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Web scraping and data extraction

Web Scraping Services

Turn public web pages into clean, structured datasets with a documented schema and a repeatable pipeline.

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CLUSTER_02 · 148 POI40.7128, -74.0060CLUSTER_01 · 96 POI37.7749, -122.4194LAT 40.80LAT 40.72LAT 40.64LON -73.94
Extracted locations after geocoding and validation

Overview

A pipeline, not a one-off script

Most scraping projects fail at the second run, not the first. A page template changes, a category is paginated differently, a field moves inside a tab, and the dataset quietly loses a third of its rows. The extraction is the easy part; keeping it correct is the work.

We build each project as a pipeline with three separate layers: fetching, parsing and validation. When a site changes, only the parsing layer needs attention, and the validation layer tells us before you notice anything in the data.

Every project ships with a field dictionary, a record count reconciliation against the source's own listings where one exists, and a written note on what could not be collected and why. If we think an official API or a licensed feed is a better answer than scraping, we say so.

Sample output

The same data, mapped

A static preview rather than an embedded map SDK, so the page stays fast. Interactive maps are built on request as part of a visualization project.

CLUSTER_02 · 148 POI40.7128, -74.0060CLUSTER_01 · 96 POI37.7749, -122.4194LAT 40.80LAT 40.72LAT 40.64LON -73.94
Extracted locations after geocoding and validation

Scope

What we can extract

Anything published on a page you are permitted to access can usually be turned into a column. Common targets include:

  • Product and catalogue data

    Names, identifiers, specifications, availability and published prices across categories.

  • Business directories

    Company names, addresses, contact details, categories and branch listings.

  • Listings and inventory

    Property, vehicle, job or venue listings with their published attributes and posting dates.

  • Reviews and ratings

    Aggregate scores, review counts and, where the scope allows, review text for sentiment work.

  • Content and articles

    Titles, authors, publication dates, tags and body text for research corpora.

  • Documents and tables

    Values locked inside published PDFs, statistical tables and open-data portals.

  • Change history

    Repeated collection of the same pages so you can measure what changed between runs.

  • Images and media references

    Source URLs and metadata, collected for analysis rather than redistribution.

Output formats

Delivered the way your stack expects

CSV
Flat, UTF-8, one row per record. The fastest way to hand data to an analyst.
Excel
Workbook with data, field dictionary and run summary sheets.
JSON / JSONL
Nested records for anything that does not flatten cleanly, and line-delimited output for large volumes.
Parquet
Columnar output for large datasets loaded into a warehouse or a data lake.
Database load
Direct load into PostgreSQL, PostGIS, BigQuery or an S3 bucket you control.

Use cases

What clients use it for

Sample dataset

What you actually receive

Sample project data. Every delivery carries a collection timestamp so you always know how fresh a row is.

Sample project · Store locator extract · schema excerpt
Record IDStore nameCityPostcodeOpenedStatusCollected at
ST-000141Riverside MarketPortland972092018-04Open2026-08-11
ST-000142Pearl District MarketPortland972102021-09Open2026-08-11
ST-000143Alberta Street MarketPortland972112016-11Temporarily closed2026-08-11
ST-000144Beaverton MarketBeaverton970052019-02Open2026-08-11
ST-000145Vancouver WaterfrontVancouver986602023-06Open2026-08-11

Delivered work

This service on a real project

Sample projects built on this service, with the numbers they produced and what each one settled.

POI DataData CollectionMarket Research

POI Data Analysis

Building a comparable category census across five metro areas, and the standardisation work that made the comparison valid.

Raw records
94,200
Unique POIs
71,480
Category labels
14 → 1

What it showed

  • Ranked by absolute count, Metro A led by a wide margin. Ranked per capita, it placed third, and the two markets the team had considered marginal turned out to be the most densely served.
  • Chain share varied from 17.9% to 51.4% across markets that industry commentary had treated as broadly similar. That spread became the central finding of the research rather than a footnote.
Read the full case study
Heatmap AnalysisGIS AnalysisMap Visualization

Urban Heatmap Data Analysis

Turning a scattered point dataset into a normalised density surface that separates real activity clusters from population artefacts.

Input records
48,600
Records corrected
9.1%
Hotspots confirmed
2 of 5

What it showed

  • Central had by far the largest raw count and by far the largest visual hotspot, but ranked third on activity per resident. The original map had been describing population distribution, not activity.
  • Harbourside, which barely registered on the unnormalised map, was the strongest genuine hotspot at 99% confidence. It has a small residential population and a high concentration of activity, precisely the pattern raw-count heatmaps hide.
Read the full case study

How it works

Five steps, every project

  1. Step 01

    Tell us what data you need

    Send the pages or sources you have in mind and the questions the data has to answer.

  2. Step 02

    We define the data scope

    We agree the field list, the coverage, the refresh cadence and the acceptable error tolerance.

  3. Step 03

    We collect and process the data

    Fetching, parsing and normalisation run as separate, monitored stages.

  4. Step 04

    We validate the dataset

    Row counts, field completeness, type checks and duplicate detection run on every batch.

  5. Step 05

    We deliver the final result

    Data lands in your chosen format or destination, with documentation and a run report.

Every extraction is quoted on its own scope: volume, source complexity and, for ongoing pipelines, run frequency and monitoring requirements. Send us the sources and the fields and you get a fixed price before anything runs.

FAQ

Questions we get asked

What kind of websites can you work with?

Publicly accessible pages, open data portals, published documents and sources you are authorised to access. Server-rendered and JavaScript-rendered pages are both fine. We do not work on sources that require defeating authentication, paywalls or anti-bot protections.

How do you handle sites that change?

Parsing is isolated from fetching, and every run is validated against expected row counts and field completeness. When a template changes, the validation step fails loudly and we repair the parser, usually within a business day on a maintained project.

Can you run collection on a schedule?

Yes. Daily, weekly and monthly schedules are all common. Scheduled projects include change logs so you can see exactly which records were added, removed or modified between runs.

How do you handle duplicates?

We agree a business key up front, for example a source identifier or a name-and-address pair, then deduplicate against it. Near-duplicates are resolved with normalised string matching and, for locations, a distance threshold. The rules are documented in the delivery.

Who owns the resulting data?

You do. We deliver the dataset and the documentation, we do not resell your project's output, and we delete working copies on request once a project closes.

What if the data is not available publicly?

Then we say so early. Depending on the case we will point you at an official API, a licensed provider, or a survey-based approach. A clear no is more useful than a dataset you cannot rely on or use.

More services

Often combined with

Street View Data

Street-level imagery turned into measurable indicators for every segment of a network.

Learn More

Semantic Segmentation

Pixel-level image analysis that turns imagery into counted, measured classes.

Learn More

POI Data

Business and point-of-interest records with categories, coordinates and attributes.

Learn More

Keep exploring

Where to go next

Related services

Related case studies

Related reading

Next step

Have a specific data requirement?

Tell us the geography, the fields and the cadence you need for web scraping. You get a scoped plan, a sample and a fixed price before any work starts.

Request a QuoteView Case Studies