Risk Management
PYLER needed to verify brand safety across millions of videos per day, but keyword and rule-based moderation could not provide the semantic precision, throughput, or operating cost profile required for real-time advertising decisions.
Reported impact
PYLER processed 2 million videos a day, reduced unsafe content exposure by 76.7% for major advertisers, cut validation time from 30 seconds to 0.011 seconds, and used the consolidated NVIDIA stack to achieve a 10x reduction in operational costs while serving major global brands.
PYLER built a multimodal video-intelligence platform on NVIDIA DGX systems, CUDA, NVLink-connected Blackwell infrastructure, NVIDIA NeMo, and Mission Control to create what it calls semantic primitives for brand safety. Video, audio, text, and metadata are embedded into a single representation, stored in a PostgreSQL vector database with pgvector, and served through SingleStore for high-throughput retrieval and policy evaluation. NVIDIA NeMo Curator automates data curation and filtering, while the DGX environment accelerates model training, hyperparameter search, and large-scale inference. The platform can then label and search millions of videos in near real time so advertisers can validate placements against trust-and-safety rules instead of relying on manual review or brittle keyword heuristics. The result is a neutral validation layer that scales across YouTube and emerging CTV inventory.
Incoming video, audio, text, and metadata are embedded into unified vectors, stored in PostgreSQL with pgvector, retrieved and scored through SingleStore-backed services, and then evaluated by PYLER's policy logic to classify brand-safe or unsafe video environments in near real time.
Processed 2 million videos daily-equivalent to 60 years of content every 24 hours.
Reducing unsafe content exposure by 76.7% for major advertisers.
Validation times from 30 seconds (human benchmark) to just 0.011 seconds (AI).
leveraging NVIDIA Video Search & Summarization (VSS), and NVIDIA NeMo to achieve a 10X reduction in operational costs.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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