Building Brand Safety for Global Video: PYLER's Context-Aware AI Platform

PYLER ensures advertisers' ads are never displayed alongside harmful videos, which helps protect brand image. Instead, it places ads in contextually relevant settings to enhance brand value and marketing effectiveness. The solution provides a neutral, explainable validation layer needed by the market.

PYLER operates at the intersection of video understanding and brand trust, defining semantic primitives—the meaningful units of video context—and organizing them into rich, time-aware semantic spaces. By composing and evaluating these spaces against brand safety and suitability policies, PYLER gives enterprises a transparent trust layer that works across platforms and formats, from YouTube to emerging CTV inventory.

Leading brands including Samsung, LG, and Kenvue rely on PYLER to validate video environments across billions of impressions, with PYLER already serving 20 of Korea's largest firms and 10 Fortune 500 companies. To support this footprint, PYLER's Antares model processes roughly 100 GB of video per day, handling 2 million videos and 700 million videos cumulatively—equivalent to more than 60 years of content every day.

Before adopting NVIDIA's platform, PYLER's core challenge was technical scale and complexity: video is large, multimodal, and extremely information-dense, and existing fragmented pipelines could not keep up. Human-in-the-loop sampling, function-specific models, and rule-based validation created high latency and costs, limiting throughput and slowing down iteration on new policies or use cases.

PYLER needed an infrastructure that could embed, search, and label massive video streams in near-real time while keeping per-video costs low enough for production deployment. NVIDIA's accelerated computing stack, including NVIDIA DGX systems based on NVIDIA Blackwell architecture, CUDA, and AI frameworks, emerged as the only viable path to reach the required throughput and latency for multimodal inference and retrieval at scale.

PYLER implemented a Video Vector Embedding pipeline that assigns a unique digital fingerprint to each moment of video, integrating multimodal inputs (visual, audio, text, and metadata) into a unified representation. These embeddings are stored in a PostgreSQL-based vector database with pgvector, complemented by SingleStore for high-throughput serving, enabling fast similarity search and context matching across millions of videos.

NVIDIA DGX systems, featuring the Blackwell architecture and high-bandwidth NVIDIA NVLink interconnects, provide the foundational horsepower for this high-performance training and inference pipeline. By utilizing NVIDIA Mission Control to orchestrate complex training workloads and NVIDIA NeMo Curator to automate data curation and filtering, PYLER increased video pre-processing throughput by 4x compared to their previous in-house pipeline. This hardware-software synergy allows PYLER to handle video analysis and retrieval across large datasets with unprecedented efficiency.

The transition to the DGX B200 also fundamentally shifted PYLER's development velocity. By leveraging the increased compute density for a 5x increase in hyperparameter search capabilities, the team reduced their model training iteration cycle from three months down to just one. Furthermore, the move to Blackwell-based systems delivered a 3x improvement in multimodal model training speed over the prior generation, ensuring that PYLER's models can be developed and deployed faster than ever before.

By expanding upon the NVIDIA Blueprint for Video Search and Summarization (VSS) and leveraging NVIDIA NV-Embed to accelerate embedding generation, the system handles video analysis and retrieval across large video datasets at scale efficiently. PYLER's model doesn't just see "a car"; it understands "a person is feeling frustrated while driving in the rain," enabling precise contextual placement and safety audits that were previously impossible at high volumes.

PYLER's technical moat lies in vector-based, time-aware context reasoning that operates at the representation level, allowing policy and safety definitions to be updated quickly without full retrains. This architecture has delivered clear, quantifiable outcomes for customers across brand safety and suitability.

For Nongshim's KPOP Demon Hunters (HUNTRIX) campaign, PYLER's AiD Brand Safety solution reduced the share of the budget spent on brand-unsafe content from a 19.3% benchmark to just 4.67% within one month, validating unsafe placements across more than 2 million videos in that period. For Kenvue's Tylenol campaign, PYLER's AiM Brand Suitability solution cut average validation time from around 30 seconds for human reviewers (based on external industry benchmarks) to 0.011 seconds, enabling brand managers to execute highly granular, video-level media strategies while reducing reviewer cost by over 99.963%.