We build the extraction systems other teams quote as undoable: the most heavily defended sources in the industry, at millions of records a day, with coverage you can prove rather than hope for. This is the discipline we are number one in.
Twelve years, 1 000+ extraction systems and five billion delivered records. Have a task or a problem you need solved? Send it over, whatever its size — a great many of our clients arrived with something small and stayed for years. You get an answer within 24 hours.
extraction systems built and shipped
records collected and delivered
records a day on a single pipeline
years working only on hard sources
We run production extraction against the most defended public platforms in their industries — the ones that hold an entire market's inventory and spend real money on keeping robots out.
Event inventory, price tiers and availability on a platform with queue systems, aggressive behavioural scoring and per-event access rules.
Queue · behavioural · rate
Listings, price history and status changes across a national portal with edge protection and result caps that hide most of the inventory from a naive crawl.
Edge WAF · result caps
Live seat-level pricing that moves by the minute, where the value of the data is entirely in how fresh it is.
Real time · high churn
Catalogue, offers, buy-box and stock at catalogue scale, with per-region and per-session variation in what is even shown.
Scale · personalisation
Rates and availability that only exist as a function of a search — a combinatorial space, not a page you can list.
Query space · freshness
European portals with strict bot management, regional splits and short-lived listings that vanish before a weekly crawl sees them.
Bot management · churn
Store-level pricing and stock, where the answer differs per location and the interesting question is always local.
Geo matrix · volume
The one another vendor returned as impossible. Two weeks, fixed fee, working proof.
Bring it to us
Named platforms are examples of the defence classes we run against in production. We collect publicly accessible data, we do not break authentication, and we shape load so the source is never degraded — the same rules on every engagement, written into the contract.
A modern protected site is not one wall — it is six independent systems, each of which fails a scraper differently. Most projects die because one of them was never engineered for. Here is all six, and what we do about each.
Cloudflare, Akamai and Imperva score a request before the origin ever sees it — on network reputation, header order, protocol quirks and a hundred signals you don't get to read.
What we doCoherent clients rather than patched ones: transport, TLS and HTTP behaviour that is internally consistent, over routing chosen per source and per region.
DataDome, HUMAN and Kasada don't judge a request, they judge a session: timing, navigation order, what a real user would have loaded on the way here.
What we doSession strategies modelled on real journeys, with pacing, warm-up and abandonment tuned per source instead of one global delay.
TLS handshakes, HTTP/2 frame settings, canvas and font enumeration, sensor entropy. A headless browser with a spoofed user-agent is identified instantly and quietly.
What we doFingerprint control end to end, so what the network layer claims and what the runtime does are the same story — and a drift monitor that tells us when the story stops working.
JavaScript proof-of-work, invisible challenges, queue systems that hold a session for twenty minutes and expire it if it looks synthetic.
What we doA challenge pipeline that resolves in-session and keeps state through the wait, so throughput survives a queue instead of collapsing at it.
Machine-generated class names that rotate on deploy, values assembled client-side, decoy nodes, content split across lazy fragments.
What we doWe work from the interfaces underneath the page wherever they exist, so a cosmetic redesign is a non-event rather than an outage.
200 results per query over a catalogue of millions, pagination that stops at page 50, indexes that are deliberately partial. A naive crawl reports success and silently misses a third of the market.
What we doQuery-space decomposition — the catalogue is partitioned until every partition fits under the cap, then reconciled against control totals.
Getting through a defence once is a demo. The engineering is in holding that throughput every day for years, at a cost per million rows that still makes the data worth having.
Operating envelope
Full-site collection — the complete catalogue, not a sample — plus a delta pass fast enough that the picture is never more than a scheduled window old. Volume is a design input from the first day, not something discovered in month four when the pipeline stops keeping up.
Where the volume goes
Illustrative profile of one production pipeline. Your envelope is fixed in the design phase and written into the SLA.
Every parser we ship runs on the same contour. Blocks change per source; the property that does not change is that a failure anywhere is detected, isolated and replayed rather than silently delivered as missing rows.
The difference between a scraping vendor and an extraction engineering team shows up months after launch — in whether the data is still arriving, and whether anyone can demonstrate that it is complete.
Every run is reconciled against control totals derived independently of the crawl — category counts, sitemap cardinality, known-item probes. Coverage ships as a number on the same dashboard we watch, so “did we get everything” is never a matter of opinion.
We routinely take a source in full: hundreds of millions of items, every category, every region, every language variant. The partitioning strategy that makes that possible under result caps is the part that takes experience — it is why 1 000+ built systems matters more than any single clever trick.
On a defended source at scale, egress, compute and challenge handling are the recurring line item that decides whether the data is worth collecting. We design the execution model per source — interface-first where possible, rendered only where required — and report cost per million rows from day one.
Tests, runbooks, infrastructure as code and a documented access strategy. Shipping is the start of it, not the end: hosting, monitoring, on-call, upstream changes, tuning and new features all sit inside one monthly arrangement, handled by the same engineers who designed the thing. A system we will still be running in year eight has to be engineered to be operated, not merely delivered — and most of our clients are on their third or fourth system with us.
A parser is a product with an uptime, not a script that was delivered once. These are the commitments in the support agreement; tiers and windows are set per project against how critical the feed is.
| Commitment | Target | What it means in practice |
|---|---|---|
Coverage |
99 %+ | Measured against independent control totals every run, not asserted. When coverage drops, you see it on the same dashboard we do, before it reaches a report. |
Anomaly monitoring |
24 / 7 | Volume shifts, block-rate movement, schema drift, latency spikes and silent field loss are detected by us and raised to you — not discovered by your analyst. |
Data quality checks |
Every run | Completeness, duplicates, field types, ranges and cross-field consistency verified automatically; failing batches are quarantined rather than published. |
Engineering response |
From 4 h | Incident tiers with named windows. A source changing its defences or its markup is routine work covered by the retainer, not a change request. |
Recovery |
Defined RTO | Fallback routes, checkpoints and replay queues designed in advance, so an upstream change costs a window of latency — not a week of missing history. |
Availability |
99.9 % | Measured on delivered runs against schedule and reported monthly. You get the same dashboard we watch. |
Each of these came to us after another team returned it as impossible or handed over a pipeline that had stopped working. Clients are under NDA — the engineering detail we walk through against your own case.
Minute-level prices and availability for every event in nine countries, feeding a secondary-market pricing engine.
What was hardWaiting-room queues, behavioural session scoring, per-event access rules, and data whose value expires in under two minutes.
Complete daily inventory with price and status history across a country-scale listings portal behind managed bot protection.
What was hardA 1 000-result ceiling per query over 11M active listings, regional result variation, and a previous vendor's pipeline that had been silently missing 38% of the market for a year.
Full catalogue, offers and stock for a global marketplace, delivered into the client's warehouse for a repricing engine.
What was hardCatalogue scale, per-region and per-session personalisation of what is even displayed, and a hard cost ceiling per million rows to keep the product viable.
On a defended source, a quote written from a brief is a guess — the spread between the easy and the hard version of the same site is roughly tenfold. So we prove it on the real target first, for a fixed fee.
1Brief
Which source, which fields, what volume, how fresh, and what the data is used for. Half the time the scope narrows and gets cheaper right here.
OutputTarget map & field spec
2Feasibility
Two weeks against your actual target: access strategy, sample volume, real block rate, real cost per million rows — and the honest risks.
OutputRunning PoC + risk list
3Design
Data model, delivery interface, throughput, freshness, coverage target and running cost — written into the contract, not into a slide.
OutputFixed scope & fixed price
4Build
Every week ships something that runs in your environment. Monitoring, runbooks and documentation are built alongside it, not bolted on at the end.
OutputA system running in production
5Operate
On-call, handling defence and markup changes on the source, tuning and extensions. Routine changes sit in the retainer; large ones are quoted first.
OutputA feed you stop thinking about
Extraction prices by resistance, not by field count. So we price in two steps, and we say no early when the work is not ours.
Model
You start on whichever rung fits, and most clients start with one small first task. From the Operate rung onward it is a single monthly arrangement covering hosting, monitoring, on-call and continued development, so nobody on your side has to build an operations team around it. Infrastructure runs on ours or inside your own perimeter — whichever your security and finance people prefer.
Not our work
If your source is real but not our kind of work, we will say so in the first reply and point you at someone better suited. That costs us nothing and saves you a month.
Legal & governance
Projects are scoped around publicly accessible data, or access the client is authorised to hold. We document data purpose, minimise sensitive fields, and design access, retention and deletion controls into the pipeline rather than bolting them on.
GDPR and CCPA alignment is implemented through technical and organisational controls. Confirming the lawful basis and permitted use for a specific dataset and jurisdiction remains with the client’s legal team — we give them the documentation to do it.
If yours isn’t here, put it in the form — we answer in writing, within 24 hours, and without a discovery call first. Most questions get a straight yes or no rather than a proposal.
Bring us the hard source
Send the source, the fields and the volume you need. You get a straight answer: whether it is solvable, what specifically makes it hard, and roughly what it costs to run. No deck, no discovery call before there is anything to discover.