Fresh, resilient collection at scale
Monitor recurring runs, source changes, validation results, and historical observations without owning every crawler failure mode.
Build a commerce intelligence system around your market.
Crawlr turns fragmented web signals into collection, product-matching, monitoring, and delivery workflows designed around the decisions your business needs to make.
The data problem.
Commerce intelligence breaks when collection is fragile and product identity is unreliable.
Fragile in-house collection
A crawler can start quickly, then become a permanent maintenance queue as layouts, defenses, markets, and catalog structures change.
Inconsistent product identity
Titles, identifiers, variants, units, and pack sizes differ by source. Without dependable matching, price and assortment comparisons become misleading.
One dependable data foundation.
From changing web pages to intelligence your teams can use with confidence.
Crawlr connects collection, normalization, matching, monitoring, and delivery so every downstream decision starts from consistent evidence.
Collect continuously
Turn retailer, marketplace, category, product, seller, and review pages into recurring structured datasets with source-level health checks.
Match reliably
Resolve identifiers, attributes, variants, units, and pack sizes into explainable product relationships, with ambiguous candidates kept reviewable.
Act with confidence
Deliver normalized observations, matched histories, exceptions, and alerts through APIs, scheduled files, or an agreed cloud workflow.
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Define target sites, markets, and decision-ready outputs
Choose the sources, products, countries, fields, collection cadence, matching rules, and downstream decisions the dataset must support.
Crawlr collects, cleans, normalizes, and matches
Recurring collection and validation create consistent observations while identity logic connects equivalent products across changing catalogs.
Receive data through the delivery mode that fits your stack
Use an API, scheduled files, or an agreed cloud delivery workflow, with health signals and exceptions that make the output operational.
Built for repeatable commerce-data operations.
Technical depth for data teams, with outputs business teams can act on immediately.
Explainable matching and confidence
Keep normalized attributes, match evidence, confidence, and review status attached to every proposed product relationship.
Flexible delivery, monitoring, and alerts
Move data into your workflow through APIs, scheduled files, or agreed cloud delivery, with exceptions surfaced for action.
Commerce intelligence platform
Six connected capabilities, from source collection to decision-ready outputs.
Start with one focused workflow or combine services around a shared product identity layer. Every scope is validated against representative sources before ongoing delivery.
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Commerce data collection
Collect structured product, price, availability, promotion, content, and review data from the sources that shape your market.
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02Product matching
Connect retailer listings to the same underlying product with normalized attributes, explainable evidence, and reviewable confidence.
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03Pricing and promotions
Monitor list prices, selling prices, discounts, promotion mechanics, and change events across matched products and markets.
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04Assortment and availability
Measure assortment breadth, catalog overlap, new listings, delistings, stock signals, and regional availability over time.
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05Digital shelf and content
Monitor search placement, category visibility, titles, images, descriptions, attributes, seller context, and content completeness.
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06Brand and review intelligence
Track brand representation, seller activity, ratings, review volume, recurring themes, and agreed marketplace exceptions.
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In production.
Commerce teams power company-wide change with Crawlr
NORTHSTAR RETAIL — MOCK
Build a market view that stays current
Mock customer story: recurring collection, normalized catalogs, and change monitoring in a single operational workflow.
ATLAS BRANDS — MOCK
Own product identity end to end
Mock customer story: matched listings give teams a reviewable foundation for price, availability, and assortment decisions.
STOREFRONT LABS — MOCK
Turn market changes into team-ready signals
Mock customer story: delivery, exceptions, and alerts are designed around the systems and decisions that consume the data.
1/3
They are the real sales
The flexibility is really what made the difference. Our needs evolve very fast. I discover a new need and in two clicks I can address it. That is a real advantage when you are moving quickly.
We needed a workflow that made changing source data useful to the whole team, without turning each new question into a one-off technical project.
Crawlr gives us a clear way to define the market signals we need and put them into the workflows that already run the business.
A project can include product details, prices, promotions, availability, assortment, sellers, search or category placement, content, ratings, and reviews when those fields are available in the agreed sources.
Matching combines exact identifiers, normalized attributes, variant and pack-size logic, and project-specific evidence. Ambiguous relationships remain reviewable instead of being presented as certain.
Coverage is scoped to the retailers, marketplaces, countries, categories, and fields required by each project. Request a sample to validate representative sources before launch.
Cadence depends on the source, volume, and business decision. Crawlr agrees a realistic schedule after validating the collection scope and representative pages.
Yes. Projects can keep list, selling, member, and promotional prices separate and retain visible promotion text, seller context, currency, availability, and collection time.
Supported delivery modes include APIs, scheduled files, and agreed cloud workflows. The right mode depends on data volume, cadence, schema, and the systems that consume it.
Share the target sources, markets, approximate catalog size, and desired outputs. Crawlr will use that context to assess a representative sample without promising automated delivery or a fixed response time.
Each project is scoped around agreed public sources, fields, delivery, and handling requirements. Crawlr does not invent legal conclusions; customers should review source terms and their own compliance obligations.