Cut Your Data in Half. Your Models Won't Notice
ML-Safe compression across the AV data lifecycle, validated for real-world and synthetic video
Cut Your Data in Half. Your Models Won't Notice
ML-Safe compression across the AV data lifecycle, validated for real-world and synthetic video
Cut Your Data in Half. Your Models Won't Notice
ML-Safe compression across the AV data lifecycle, validated for real-world and synthetic video
More sensors. More miles. More data than any pipeline was built for. We close the gap, frame by frame, without risking your model accuracy.
More sensors. More miles. More data than any pipeline was built for. We close the gap, frame by frame, without risking your model accuracy.
Recent Stories
From Uncertainty to Confidence: ML-Safe Video Data Compression for Physical AI Systems
How content-adaptive compression, accelerated by NVIDIA, addresses video data bottlenecks for physical AI pipelines, including autonomous vehicles, without compromising ML model performance
Read more→Deep Dive: Managing the Petabyte-Scale AV Video Data Bottlenecks
Optimized video data processing for AV – 20%-50% file size reduction with remarkable detection, localization and confidence consistency
Read more→ML-Safe AV Video Data Processing Achieves Up to 50% Storage Reduction
Benchmark testing shows how AV developers can achieve greater savings across storage, networking and compute without compromising ML model accuracy
Read more→Recent Stories
From Uncertainty to Confidence: ML-Safe Video Data Compression for Physical AI Systems
How content-adaptive compression, accelerated by NVIDIA, addresses video data bottlenecks for physical AI pipelines, including autonomous vehicles, without compromising ML model performance
Read more→Deep Dive: Managing the Petabyte-Scale AV Video Data Bottlenecks
Optimized video data processing for AV – 20%-50% file size reduction with remarkable detection, localization and confidence consistency
Read more→ML-Safe AV Video Data Processing Achieves Up to 50% Storage Reduction
Benchmark testing shows how AV developers can achieve greater savings across storage, networking and compute without compromising ML model accuracy
Read more→Questions We Get Asked a Lot
Your video data will be up to 50% smaller while preserving ML model accuracy. Smaller footprint means reduced storage and transfer costs, and improved I/O time. Beamr’s compression is validated across the ML pipeline with rigorous benchmarks verifying less than 2% difference in mean Average Precision (mAP) and robust results in industry-standard metrics.
he patented Content-Adaptive Bitrate (CABR) technology analyzes each frame individually, compressing as aggressively as the content allows while preserving the structural details ML models depend on — unlike standard methods that apply uniform compression parameters regardless of content.
You can ingest any input, output is in major codecs - AVC, HEVC, and AV1 - ensuring compatibility with existing decoders, and analytics tools. Deploys as a Docker container - cloud or on-prem - and integrates via SDK or FFmpeg plugin. If you're already running NVIDIA GPUs (Ampere or newer), you can use your infrastructure.
Yes. Book a demo and we'll run your content through Beamr's system. You see results on your actual data - not generic benchmarks.
Direct access to Beamr's engineering team: seasoned video experts who work with you from integration through optimization. No generic support tiers.
Beamr can be deployed within your own environment — nothing needs to leave your infrastructure. SOC 2® Type II compliant.
Per GB processed. No seat licenses. No upfront fees. You pay for what you compress.