A scraping pipeline built around your targets, not the other way around
When your data needs don't fit a self-serve API — non-standard targets, unusual schemas, strict compliance requirements — our data engineering team designs, builds, and maintains the pipeline for you. You get the data. We own the infrastructure.
target: "client-catalog-portal.com"
auth: session-based
render: true
schema:
- sku
- price_history
- stock_by_region
delivery: warehouse
frequency: "hourly"
owner: data-eng-team
Schema validated
Auto-alerts on drift
Trusted data from leading platforms
What "custom" actually means here
Custom web scraping is a managed service: instead of integrating an API and handling the edge cases yourself, a dedicated engineering team designs the pipeline against your exact target sites and schema, then keeps it running. It's the right fit when targets don't behave like typical e-commerce or listing pages — think authenticated portals, PDF-based catalogs, sites with heavy anti-bot measures, or data spread across dozens of inconsistent sources.
You tell us what the data needs to look like when it lands. We handle everything upstream of that — collection, rendering, parsing, validation, and delivery — and we own fixing it when a target site changes.
- Built for your exact schema, not a generic template
- Handles authenticated, JavaScript-heavy, or anti-bot-protected targets
- Maintained and monitored by our team, not yours
- Delivered to the infrastructure you already use
- Backed by an uptime SLA and a named point of contact
Why teams stop trying to build this themselves
These are the problems that usually show up somewhere between the second and sixth month of an in-house scraping project.
Non-standard targets don't fit templates
Authenticated portals, PDF-based catalogs, and sites with heavy client-side rendering need custom logic — generic scrapers stall out on exactly these cases.
Nobody owns the fix when it breaks
A markup change turns into a support ticket nobody has time for, and "we'll fix it next sprint" quietly becomes three months of stale data.
Compliance and data-handling questions pile up
Legal wants to know what's collected, how, and where it's stored — questions an ad hoc script was never built to answer.
A pipeline your team never has to touch
Every custom engagement starts with discovery, but the platform underneath is the same battle-tested infrastructure powering our API — proxy rotation, rendering, and monitoring included.
Schema-matched extraction
Fields are mapped to your exact schema during discovery, not adapted after the fact.
Built for hard targets
Authenticated sessions, anti-bot measures, and inconsistent markup are handled as part of the build, not an afterthought.
Monitored around the clock
Structural changes on target sites trigger alerts and get fixed by our engineers, usually before you'd notice a gap.
One point of contact
A named engineer owns your pipeline — no ticket queue, no rotating support reps.
{
"sku": "CL-88213",
"price_history": [48.00, 45.50],
"stock_by_region": {
"us": 142, "eu": 76
}
}
From first call to a running pipeline
Discovery
We map your target sites, schema, edge cases, and delivery requirements in a working session with your team.
Build & QA
Engineers build the pipeline against a staging sample, validating output against your schema before anything goes live.
Deploy
The pipeline goes live on your delivery schedule, with backfill of historical data where it's needed.
Monitor & maintain
We track target-site changes and data quality continuously, fixing issues before they reach your dashboards.
Deliverables at each stage
Discovery document
A written scope covering target sites, schema, refresh frequency, and delivery format, signed off before build begins.
Staging pipeline + sample dataset
A working pipeline against a sample of targets, with output validated against your schema for review.
Production pipeline + historical backfill
Full deployment on your delivery schedule, with historical data backfilled where the project calls for it.
Monitoring, maintenance, and a monthly health report
Continuous monitoring plus a monthly summary of uptime, data quality, and any target-site changes handled.
Choose how you want to work with us
Project-Based
Best for a one-time build or a defined scope with a clear end date.
- Fixed scope and timeline
- Pipeline handed off at completion
- Optional maintenance add-on
Managed Pipeline
Best for ongoing data needs where reliability matters more than owning the code.
- Build, hosting, and maintenance included
- 99.95% uptime SLA
- Named engineer as point of contact
Enterprise Retainer
Best for teams running many pipelines across multiple business units.
- Dedicated engineering capacity
- Priority turnaround on new targets
- Quarterly business reviews
Custom builds across industries
E-commerce & Retail
Multi-region catalogs and pricing feeds with inconsistent structure across brands.
Travel & Hospitality
Fare and availability data from booking systems with authenticated sessions.
Real Estate
Listings aggregated from portals and PDF-based broker sheets.
Finance & Investment
Alternative-data pipelines built to strict compliance and audit requirements.
Custom web scraping vs. the alternatives
| Capability | Custom Web Scraping | Self-serve API | In-house build |
|---|---|---|---|
| Handles non-standard, authenticated targets | |||
| Schema matched to your internal fields | |||
| Maintenance owned by someone else | |||
| Engineering time required from your team | Minimal | Some integration work | Ongoing, indefinitely |
| Time to production | 2–4 weeks | Days | Months |
Built for the targets other vendors turned down
"We'd been told our supplier portals were 'too custom' to scrape reliably by two other vendors. ScraperScoop's team had a working pipeline in three weeks."
"The monthly health report alone has saved us hours — we know exactly what changed on target sites before it ever affects our numbers."
"Compliance sign-off was the hard part internally. Having a documented, auditable pipeline made that conversation straightforward."
Delivered straight into your infrastructure
No new tools for your team to learn — data lands where you already work.
Get a scoped estimate for your project
Tell us your targets and schema — most quotes come back within two business days.
Questions we get before a first call
Tell us about your target sites
A data engineer will scope the project and come back with a realistic timeline and quote — no commitment required to start the conversation.
Most projects begin production within 2–4 weeks of kickoff.
