USPS publishes an API and its robots file closes the quick tracking tool. Both facts point the same way, and following them is why the data keeps arriving.
ScrapeIt builds and runs the pipeline as a managed service. You tell us the shipment volumes, lanes and carriers; we build the integration, normalise statuses across all of them, watch for silence rather than only for errors, and hand back CSV, JSON, Excel or a push into your warehouse.
Where the official developer interfaces are the right route we use them and help you get set up with credentials rather than routing around them. That is both the supported path and the reason the feed keeps arriving. Public collection is confined to open surfaces such as published retail rates, and we honour the paths the robots file closes, the quick tracking tool among them.
Tracking data describes shipments, and shipments have recipients. We do not collect recipient names or addresses beyond the postcode level needed for lane analysis, and where a client's own data contains them we keep them on the client's side. Bring the use case to your own counsel before the project starts.
Tracking output carries the tracking number, the carrier, the raw status text exactly as the service words it, our normalised status, the event timestamp with its time zone, the event location, and the scan type where it is distinguished.
Both the raw and the normalised status are delivered, always. The raw wording is what lets somebody audit a decision six months later; the normalised value is what lets a dashboard count anything. Delivering only one of them is the most common design mistake in tracking pipelines.
Shipment level fields cover origin and destination postcode, service level, weight and dimensions where available, the promised delivery date where the service publishes one, the actual delivery date, the delivery attempt count and the exception reason where one is given.
Rate records are a separate set: service level, origin and destination zone, weight band, dimensional constraints, the published price and the effective date of the price table. Postage prices change on announced dates, so the effective date is not decoration, it is what makes a historical rate comparison possible at all.
Every row carries the collection timestamp, and every derived field such as days in transit is computed from stored events rather than supplied as an unexplained number.
Rate tables age in steps rather than drifting. Prices change on announced dates, so the collection design is a periodic check plus a full re-collection when a new table takes effect, with the old table retained. Overwriting the previous prices destroys the ability to answer what a shipment would have cost last year, which is precisely the question a procurement review asks.
Reconciliation against invoices is a common extension and a profitable one. Tracked events on one side, billed charges on the other, matched by tracking number, surface surcharges applied to shipments that do not match their recorded characteristics. Clients regularly find more in that comparison than the data collection costs.
Multi carrier design should be settled at the start rather than bolted on. The status model, the lane definitions and the exception taxonomy have to be common across carriers or the second carrier forces a rebuild. We define them once, up front, and add carriers into the existing schema.
Location and service point data is a smaller but real dataset: where facilities are, what services they offer and what hours they keep. It changes slowly and suits a monthly refresh, and it is what powers a drop off locator in a checkout.
The United States Postal Service handles a volume of parcels that makes it unavoidable for any American ecommerce operation, and it is frequently the last mile carrier even for shipments that started with somebody else. Any multi carrier tracking product that omits it has a hole in the middle of its domestic coverage.
Access here has a clear signposted route. The service runs a developer programme with documented interfaces for tracking, address validation, rate calculation and location lookup, and its robots file explicitly disallows the quick tracking tool and the post office locator paths. Read together, those are not mixed signals: they say use the interfaces, not the consumer tools.
We follow that. Where a client has or can obtain credentials, the official interface is the right route and we build the pipeline around it, handling the parts that are genuinely difficult: retry behaviour, event normalisation, reconciliation and delivery into their warehouse. Public collection is confined to the surfaces that are open and useful, such as published retail rate information.
The published rate side is worth separating from tracking in any scope. Retail postage prices, service levels and zone based pricing are public reference data, and they change on announced schedules rather than continuously, which makes them a completely different collection problem from event tracking.
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A shipment in trouble rarely announces itself. It does not emit an exception event; it simply stops emitting events at all. The last scan sits there, three days old, and nothing arrives to contradict it. Any monitoring built to react to error statuses will miss most of the problems it was bought to catch.
So the useful design watches for absence. We model the expected cadence for a lane and a service level, then flag shipments whose event stream has gone quiet relative to that expectation. It is a different mechanism from status monitoring and it catches a different and larger set of failures.
The second reason is the promise. A carrier publishes an expected delivery date and delivers on some other day, and the gap between the two, measured across thousands of shipments by lane and service level, is the only honest measure of carrier performance. It needs the promise captured at ship time, because it is not always retrievable afterwards.
The third is normalisation across carriers. Nobody ships with one carrier. A dashboard showing five carriers with five status vocabularies is not a dashboard, it is five reports side by side. Mapping them onto one model, with the original wording kept beside it, is what turns multi carrier data into something countable.
The fourth is rates. Published rates by service, zone and weight band are what let a finance team check what they are being charged and model what a shipping profile would cost on a different service, and they need the effective date to be meaningful.
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 integration, watches it as interfaces and status wording change, and repairs it before your exception alerts go quiet.
You see a sample first, in your format, on your own lanes, with statuses normalised and silence detection running so you can judge the part that actually matters.
Where you can, yes, and we will help you get there rather than routing around it. The service publishes documented interfaces and its robots file closes the consumer tracking tool, which points the same way. What we add is everything after retrieval: normalising statuses across all your carriers, detecting silence, reconciling against invoices and delivering into your warehouse.
Mapping each carrier's wording onto one model - accepted, in transit, out for delivery, delivered, exception - while keeping the original text on the same row. The normalised value is what a dashboard can count; the raw wording is what lets somebody audit a decision months later. Delivering only one of them is the most common mistake in this kind of pipeline.
By watching for silence. A troubled shipment usually stops producing events rather than reporting an exception, so monitoring built around error statuses misses most real problems. We model the expected event cadence per lane and service level and flag streams that go quiet against it.
Yes, as a separate dataset from tracking, because it behaves completely differently. Rates change on announced dates rather than continuously, so each table is stored with its effective date and previous tables are retained. Overwriting them destroys the ability to answer what a shipping profile would have cost last year.
No. Tracking analysis needs origin and destination at postcode level for lane work, and nothing more. Recipient identities stay on your side of the boundary. If a brief asks for them we will explain why the analysis does not need them.
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.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
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