Enterprise Knowledge Search
StudyFetch needed to keep AI tutoring affordable while scaling live lecture transcription and personalized learning for millions of students, but off-the-shelf cloud transcription created cost and latency ceilings that threatened the real-time tutoring experience at the heart of the product.
Reported impact
StudyFetch cut its largest inference workload cost by about 10x, supports more than 7 million learners, processes hundreds of thousands of lectures each month, personalizes over 100 million learning interactions in real time, and unlocked new voice-tutoring and agentic-learning features without blowing up unit economics.
StudyFetch rebuilt its lecture-ingestion and tutoring stack on NVIDIA infrastructure so classroom content could become real-time study guidance. Live lecture audio is transcribed with NVIDIA Riva and the Parakeet ASR model packaged in NVIDIA NIM containers on L40S GPUs, cutting the company's largest inference cost center by roughly ten times. The resulting transcripts feed StudyFetch's Learn Engine, which runs on H100 and L40S GPUs and personalizes tutoring, flashcards, practice tests, and study plans against each learner's course material. Across the rest of the stack, StudyFetch is distilling open NVIDIA Nemotron models so more tutoring and course-generation workloads can move off expensive closed APIs and onto predictable internal capacity. This architecture lets the platform keep hundreds of thousands of lectures flowing every month while expanding into voice tutoring and broader agentic education products for both higher education and new AI-literacy programs.
Lecture audio is transcribed in real time with Riva and Parakeet, the resulting text is fed into StudyFetch's Learn Engine, and personalized tutoring, flashcards, tests, and study plans are generated against each learner's own course materials.
100M+ learning interactions personalized in real time through StudyFetch’s Learn Engine on NVIDIA H100 and L40S GPUs
By migrating its transcription pipeline to NVIDIA Riva with the Parakeet ASR model, packaging it in NVIDIA NIM microservices, and adopting NVIDIA Nemotron open models with distillation across its stack, StudyFetch cut its largest inference workload’s cost by roughly 10x
Hundreds of thousands of college lectures transcribed in real time every month
StudyFetch is an AI-native learning workspace used by more than 7 million learners
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