Data for AI: Clean web data for RAG, training, and evaluation
Delivered as datasets, real-time feeds, or fully managed collection.
- Reliable collection at scale
- Stable schema + scheduled refresh
- Production-ready delivery
&w=3840&q=80)
ㅤㅤ
ㅤㅤ
Choose your delivery model
Pick the option that matches your workflow: bulk datasets, real-time APIs, or fully managed collection.
Delivery formats: JSON/JSONL/CSV/Parquet
Datasets
Structured datasets built around your target scope: vital for training runs, backfills, and evaluation sets.
Typical dataset themes:
- STEM & research (papers, abstracts, citations, author graphs)
- Patents (filings, assignees, claims, classifications, legal status)
- SERP & web discovery (queries, results, snippets, rich features)
- Ecommerce (catalogs, pricing, stock, reviews, seller signals)
&w=3840&q=80)
Real-time APIs/Feeds
Pull structured data on demand - designed for high-volume, low-friction usage.
Conventional real-time verticals:
- SERP data (localized results at scale)
- Ecommerce data (pricing, availability, reviews, product details)
- Social & community signals (public pages/posts, where compliant)
- Listings & directories (jobs, marketplaces, classifieds)
&w=3840&q=80)
Managed collection
Built for teams who want the data, not the maintenance. We operate collection, parsing, and ongoing maintenance on our directly operated network, delivering clean outputs on your schedule.
Common managed projects:
- High-volume collection across thousands of sources with billions of records
- Complex targets with frequent layout changes requiring adaptive resilience
- Continuous refresh pipelines with automated QA gates and proactive monitoring
- Custom extraction workflows with complex enrichment and validation requirements
&w=3840&q=80)
What you get
Clean, structured data designed for production. Validated, deduplicated, and delivered on your schedule.
- Stable schema you can build against
- Validation + QA (required fields, type checks, sanity rules)
- Dedup options where it matters
- Refresh cadence: one-time, daily, weekly, monthly
- Production-ready delivery
- 99.99% uptime: engineered for continuous production workloads
ㅤㅤ
ㅤㅤ
Common use cases
Where structured web data makes an impact across the ML lifecycle. From retrieval to monitoring, support critical workflows with reliable inputs.
- RAG/grounding: keep corpora current and reduce stale answers
- Training: domain coverage with repeatable refreshes
- Evaluation: consistent test sets and drift tracking
- Monitoring: track changes in markets, products, and public signals
ㅤㅤ
ㅤㅤ
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.