform enables real-time, on-device video and audio processing without requiring cloud connectivity, which helps reduce latency and security risks.
The partnership targets five primary sectors: smart cities, medical, industrial inspection, defense, and robotics.
Lira supports real-time system capabilities including face detection, landmarking, object detection, and depth estimation for video and image data.
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