Web scraping and data extraction systems that deliver usable data.

We build systems that collect, structure and deliver web data for businesses: extraction from the sources you need, cleaning and validation, and delivery into a database, file, API or the tools your team already uses, once or on a schedule.

Prefer email? contact@re4loai.com

Scope

Source analysis
Extraction
Cleaning
Normalisation
Scheduling
Monitoring
AI enrichment
Delivery

/ What we build

A data pipeline, not a one-off script.

A script that works once is the easy part. What a business needs is data that keeps arriving in the right shape, from sources that change without warning, into the place it’s actually used.

Source analysis

Before any code, we look at the pages you need: how they’re structured, whether content is rendered in the browser, how they paginate, and what limits access.

Extraction logic

Crawlers and parsers written for each source, including browser automation where pages only render their content with JavaScript.

Structuring & cleaning

Raw values become consistent records: fields mapped to a schema, formats normalised, duplicates removed and missing values flagged.

Storage

Records kept in a database with the source URL and collection time, so you can see where each value came from and how it changed.

Scheduling & monitoring

Runs on a schedule or on demand, with checks that notice when a source changes or a run returns empty or malformed data.

Delivery & integration

Data delivered in the format and place it’s used: files, a database, an API, a spreadsheet or straight into your CRM or internal tools.

/ Types of data

The kinds of web data businesses ask for.

Each project starts from what the data is for. These are the usual categories; most projects combine a few of them.

Product & catalog data

Product names, specifications, categories, images and identifiers from catalogs and retailer pages.

Pricing & availability

Prices, stock status and changes over time, collected on a schedule so differences between runs are visible.

Business & directory listings

Company names, categories, locations and public contact details from directories and listing sites.

Public-record & listing data

Publicly published records, property and real-estate listings, tenders, and job postings.

Reviews & public content

Published reviews, ratings and articles, structured so they can be searched, counted or analysed.

Market & competitor pages

Service pages, offers and announcements from the sites you track, captured so changes can be compared.

/ How it works

From a web page to a record your systems can use.

A run starts on a schedule or on request. The extractor fetches each source page, follows pagination, and parses the fields you’ve asked for.

Structuring turns what was parsed into clean records: one schema, consistent formats, duplicates removed, and anything that fails validation flagged rather than passed through.

Records are stored with their source and timestamp. Where it’s useful, an AI or automation step classifies or enriches them, and the result is delivered to the system that needs it.

The output is a dataset you can trust enough to build on.

Fig. CFrom web source to client system

Sourcepublic pages
Extractionfetch · parse
Structuringclean · validate
Databaserecords + history
AI / Automationclassify · enrich
Client systemAPI · CRM · file

InputStepAIOutput

/ Structuring & delivery

Clean data, delivered where it’s used.

Most of the value is in what happens after extraction. The same processing applies whether the output is a single file or a live feed.

Processing

What happens to the data

Field mapping to an agreed schema, so every record has the same shape.
Normalisation of dates, prices, currencies, units, names and addresses.
De-duplication within a run and across runs.
Validation rules that flag missing, malformed or out-of-range values.
Source URL and timestamp kept on every record.
Change tracking between runs where history matters.

Delivery

Where it can go

CSV or Excel files for one-off datasets and reporting.
JSON for developers and downstream processing.
A database such as PostgreSQL, owned by you or hosted for the project.
An API endpoint or webhook that your applications call or receive.
Google Sheets, cloud storage or a dashboard, where the team already works.
Your CRM or internal tools, connected through their APIs.

/ Recurring extraction

One-time datasets and scheduled pipelines.

Some projects need a dataset once. Others need the same data kept current. The difference changes how the system is built.

One-time extraction

A defined dataset collected, cleaned and delivered as files or a database export. No ongoing infrastructure to run.

Scheduled runs

The pipeline runs hourly, daily, weekly or on a custom schedule, and each run updates the stored records and their history.

Change monitoring

Selected pages are checked for changes such as new listings, price movements or updated content, and changes trigger a notification or a workflow.

Scheduled pipelines include run checks: a source that suddenly returns nothing, or returns data that fails validation, raises an alert instead of quietly delivering a broken dataset.

/ Where AI fits

AI where the data needs judgment, not everywhere.

Most extraction is ordinary engineering: selectors, parsers and validation rules. AI is added for the steps that need reading or judgment, and its output is checked like any other field.

Classification

Sorting records into your categories when the source doesn’t use them, such as product type, industry or listing kind.

Extraction from free text

Pulling specific fields out of descriptions and long-form text where no fixed pattern exists.

Matching & enrichment

Matching the same entity across sources and adding derived fields, with uncertain matches flagged for review.

Summaries

Short summaries of long pages or reviews, so people can scan what changed without reading everything.

Once the data is structured, it can trigger the next step: a CRM update, a notification, a report. See how we build AI and workflow automation.

/ Technical considerations

What shapes every extraction project.

We can’t extract every website without limits, and we won’t pretend to. These are the constraints we assess before anything is built.

Access

We work with data that is publicly available or that you are entitled to access. Content behind logins, paywalls or explicit access restrictions is scoped case by case, and some of it isn’t appropriate to collect.

Site rules & load

We review each source’s robots rules and terms, keep request rates modest, and avoid putting unnecessary load on the sites we read.

Rate limits & blocking

Many sites limit or block automated traffic. That affects how fast and how often data can be collected, and sometimes whether a source is practical at all.

Dynamic pages

Pages that render content with JavaScript need a real browser to read them. That works, but it’s slower and costs more to run than reading plain HTML.

Sources change

When a site changes its layout, extraction can break. Monitoring catches it, and maintenance is agreed up front rather than discovered later.

Legal & personal data

Whether data can be collected and how it can be used depends on the source, its terms and where you operate. We raise concerns we see, but we don’t give legal advice; check anything sensitive, especially personal data, with your own counsel.

/ Use cases

What the data is typically for.

Price & catalog monitoring

Tracking competitor or supplier catalogs and prices on a schedule, with changes surfaced instead of rechecked by hand.

Market research datasets

Collecting a defined set of companies, listings or offers into one structured dataset for analysis.

Directory data for sales teams

Structuring public business listings into a clean list that can be reviewed and imported into a CRM.

Data for internal tools

Feeding external data into dashboards, internal systems or customer-facing products through a database or API.

Content for AI systems

Collecting and cleaning documentation or public content that an AI assistant or search tool will draw on.

Replacing manual copy-paste

Automating the recurring task of someone copying data from websites into a spreadsheet.

/ How we work

Sampled first, then built, then validated.

01
Scope & sample
We agree the fields, sources and output, review the sources for constraints, and test extraction on sample pages.
02
Build the pipeline
Extraction, cleaning, validation and storage are built for each source, with delivery to where the data is needed.
03
Validate the output
You review sample data against the agreed schema before full runs start, and we adjust the rules from that review.
04
Run & maintain
Scheduled runs go live with monitoring. When a source changes, the extraction is updated under the agreed maintenance terms.

Before we start

What makes an extraction project go well

A clear purpose: what decision or system the data feeds.
The fields you need, and an example of the output you’d like.
The sources, with a few sample URLs.
How often the data needs refreshing, and roughly how much there is.
Where the data should end up, and who will use it.
Who looks after the pipeline once it’s running.

If it isn’t a fit

We’ll say so before building.

If a source can’t be extracted reliably, or the data isn’t appropriate to collect, we’ll tell you during scoping and suggest an alternative where there is one, such as an official API, a data provider or a narrower scope.

/ Related

Where web data goes next.

ServiceAI & workflow automationWorkflows that act on structured data: CRM updates, notifications, routing and AI-assisted steps with human review.Read more CapabilitiesCustom software & integrationsDashboards, internal tools and API integrations built around the data, when an off-the-shelf tool isn’t enough.Read more

/ Start a Project

Tell us what data you need, and where it should end up.

Send the sources, the fields and how often you need them; sample URLs help. We’ll reply by email with how we’d approach it, and anything that could limit what can be collected.