Data extraction for AI and ML training.
Structured catalogs, 3D model metadata, and domain-specific datasets extracted from fragmented sources — delivered in formats ready for your training and retrieval pipelines.
Industry challenges.
- Fragmented source landscape Training data often lives across dozens of distributor catalogs, niche repositories, and industry-specific databases with no unified API.
- Format and schema drift Sources change their data structures without notice. Training pipelines that ingested data last month may silently produce corrupted datasets this month.
- Scale and refresh requirements AI training needs large volumes refreshed continuously. One-off scrapes produce stale datasets that degrade model performance.
Our approach.
We build multi-source extraction pipelines that continuously harvest structured data from catalogs, repositories, and domain databases. Each source gets its own pipeline with authentication handling, format normalization, and drift detection. Output schemas are designed for direct ingestion into your ML infrastructure.
Delivery.
Normalized JSON or NDJSON datasets delivered to S3, BigQuery, or your data lake. Monthly, weekly, or on-demand refresh cycles.
3D-model training data from 20+ distributor sources.
A physical-AI platform needed continuously-refreshed 3D-model metadata and assets from a fragmented set of distributor and marketplace sources. Each source had distinct authentication, rate limits, and format drift. We built and still operate the extraction pipeline, delivering normalized catalog data on a monthly cadence for their training and retrieval stack.
Tell us what you need to extract.
Describe the sources, schema, and cadence. We'll reply with a scoped quote within 48 hours.
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