Industrial-scale data pipelines for the world's largest organizations.
Our enterprise solution is built for mission-critical data needs. We handle millions of requests daily with a managed infrastructure that guarantees 99.9% uptime. From resilient request handling to automated quality assurance, we provide the muscle for your data strategy.
Enterprise collection is less about any single scraper and more about what happens on the thousandth run. Sites change, hosts rate-limit, a parser that worked for six months returns an empty field, and nobody notices until a dashboard looks wrong. The work is building collection that reports its own health and fails loudly rather than quietly.
We run the infrastructure, monitor every run, and fix selectors when a source changes layout. You receive records on a schedule in the format your systems already read, with the counts that let you verify nothing went missing.
We deploy and maintain 24/7 dedicated scraper clusters for your project.
Rate-aware, fault-tolerant collection that respects target site limits.
Automated cleaning and validation to ensure zero-error delivery.
Dynamically scale from 10k to 10M requests as your business grows.
Enterprise Standard
99.9% Extraction Reliability
The schema is yours to change — add fields, drop fields, rename them to match what your systems already read. This is the default.
| Field | Type | What it is |
|---|---|---|
run_id | string | One collection run, so a delivery can be traced back. |
source | string | Which site or endpoint the records came from. |
records_expected | integer | What the source said it had, where it says. |
records_delivered | integer | What actually arrived after validation. |
records_rejected | integer | Failed validation. A spike here is the early warning. |
duration_seconds | integer | How long the run took. Trends up before a source blocks. |
status | enum | complete | partial | failed. Partial is the one that matters. |
started_at | timestamp | UTC, ISO 8601. |
JSON — the run manifest that ships beside each delivery.
Values here are illustrative. On a real sample these are your products, from your target sites — see below.
{"run_id": "run_2026_10_15_0400","source": "example-marketplace.com","records_expected": 48200,"records_delivered": 48174,"records_rejected": 26,"duration_seconds": 2140,"status": "complete","started_at": "2026-10-15T04:00:00Z",}Catalogue and price coverage across hundreds of competitor sites, refreshed daily.
Continuous collection of public filings, listings and market pages into one schema.
Rates and availability across property portals, where a missed run shows up as a pricing error.
A defined one-off project with a known scope and a delivery date.
Continuous collection, weekly or monthly. Selector fixes after a site changes are included.
A scraping engineer working inside your team, for roadmaps rather than deliverables.
All three are reasonable. Which one fits depends on how much of this you want to own.
At fifty sources you are no longer writing scrapers, you are running a platform: a scheduler, proxy rotation, per-source retry policy, a validation layer, and someone who notices at 02:00 when a site ships a redesign. The scrapers are the cheap part. The operations around them are the job.
Self-serve platforms work until a source needs logic they do not express - a multi-step flow, a login you are entitled to use, a field that only appears on some variants. Then you are writing the custom logic anyway, inside someone else’s runtime, with their concurrency limits.
We run the infrastructure and the on-call. You get records on a schedule with a manifest saying how many were expected, how many arrived and how many failed validation - so you can verify a delivery without reading our logs.
Collection breaks. The question worth asking a supplier is not whether it breaks but whether you find out from them or from your own data.
Half the catalogue silently missing, and a dashboard that looks plausible because the rows that did arrive are correct.
The run manifest carries expected and delivered counts. A partial run is marked partial and flagged before the delivery lands, rather than discovered a week later.
Records keep arriving and keep validating, but one field now means something else.
Validation is per-field and typed, so a column that changes shape fails rather than passing through. We fix the mapping; the delivery waits.
Runs overrun the window, deliveries slip, and the schedule you planned around stops holding.
Capacity scales per source rather than globally, so one source growing does not push the others out of their window.
The same six steps on every engagement, whether it is one site or ten thousand.
Hand-shake connection has been established between potential customer and Business Team.
Potential customer exchange information and Business Team consult them with expert advice.
Compliance & Technical specialist examines the identified business need of potential buyer.
Potential Buyer confirms the compliance details to allow Engineering team to start Execution.
Delivery team dispose off the Exemplary Service to ensure the customer delight.
Build a strong long term relationship with a client by exceeding their expectations at every stage.
Three ways: a fixed price for a defined project, a weekly or monthly retainer for ongoing collection, or a dedicated scraping developer working as part of your team. Which one fits depends on how often the data changes and how much of the work is ongoing rather than one-off.
Most projects start within a few days of the requirements being agreed. A single straightforward site is usually running within a week. Large multi-site pipelines take longer, and we will give you a schedule with the quote rather than a vague estimate.
We build collection to be well-behaved: sensible request rates, proper identification and respect for published crawl rules. If a site does not want to be collected from, that is a signal we take seriously rather than a problem to engineer around. Where a site is simply slow or rate-limited, we schedule around it.
Yes, routinely, and we are happy to work under your paperwork rather than ours. We can also sign a data processing agreement where your engagement involves us handling data on your behalf.
You do. The datasets we produce for you are yours. We do not resell them and we do not reuse them for other clients.
Send a target URL and the fields you want. We return 100 real extracted rows within 24 hours so you can check the structure and the quality yourself, before any money changes hands.