Dataset collection proxy for structured ML records
Not every dataset is a raw corpus. Fine-tuning sets, evals, and benchmarks need targeted, structured records: products, prices, reviews, entity pages — clean fields you can label and split into rows. A dataset collection proxy routes that field-level scrape through real mobile exits, so a target serves you consistent, comparable records across the whole run instead of throttling you into a patchy table.
Structured collection breaks on inconsistent targets, not on code
A labeled dataset needs every record measured the same way. When a site rotates locale, currency, or layout mid-run, your fields drift and the dataset is silently corrupted. Stable mobile sessions keep a target serving one coherent experience so each record stays comparable.
Target specific record types, not the whole web
Dataset collection is narrow by design. You already know the sites and the record types — product pages, review threads, pricing tables, entity profiles — and you want the fields off each one. Route that targeted scrape through the carrier pool so you can hit the same hosts repeatedly without one exit accumulating enough volume to get flagged and skew your sampling.
Sticky sessions hold a record together
A structured record often spans a search, a listing, and a detail page that have to stay on one IP and cookie jar to read consistently. Set sticky sessions so each multi-step extraction path completes on a single exit. That is what keeps paginated, multi-page records coherent instead of fragmenting into rows with half their fields missing.
Keep fields comparable across the run
The value of a labeled dataset is that every row is measured the same way. Per-IP cookie jars and automatic 2× backoff on 429/503 keep a target serving the same locale, currency, and layout from the first record to the last, so a field like price or rating stays comparable rather than drifting as the site swaps experiences under load.
Clean structured output, ready to label
You extract fields and write rows to your own store — Parquet, JSONL, a database, wherever your ML pipeline reads from. The exit layer logs metadata only: byte counts, timestamps, exit IP, status code. No payload inspection, no retention of the records you collect. The dataset is yours; the proxy just keeps the target answering cleanly.
Built for targeted, structured runs
Targeted host coverage
Hit the same known sites repeatedly through the carrier pool without one exit skewing your sampling on per-host limits.
Sticky record sessions
Hold a search → listing → detail path on one IP and cookie jar so multi-step records extract coherent fields.
Consistent field context
Per-IP cookie jars keep locale, currency, and layout stable so a field stays comparable across the whole run.
Backoff for clean rows
Auto 2× backoff on 429/503 keeps a target answering so records land complete instead of half-empty.
Structured collection at a glance
Dataset collection proxy — questions
What is a dataset collection proxy?+
How is this different from a training-data crawl?+
Should I use sticky sessions?+
How do I keep records consistent?+
Start a collection run
Sizing the pool
Collecting structured records across a set of targets? Check the pricing for per-IP rates and bulk discounts, then create an account and point your extractor at the endpoint in under 90 seconds.
Pull a sample dataset, free
Run one real mobile IP for an hour with no card. Point your crawler or agent at the source, watch the data come back clean, then move to a plan.