The rates are behind a quote form, and no amount of crawling changes that. What is public is the market commentary, and it is worth more than it looks.
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CSV · Excel · JSON · XML · FTP · SFTP · Amazon S3 · Google Cloud
API, JSON feed or CSV, XLSX and JSON files
ScheduleOne-time, monthly, weekly, daily or 3 times a day
Flexport pages, filters, fields and a schedule
Built, monitored and repaired by our team
API, JSON feed or CSV, XLSX and JSON files
ScrapeIt runs the collection as a managed service. You name the lanes, modes or topics; we build the pipeline with numeric claims extracted as rows carrying their periods and sources, lane and mode as fields, and hand back CSV, JSON, Excel or a push into your warehouse.
Publication is irregular, so we collect on a schedule that catches new pieces promptly rather than one that re-reads a static library daily.
We collect published research only. There are no public rates on this source and we will tell you that before quoting rather than after. Requests are paced. We collect only publicly available data - research, indices and service network pages, everything a visitor can see on Flexport - and we collect it legally.
Research records carry the title, publication date, author or team as credited, trade lane or region, mode - ocean, air or ground - topic tags, summary and the full text where a client needs it for analysis.
Where a piece contains numeric claims - transit times, capacity changes, congestion figures, index values - those are extracted as their own records with the value, unit, the period it describes and the piece it came from. A number buried in prose is invisible to any query; the same number as a row with a source reference is usable and checkable.
Mode and lane are first class fields, because ocean and air behave differently enough that a combined analysis is rarely meaningful, and lane is how anybody in freight actually asks a question.
Service network data - the routes and services described publicly - is collected where published, since coverage is a structural fact about a forwarder that is otherwise hard to assemble.
Every row carries the collection timestamp and the source address.
Numeric claim extraction is the technique that makes this source worth the work, and it needs care: a figure without its period and its qualification is worse than no figure. We keep the surrounding claim text alongside the extracted value so a user can always see what the number was actually asserting.
Lane coverage analysis over the research corpus shows which trade lanes get attention and when, which is a rough but real proxy for where disruption and volume are concentrating.
Timeline analysis around disruptions - a canal closure, a port strike, a capacity withdrawal - shows how commentary builds and resolves, and is useful for anyone building an early warning process.
Every extracted claim row carries the value, the unit, the period it describes, the surrounding claim text, the lane and mode it concerns, and the piece it came from with its publication date.
Flexport is a digital freight forwarder handling ocean, air and ground freight with a technology platform wrapped around traditional forwarding. It moves goods and it also publishes an unusual amount of commentary about the market it operates in.
The first thing to be clear about is what cannot be collected. Rates here are quoted, not listed. There is no public price list to scrape, and a brief that assumes otherwise needs correcting before anybody is charged for a pipeline that will come back empty.
What is public is genuinely useful: market research, capacity and congestion commentary, trade lane analysis and occasional indices, published as a research output rather than as a rate card. For a shipper or an analyst tracking conditions rather than shopping for a price, that is the right material.
Everything the research sections show can be collected: title, publication date, credited author or team, trade lane or region, mode, topic tags, summary and full text, plus the numeric claims inside each piece.
Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan detailsA research library looks like reading material rather than data, and that is why most of its value goes uncollected.
The numeric claims inside it are the dataset. Transit times, capacity commentary, congestion figures and index values published over months form a time series about market conditions that nobody assembles because each figure arrives wrapped in a paragraph. Extracted with the period and the source attached, they become a series a client can actually plot against their own costs.
The second reason is that forwarder commentary is a leading view. A company physically moving freight sees congestion, blank sailings and capacity shifts before they show in published indices, and the timing of what gets written about is itself informative.
The third is scope honesty, which on this source has to come first. No rates are public. Anyone needing actual prices needs rate benchmarks published by other sources, which we can collect as well, or their own quotes, and we say so at scoping rather than delivering a research corpus to somebody who was expecting a price list.
The fourth is that this is one forwarder's view. It is a well-informed one and it is not the market, so a serious conditions dataset reads several sources and notes where they disagree.
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, retunes claim extraction as writing formats change, and repairs the collector before a conditions series misses the month something happened.
You see a sample first, in your format, over the lanes you actually ship, with numeric claims extracted and their source text attached so you can judge the extraction rather than trust it.
Still unsure? Write to info@scrapeit.io
No, and we will say so before quoting. Rates here are quoted rather than listed - there is no public price list, so a pipeline aimed at one comes back empty. For actual prices you need rate benchmarks published by other sources, which we can collect as well, or your own quotes.
The numeric claims inside the research - transit times, capacity and congestion figures, index values - extracted with their periods and sources. Published over months they form a conditions series nobody assembles because each figure arrives wrapped in a paragraph.
As reliable as the claim it came from, which is why we keep the surrounding text on the row. A figure without its period and qualification is worse than no figure, so the extraction is auditable rather than presented as a clean measurement.
No. It is well informed and it is one company's perspective. A serious conditions dataset reads several sources and notes where they disagree - the disagreements are often the most informative part.
Yes. The ScrapeIt Flexport API returns research titles with publication dates, trade lane, mode, topic tags and the extracted numeric claims with their periods and sources as JSON from an endpoint we host, refreshed on the schedule you set. The same data also comes as CSV or XLSX files or straight into your database.
You share your needs, expectations, and desired timeframe. We'll suggest the best solution based on your request and budget.
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
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.
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.
Tell us which sites, which fields and how often. A couple of lines is enough to start.
Flexport data from €169/month. Free project assessment, reply within 1 business day.