The FT wrote its AI position into the one file every crawler reads first. A licence for research does not quietly extend to training a model.
Plans from €169/month · Free project assessment · Reply within 1 business day
ScrapeIt runs the work as a managed service. You name the sections, topics or companies; we build the collector, record headline, standfirst, byline, section and timestamps on every row, and hand back CSV, JSON, Excel or a push into your warehouse.
Anti-bot handling, proxy rotation and rendering are part of the service, and we never log in or go behind the paywall.
Article records carry the headline, standfirst, byline where the page shows it, section, publication and update timestamps and the article identifier, as the public pages display them.
Citation records are a separate table built without the FT site: where other outlets cite or quote FT reporting, showing which FT stories shaped wider coverage.
Every row carries the collection timestamp and its source type.
Citation analysis is the strongest product: which FT reports are cited, by whom, how quickly and in what context, joined to the headline, section and publication time the FT gave each story.
Headline tracking follows the public pages over time: each run records headline, standfirst, section and update timestamp, so a retitled story or a revised standfirst shows up as a dated change.
The Financial Times is one of the most influential business newspapers in the world, published behind a subscription and read closely by finance, policy and corporate audiences.
Part of it is still public. Front and section pages list each story with its headline, standfirst, section and timestamp, and every article page opens with its headline and standfirst before the paywall starts.
We collect only publicly available data - everything a visitor can see on the Financial Times - and we collect it legally. We do not log in and do not go behind the paywall.
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Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan details →The FT is read closely by finance, policy and corporate audiences, so what it puts in front of them is itself a signal. A dated series of headlines, standfirsts and sections shows which companies, markets and policy themes the paper covers and how that mix shifts over time.
Most FT data briefs are really about influence: which FT stories moved markets or set agendas. That question is answered from two sides: what the FT put on its front and section pages and when, and how other outlets cite the FT afterwards.
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, keeps it running and repairs collection when formats change.
You see a sample first, in your format. The sample covers the sections or companies you track, so you can judge the value before committing.
Yes, the public layer: front and section pages and the part of each article page shown before the paywall, with headline, standfirst, byline where shown, section, timestamps and article identifier. Anti-bot handling is part of the service, and delivery is CSV, JSON, Excel or a push into your warehouse.
Yes. The ScrapeIt Financial Times API returns headline, standfirst, byline, section, timestamps and article identifier 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.
Yes. We collect headlines, standfirsts, bylines, sections, timestamps and article identifiers from the public pages, plus citations of FT reporting in other outlets. We collect only publicly available data - everything a visitor can see on the Financial Times - and we collect it legally, without logging in or going behind the paywall.
Yes. Each run records headline, standfirst and update timestamp, so a retitled story or a revised standfirst appears as a new row with its collection time, and the versions stay side by side.
Yes. Citations of FT reporting in other outlets show which stories shaped coverage, how fast and where. That answers most influence questions without touching ft.com.
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.
Financial Times data from €169/month. Free project assessment, reply within 1 business day.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
REGON: 523384582
