Given only a tracking number, which carrier is it? The answer is a pattern library, and this is one of the few places it is written down.
ScrapeIt runs the collection as a managed service. You name the regions or take the full directory; we build the pipeline with format rules expressed as applicable patterns, overlaps recorded, postal conventions flagged, and hand back CSV, JSON, Excel or a push into your warehouse.
Formats change slowly, so this is usually a one-time build with occasional refresh rather than a monitored feed, and we quote it that way instead of selling a subscription for static data.
We collect published reference data only. No tracking events for third party shipments, no recipient information. Crawl rules are honoured and requests paced. Your counsel should see the use case before the project starts if the product will handle tracking data for real consignees.
Carrier records carry the carrier name, country, carrier type, website and the service descriptions published for it.
Format records are the heart of the dataset: the pattern as documented, expressed both as published and as a regular expression, with length, prefix, suffix and any check digit convention as separate fields so the rule can be applied rather than only read.
Ambiguity is recorded rather than resolved away. Where a pattern is shared by several carriers, the overlap is a field, because a detection system that does not know a number is ambiguous will confidently route it to the wrong carrier.
Universal postal formats are flagged separately, since a number following the international postal convention encodes an origin country and belongs to a class of its own rather than to one operator.
Every row carries the collection timestamp and the source address.
Detection testing is worth building alongside the dataset: a set of known numbers per carrier to check that the rules actually match what they claim. Rules documented in prose frequently disagree with reality at the edges, and testing is how that gets found before a customer does.
Postal operator coverage mapping answers which national posts are documented and which are not, which for a cross-border product is a direct list of where manual handling will still be needed.
Overlap analysis across the whole rule set shows where detection is inherently ambiguous, and it is the input to a sensible fallback design rather than an afterthought.
Scope is the same as for any tracking source. We collect carrier and format reference data. We do not collect tracking events for shipments that are not the client's own, because those describe parcels moving toward named recipients and that is personal data whatever route it arrived by.
Parcels App is a shipment tracking service covering a wide set of postal operators and couriers internationally, with a public carrier directory describing each one.
What makes it worth collecting separately from other aggregators is emphasis. The directory documents how carriers' tracking numbers are structured - prefixes, lengths, check digit conventions, the country codes embedded in postal formats - which is the raw material for a problem every shipping product eventually hits: a customer pastes a number and gives no carrier.
Carrier detection from a number alone is a pattern matching exercise with real ambiguity, because formats overlap and several carriers can plausibly match the same string. A useful dataset therefore has to support ranked candidates rather than a single answer, and that shape has to be decided before collection.
The carrier directory responds directly with substantial content. Crawl rules are published, allow the site generally, and point at a sitemap.
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Carrier detection is one of those problems that looks solved until it is deployed, and the failure mode is quiet.
Formats genuinely overlap. A thirteen character string ending in a country code fits the international postal convention and could belong to any of dozens of operators. A numeric string of a common length matches several couriers. A system returning one carrier for such a number is not detecting, it is guessing with a confident interface, and the customer gets a tracking page for the wrong company.
Building it properly means returning ranked candidates with the matched rule attached, so the calling application can ask the customer, try several, or apply its own priors about which carriers it actually uses. That design decision has to be made at collection, because a dataset that recorded only the first matching carrier cannot be repaired later.
The second reason to use this source is coverage of postal operators. Express couriers are documented in many places; national posts in smaller countries are documented in few, and those are exactly the carriers a cross-border e-commerce product struggles with.
The third is that this is reference data with a long shelf life. Formats change rarely, so a well built dataset stays useful for years rather than needing constant refresh.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipeline, tests the rules against known numbers rather than trusting the documentation, and refreshes the set when carriers change their conventions.
You see a sample first, in your format, over the carriers you actually encounter, with overlaps recorded so you can see immediately where detection will need a fallback.
We can give you the rule set that does it, returning ranked candidates rather than one answer. Formats genuinely overlap, and a system returning a single carrier for an ambiguous number is guessing with a confident interface - the customer gets the wrong company's tracking page.
Because resolving them requires context we do not have and you do - which carriers you actually use. Recorded as overlaps, your application can ask the customer, try several, or apply its own priors. Resolved silently, it just gets them wrong.
Mostly, and not always at the edges, which is why we recommend building a test set of known numbers alongside. Rules written in prose disagree with practice often enough that testing is how you find it before a customer does.
Rarely. Formats change slowly, so this is usually a one-time build with occasional refresh. We quote it that way rather than selling a subscription for data that is essentially static.
Not for shipments that are not yours. A tracking event describes a parcel moving toward a named recipient, which is personal data whatever route it arrived by. For your own shipments, the platform's API is the right tool.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
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