Web scraping services, for public records only, run by us.
Hiring a web scraper gets you a script that breaks the first time a site changes. Recordpipe runs collection as a managed service: we build it, operate it on production infrastructure, monitor it, and fix it when sources change. You get structured data on cadence. We collect from publicly available government and public-record sources only, and we decline login-walled, private, and personal-data targets.
Enterprise web scraping, managed
Large-scale collection across hundreds of public sources, one output schema.
Maintained when sources change
Source changes are our problem, handled inside the retainer, not a new ticket for your devs.
Structured, deduplicated delivery
Normalized fields, entity resolution, and deltas by webhook, API, or file drop.
Public data only
Government records, registries, filings, and published reports. Nothing behind a login, nothing private.
Managed web scraping services for public and government data only
Recordpipe's web scraping services collect from publicly available government and public-record sources — county recorders, assessors, courts, state registries, licensing boards, permit offices — and nothing else. We say that plainly because most web scraping companies will take any target. We decline login-walled sites, private platforms, social profiles, and anything built to harvest personal data. If a record is public and published by a public body, it is in scope. If it is not, we do not build it.
Inside that boundary, the service is fully managed. Your team does not write scrapers, run them, or fix them. Our RPA and automation bots do the collection on our infrastructure, parsers normalize the output to your schema, and the pipeline runs on the cadence you set. You receive data — files, warehouse tables, webhooks, or an API — with a capture date and source category on every row.
The engagement is fixed-price. Scoping tells you what is feasible and what it costs before anything is built.
Enterprise web scraping: we run it, you get data
Enterprise web scraping is a staffing problem disguised as a coding problem. The first scraper is easy. Every additional source, the layout change on a Tuesday, the run that silently returned fewer rows — that is where in-house projects go to die. Managed web scraping moves all of it to a vendor whose only job is keeping collection running.
Large scale web scraping at Recordpipe runs on the same infrastructure that processes over one million public records nightly. Every source in your scope has its own collector and parser, monitored per run for row-count drift, empty fields, and late completion. When a check fails, a person looks at it before the data ships. When a source restructures, we repair the collector under the retainer.
The output is what makes this crawling as a service rather than a crawler: one schema across every source, entities resolved across variant names, duplicates removed, and a change delta per run so your systems ingest what is new instead of re-reading everything. The AI analysis layer filters, ranks, and flags on top, so the feed arrives shaped for the decision it supports.
Public records scraping that survives source changes
Public records scraping has a shelf life measured in weeks if nobody maintains it. Government offices redesign search pages, migrate vendors, change field labels, and move document archives without notice. A custom web scraping build that works on delivery day and breaks the following month has not delivered anything. What you are buying from a data scraping company is the maintenance, not the code.
That is the shape of the Recordpipe retainer. Each source has monitoring that compares every run to the last: rows collected, fields populated, time to complete. Drift triggers review. A break triggers repair, by us, before you notice. Your schema does not change because a source did — the parser absorbs the difference.
Cadence is set in scoping and held. Daily for datasets where a day matters — new filings, permit issuances, license actions. Weekly for slower-moving records. Each run delivers a full snapshot and a delta, with a manifest that states what was covered and what was validated. If you need to scrape government data reliably over the long run, the operating model is the product; the scraper is a detail.
Hire a web scraper, or hire the outcome: how the engagement works
Teams searching to hire a web scraper usually want a dataset, not a contractor. A contractor hands you code you now own and maintain. A web data extraction service hands you rows that keep arriving. Recordpipe sells the second, at a fixed price.
Start with the free intake form: the sources, the fields, the cadence, and where the data should land. We reply within 2 business days with feasibility and an approach, and we tell you at that stage if a target is out of bounds. The $500 scoping, credited in full against the contract, delivers a real sample from your actual sources and a fixed quote within 5 business days. Your engineers review rows in the format they will receive, not a proposal.
Contracts run from $5,000 to $3 million depending on the number of sources, the cadence, and the analysis layered on top. The build is fixed-price; ongoing collection, monitoring, and maintenance run under a flat retainer quoted alongside it. Delivery is configured to your stack — S3, GCS, SFTP, Snowflake, BigQuery, webhooks, or an API — and tested against it before launch.
What an engagement delivers
| Deliverable | Description |
|---|---|
| Source scope and eligibility review | Each requested source confirmed as publicly available and in bounds before the build starts; anything login-walled or private is declined in writing. |
| Collectors per source | Automated collection built and operated by Recordpipe for every source in scope, run on your cadence on our infrastructure. |
| Parsers and normalized schema | Source-specific parsing into one agreed field set, with types, allowed values, and a data dictionary. |
| Entity resolution and deduplication | Parties, parcels, cases, and licenses matched across sources and spelling variants, with a stable identifier on each. |
| Full snapshot per run | The complete current dataset delivered on every refresh. |
| Change delta | Rows added, updated, or no longer present at source since the prior run, with change type and timestamp. |
| Run manifest and validation report | Row counts against the prior run, field completeness, sources covered, and time to complete — evidence your ingestion can check. |
| Monitoring with human review | Automated checks on every run; a person reviews failures before data ships, and you are told when a source is delayed. |
| Source maintenance | Repairs when a source changes its layout or process, covered by the retainer, without a ticket from your side. |
| Delivery integration | Files, warehouse loads, webhooks, or API, configured to your stack and tested before launch. |
In the field.
Multi-county recorder feed at a real estate data company
A real estate data company can retire its in-house scraper fleet and subscribe to managed collection of recorded documents across the counties it covers. Nightly loads land in its warehouse in one schema, monitoring catches source changes, and the engineering team goes back to product work instead of parser repair.
Business registry monitoring at a fintech
A fintech's risk team can have state business registries scraped on a daily cadence, with a change delta flagging new formations, dissolutions, and agent changes across its merchant base. Records arrive by webhook into its case system; the team never runs a scraper or watches a registry site.
Permit issuance feed for a building-products sales team
A manufacturer's sales operations team can commission collection of building permits from municipal offices in its territories, filtered to the project types its reps sell into and ranked by valuation. The feed lands in the CRM each morning; the maintenance when an office changes its site is the vendor's problem.
Court docket collection for a litigation analytics product
A litigation analytics team can have civil dockets and judgments scraped from the public courts it covers and delivered as a daily delta to S3. Its models train on structured cases with provenance on every row, and coverage grows by adding courts to the retainer rather than hiring engineers.
Web scraping company vs data scraping service vs crawling as a service: pick the outcome
You are not buying scrapers. You are buying a current dataset with a fixed price and acceptance criteria. Contracts from $5,000 to $3 million; scoping is $500, credited, with a sample in 5 business days.