ENTERPRISE WEB CRAWLING

The whole platform.
Not a sample of it.

// THE SHORT ANSWER

Enterprise web crawling is full-platform discovery and extraction at scale: our crawlers map every category, listing and seller page of a target platform — millions of URLs per day — then extraction pipelines convert each page into structured records. Unlike sampled scraping, coverage is measured and reported at URL level, so you know what percentage of the marketplace your dataset actually captured. Built for market-wide indices, complete catalogs and data products where 'most of it' isn't enough.

Get free sample in 48h See delivery specs
99%+field accuracy, QA-verified
48hfree sample turnaround
24/7pipeline monitoring
ISO 27001+ 9001 certified

Key facts

  • Throughput: Up to 10M+ pages/day sustained, per-target rate limits enforced
  • Coverage: URL-level discovery/extraction reporting on every run
  • Refresh: One-time snapshots, scheduled full crawls, or incremental change tracking
  • Formats: JSONL/Parquet to S3/GCS, or warehouse-direct (Snowflake, BigQuery)

Scraping answers "what does this page say?" Crawling answers a harder question: "how many pages are there, and did we get them all?" For market-share analysis, complete catalog builds and data products, a sampled feed silently biases every downstream number — you can't compute share of anything without knowing the denominator.

Our enterprise crawls start with exhaustive discovery (category trees, sitemaps, pagination, internal links), deduplicate and canonicalize URLs, then run extraction at sustained scale — up to tens of millions of pages daily, rate-limited per target to stay a polite guest. Every delivery ships with a coverage report: URLs discovered, extracted, failed and why.

WHAT'S INCLUDED

Coverage you can measure,
not assume.

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Exhaustive discovery

Category trees, sitemaps, pagination, search enumeration and link graphs combined — so the crawl finds pages the sitemap forgot.

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URL-level coverage reports

Discovered vs extracted vs failed, per section, per run. The denominator for every share metric you'll compute.

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Dedup & canonicalization

Mirrors, tracking parameters and variant URLs resolved to canonical records — no double-counted listings.

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Sustained scale

Architected for tens of millions of pages per day, throttled per-target to respect platform stability.

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Change-aware recrawls

Full crawls establish the baseline; incremental recrawls track new, changed and delisted pages efficiently.

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Historical snapshots

Point-in-time captures retained on request, so 'how did the catalog look in March?' has an answer.

SEE THE DATA FIRST

What you'll actually receive.

Real sample structure from this feed. Your free 48-hour sample comes in your category, in this shape — CSV, JSON or straight to your warehouse.

Full-catalog crawl output with coverage flags — marketplace-wide.
url_id category title price seller first_seen crawl_status
a1f92 Electronics > Headphones boAt Rockerz 450 1499.00 boAt Official 2026-06-01 extracted
a1f93 Electronics > Headphones boAt Rockerz 450 (Blue) 1499.00 RetailNet 2026-06-01 extracted
a1f94 Electronics > Earbuds Noise Buds VS104 1099.00 Noise 2026-07-02 new
a1f95 Electronics > Headphones (delisted) — — 2026-05-20 gone
↑ Sample structure — illustrative values. Your data reflects your platforms and category. Get this for your data →
DELIVERY SPECS

The details procurement asks for.

THROUGHPUT Up to 10M+ pages/day sustained, per-target rate limits enforced
COVERAGE URL-level discovery/extraction reporting on every run
REFRESH One-time snapshots, scheduled full crawls, or incremental change tracking
FORMATS JSONL/Parquet to S3/GCS, or warehouse-direct (Snowflake, BigQuery)
TIMELINE Discovery audit in ~1 week · first full crawl typically 1–3 weeks by platform size
PRICING Scoped per crawl: platform size × depth × frequency; snapshot and recurring models
COMPLIANCE Public pages only · robots-aware crawling policy · ISO 9001/27001 · NDA-first

Every engagement is NDA-first and starts with a free sample — judge the data before any commitment.

THE HONEST COMPARISON

Why a data layer, not a DIY script or a rigid tool

In-house DIY Generic SaaS tool iWeb Data Scraping
Setup & maintenance You build scrapers, fight anti-bot, fix breakages weekly Rigid templates, breaks on site changes, slow support Fully managed — we build, monitor and fix, you never touch a proxy
Data quality Best-effort, no QA layer, silent failures Generic parsers, frequent gaps 99%+ field accuracy, QA-verified, monitored 24/7
Coverage Limited to what you can maintain Only supported sites Any public site or app, at scale
Compliance Your legal risk to manage alone Often opaque about methods ISO-certified, PII-scrubbed, NDA-first, documented
Time to value Weeks to months of engineering Fast but inflexible Free sample in 48h, production in days
WHO USES THIS

Built for the person
who owns the number.

DATA PRODUCT TEAM

Build on a complete index

Your product's coverage claim is only as good as the crawl underneath it — ours ships with the receipts.

MARKET RESEARCH

Real denominators for share metrics

Share of listings, sellers or assortment computed on measured coverage, not extrapolated samples.

RETAIL INTELLIGENCE

Full-catalog competitive assortment

Every SKU a rival lists — including the long tail where assortment strategy actually hides.

RELATED:Managed Web Scraping →Alternative Data →Datasets →
FAQ

Before the first call.

Crawling is discovery — finding and traversing every page of a platform (breadth). Scraping is extraction — pulling structured fields from those pages (depth). Enterprise projects need both: our crawls establish measured, complete coverage, then extraction turns each discovered page into clean records.

Every run ships a coverage report: URLs discovered by each method, successfully extracted, failed with reasons, and deltas versus previous runs. For share-of-market metrics this report is the denominator — auditable, not asserted.

Yes — sustained large-scale crawling is an infrastructure discipline: distributed fetching, adaptive rate limits per target, and anti-bot handling maintained by a dedicated team. We throttle to protect target stability; polite crawls are also the durable ones.

Both. One-time snapshots suit catalog builds, migrations and research; scheduled full crawls plus incremental change-tracking suit data products that must stay current. Many clients start with a snapshot and graduate to incremental refresh.

Get a free sample dataset in 48 hours.