Multi-source data collection

Turn difficult digital sources into dependable data.

Crawlr collects the information your teams need from websites, mobile apps, APIs, authenticated platforms, files, and complex digital products, then validates and normalizes it into a consistent operational dataset.

See the collection workflow

Collection you can operate

Source changes become visible exceptions, not silent data failures.

Every delivery retains the context needed to understand freshness, schema validity, and source-level collection health.

Source coverage
DEFINED
Schema validation
CHECKED
Change handling
MONITORED

How collection works

From representative sources to a repeatable data feed.

  1. 01

    Define the scope

    Agree the sources, markets, categories, fields, cadence, and decisions the dataset must support.

  2. 02

    Validate the source

    Collect representative inputs, normalize the schema, and expose access and structural edge cases before scale.

  3. 03

    Deliver and monitor

    Run recurring collection with health signals, historical snapshots, and explicit exception handling.

Operating detail

What goes into the workflow—and what comes out.

Capabilities

  • Website, mobile app, API, platform, and file collection
  • Field normalization and schema validation
  • Change monitoring, retries, and collection health signals
  • Historical snapshots for trend and event analysis

Inputs

  • Target sources and access method
  • Business entities and scope
  • Required fields
  • Refresh cadence

Outputs

  • Normalized listings
  • Collection health summaries
  • Historical snapshots
  • Source-level diagnostics

Use cases

  • Competitive monitoring
  • Catalog discovery
  • Market research
  • Data enrichment

Delivery modes

  • API
  • Scheduled files
  • Agreed cloud delivery
  • Private client web app

Multi-source data collection FAQ

Answers about coverage, cadence, and source changes.