Data scraping and extraction: the data you need, structured
Gjethe builds custom scrapers and extraction pipelines that collect publicly available data from websites and directories, or extract fields from PDFs and documents, then clean, de-duplicate and deliver it as CSV, Excel, JSON, a database or an API, on a schedule. Every project starts with a review of terms of use and personal-data rules.
- Free first consultation
- Written quote for your project
- Flexible payment milestones
At a glance
- Sources
- Websites, directories, PDFs, scans
- Delivery
- CSV, Excel, JSON, database, API
- Schedule
- One-off, daily, weekly or near real time
- First step
- Legal and terms review
Definition
What is data scraping & extraction?
Data scraping is the automated collection of information from websites; data extraction is pulling structured fields out of unstructured sources such as PDFs, scans and emails. Together they turn scattered information into datasets you can analyse and use.
Useful data often sits on websites and in documents without an export button: competitor prices, public listings, supplier catalogues, records in hundreds of PDFs. We collect it responsibly (respecting site terms, robots rules and rate limits) and structure it so your team can use it immediately. Some requests can't be done lawfully, and we'll say so before building anything. Each project starts with a review of the target sites' terms, so the collection plan respects them from the beginning.
Who is it for?
E-commerce and pricing teams
Competitor prices, availability and assortment tracked across many sites.
Real estate and marketplaces
Public listings aggregated for analysis or market reports.
Researchers and analysts
Datasets built from public sources for market research, academic work or journalism.
Operations with document backlogs
Data trapped in thousands of PDFs, scans or emails that needs to be in a database.
What's included, and what you keep
What's included
- Source and feasibility assessment
- Terms of use and personal-data review
- Custom scraper or extraction pipeline
- Polite crawling with rate limits
- OCR and AI extraction for documents
- Data cleaning and normalisation
- De-duplication and change detection
- Output in your preferred format
- Scheduled runs
- Monitoring for site changes and failures
- Data quality report per delivery
- Maintenance when sources change
What you receive
Handed over at the end. Yours to keep.
- Structured dataset in your chosen format
- Scraper or pipeline running on your account
- Field definitions and data dictionary
- Run logs and quality reports
- Documentation of sources and methods
Is web scraping legal?
Collecting publicly available, non-personal data is generally lawful in many jurisdictions, but the answer depends on the details: the website's terms of use, copyright and database rights in the content, technical access restrictions, and, whenever people are involved, data protection law such as GDPR.
We review each project before starting: what data, from which sources, for what purpose. We avoid logging in to collect data behind accounts, don't circumvent access controls, keep request rates low and minimise any personal data. When a project isn't defensible, we decline it. This is general information, not legal advice; your own legal adviser should confirm specific uses.
Delivery options
| Format | Best for |
|---|---|
| CSV / Excel | One-off datasets and teams working in spreadsheets |
| Google Sheets | Shared datasets updated on a schedule |
| Database (PostgreSQL) | Large or growing datasets queried by other systems |
| JSON / API | Feeding data into your own application in near real time |
Document extraction examples
Invoices and receipts
Supplier, dates, totals, tax and line items extracted into accounting-ready data.
Contracts
Parties, dates, renewal terms and key clauses captured into a searchable register.
Tables in PDFs
Financial statements, catalogues and reports converted into clean spreadsheets.
Forms and applications
Scanned or emailed forms turned into structured records, with uncertain fields flagged.
How data scraping & extraction works, step by step
Every step has a clear output you can review. Timing is agreed in your quote.
- 1
Scope and review
Data, sources, purpose and legal considerations agreed before any build.
- 2
Prototype
A sample extraction to confirm the data is available and usable.
- 3
Build
Scraper or extraction pipeline with cleaning and validation.
- 4
Quality check
Output checked against the source for accuracy and completeness.
- 5
Deliver
The dataset is handed over in your chosen format, or scheduled runs are switched on.
- 6
Maintain
Monitoring detects source changes, and the pipeline is updated.
Tools, and what shapes your quote
Pricing is based on your project and agreed in your quote. These are the things that move it.
| Factor | Why it matters |
|---|---|
| Number of sources | Each website or document type needs its own extraction logic. |
| Site complexity | JavaScript-heavy sites and pagination add work compared with simple pages. |
| Volume and frequency | Large datasets and frequent runs need more robust infrastructure. |
| Data cleaning | Normalising messy, inconsistent sources takes time. |
| Document quality | Scans and complex layouts need OCR and more validation. |
| Ongoing maintenance | Sites change; scheduled scrapers need monitoring and updates. |
| Area | Tools |
|---|---|
| Crawling | Scrapy, Playwright, httpx, BeautifulSoup |
| Documents | pdfplumber, OCR (Tesseract, cloud OCR), AI extraction with validation |
| Processing | pandas, dedupe tooling, pydantic validation |
| Delivery | PostgreSQL, Google Sheets, S3, REST APIs |
Common mistakes to avoid
What we see go wrong most often, and how a good project avoids it.
Ignoring terms and privacy
Collecting personal data or ignoring site terms creates legal risk that outweighs the value of the data.
Hammering websites
Aggressive crawling burdens the source and gets blocked. Polite rates are both ethical and more reliable.
No change detection
When a site redesigns, scrapers return empty or wrong data. Monitoring catches it quickly.
Skipping validation
Unchecked extraction quietly introduces errors into every decision based on the data.
Frequently asked questions
We collect publicly available data only, respect each site's terms and rate limits, and review personal-data use under GDPR before starting. Some projects are declined if they can't be done lawfully.
Is web scraping legal?
Collecting publicly available, non-personal data is generally lawful, but it depends on the site's terms, copyright and, for personal data, GDPR. We review each project first and decline ones that can't be done lawfully. This is not legal advice.
What happens when the website changes?
Scrapers break when layouts change. On a maintenance plan we monitor runs and update the scraper quickly when that happens.
Can you extract data from PDFs and scans?
Yes. We extract tables and fields from PDFs, and use OCR and AI extraction for scans and unstructured documents, with validation of the results.
Do you scrape behind logins?
Generally no. Collecting data from behind accounts usually breaches terms of use; we prefer official APIs or data partnerships in those cases.
Can we get the data continuously?
Yes, with scheduled runs (daily, weekly or more often) and change detection so you see what's new or different.
How accurate is the extracted data?
We validate fields against rules and sample-check against the source. Each delivery includes a quality report showing completeness and any issues.
Can the data go straight into our system?
Yes, into a database, spreadsheet, CRM or your own API.
Do you sell datasets?
No. We build collection for your specific, lawful purpose; the data and pipeline are yours.
Is there an official API instead?
We always check. If a source offers an API or data licence, that's usually more reliable and clearly permitted.
Who owns the scraper?
You do: code, configuration and data run on your accounts.
Can you track changes over time?
Yes. Each run is compared with the previous one, so you can see new, changed and removed items, useful for price tracking and listings.
What if a site blocks automated access?
If a site clearly doesn't permit automated collection, we don't try to get around it. We look for an official API, a data licence or a different source instead.
Can you combine data from several sources into one dataset?
Yes. We normalise names, units and categories across sources and match records that refer to the same item, so the combined dataset can be compared directly.
How is personal data handled?
We avoid collecting it where possible. When a lawful purpose requires it, we collect only what's needed, store it securely and document the basis for processing.
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Get your data.
Tell us what you need. You get an honest assessment, a clear plan and a written quote. The first consultation is always free.
- What happens next
- 1You tell us what you need
- 2We aim to reply within 24 hours with questions
- 3You get a written quote for your project