Risk Management
Seekr needed a scalable way to evaluate huge volumes of digital and podcast content for brand safety and suitability without relying on manual, subjective, and expensive review, while also finding enough cost-effective compute capacity for growing LLM training, inference, and deployment workloads.
Reported impact
Seekr scaled trustworthy content evaluation and LLM development while reducing cloud costs, improving workload performance, and delivering real-time brand-suitability scoring across large podcast catalogs.
Seekr runs SeekrAlign and its LLM development stack on Intel Tiber AI Cloud to replace costlier self-managed infrastructure and scale trustworthy content analysis. Audio from podcasts and other digital content enters Seekr’s inference pipeline, where Intel Data Center GPU Max Series 1550 systems using Intel Extensions for PyTorch perform transcription and diarization. A 7B model on an 8-node Intel Gaudi 2 AI accelerator cluster then categorizes sensitive content and performs risk assessment. SeekrFlow orchestrates the end-to-end LLM pipeline through an API, versioned executables, custom components, and alignment algorithms. Separate 4th gen Intel Xeon Scalable processor clusters handle application monitoring, storage management, and vector and inverted-index database services that support Seekr’s RAG services and pipeline operations. Results are surfaced in the SeekrAlign dashboard as real-time podcast suitability scores.
Podcast audio and other digital content enter Seekr’s inference pipeline, where Intel Data Center GPU Max Series 1550 systems running Intel Extensions for PyTorch transcribe and diarize the media. The processed content is scored by a 7B LLM on Intel Gaudi 2 clusters for sensitive-content categorization and risk assessment. Intel Xeon-based monitoring, storage, and vector/inverted-index database services support RAG and pipeline operations before results are delivered in the SeekrAlign dashboard.
"Seekr uses Intel Extensions for PyTorch for transcription and diarization processing on a cluster of Intel GPU-powered systems."
"Seekr, named one of the most innovative companies of 2024 by Fast Company, is using the Intel Tiber AI Cloud to build, train, and deploy advanced LLMs on cost-effective clusters."
"SeekrAlign solves this challenge by allowing companies to quickly and accurately screen hundreds of thousands of podcasts."
"The 7B LLM model that powers the SeekrAlign solution is deployed on a cluster of Intel Gaudi 2 systems to perform sensitive content categorization and risk assessments."
"The Align platform has already scored 20 million minutes of podcasts, a number that is projected to reach up to 40 million by the end of 2024."
"The company uses a cluster of 4th gen Intel Xeon CPU-enabled systems for application monitoring, storage management, and vector and inverted-index database services."
"This strategic collaboration to accelerate AI helps Seekr meet its ever-increasing demand for compute capacity while reducing its cloud costs and increasing workload performance."
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.
LeadIQ says Seekr has approximately 163 employees as of August 2026, and the Organization JSON-LD lists numberOfEmployees value 163 with addressCountry "United States."
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