Video Data for AI Training and RAG
Build video corpora with stable schemas, extracted transcripts, and structured metadata: delivered as datasets, real-time feeds, or fully managed collection.
- Reliable extraction at scale
- Adaptive resilience for changing platforms
- Zero-touch delivery
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Turn video into usable training and retrieval data
Video is high-signal but operationally fragile: formats drift, platforms update layouts, and one-off extraction breaks in production. We architect the infrastructure to handle this complexity: stable outputs, adaptive parsing, and delivery that fits your ML pipeline.
Built for AI/ML workflows
- RAG corpora refresh: keep knowledge bases current with timestamped video drops and transcript updates
- Training datasets: text + metadata at scale, mapped to your schema, with reproducible refresh cycles
- Evaluation sets: repeatable snapshots to track model drift and regression against fixed baselines
- Content monitoring: track topic shifts, creator activity, and platform changes across your target corpus
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Typical outputs
Structured fields mapped to your schema: validated and deduplicated.
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- Video metadata: title, description, publish date, duration, tags/categories
- Publisher signals: channel info, catalog structure, posting cadence
- Transcripts & subtitles: speech-to-text extraction and multilingual caption coverage (where available)
- Engagement metrics: views, likes, comments (where publicly available and compliant)
- Search & discovery: query results, playlist structures, recommendation contexts
- Custom fields: your naming conventions, validation rules, and enrichment logic
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Choose your delivery model
Datasets (bulk delivery)
Structured exports for training runs, backfills, and evaluation snapshots.
Real-time APIs/Feeds
On-demand access with predictable response formats for live retrieval and agent workflows.
Managed collection
Managed extraction and adaptive maintenance delivering clean outputs on schedule.
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Common video data projects
- Video-language pretraining corpora with temporal reasoning annotations and consistent schema for large vision-language models
- Multimodal fusion datasets combining pixel, audio, and transcript signals for unified representation learning
- Ego-centric video collections for robotics and embodied AI requiring spatial-temporal consistency and long-form continuity
- Content safety and moderation training data with cross-modal context (visual + audio) and annotated risk signals
- RAG-ready video collections with timestamped scene segmentation, refreshable transcripts, and metadata for knowledge grounding
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What you get
- Stable schema you can build against
- Validation + QA (required fields, type checks, sanity rules)
- Refresh cadence: one-time, daily, weekly, monthly
- 99.99% uptime - engineered for continuous production workloads
- Zero-touch maintenance
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Scope → Sample → Scale
Validate the approach before committing to production volume.
- Define targets, schema, and delivery constraints
- Validate technical feasibility and access requirements
- Deliver pilot dataset for schema testing
- Iterate on parsing logic and quality rules
- Deploy production pipelines with SLAs and monitoring
- Continuous operation with adaptive maintenance and support
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Getting to production
Discuss your data requirements, technical constraints, and compliance needs with our engineering team. We'll analyze your target sources and provide an implementation plan.