Resilient collection
Browser and HTTP strategies are selected per source, with rate limits and change detection.
Data systems / Collection
Clean data from difficult sources.
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Web scraping
Maintainable collection pipelines with validation, freshness guarantees, and a delivery format your product can use.
What we build
Useful from
day one.
PlaywrightPythonScrapyPostgres
Structured output
Validation and deduplication happen before data reaches an API, table, or export.
Operational visibility
Freshness, block rates, and schema drift are monitored instead of discovered by users.
The route to production
Clear steps.
Visible progress.
Discover
We map the real workflow, risks, users, and success measure before choosing an implementation.
Build
Working software lands in visible increments, with the hardest assumptions tested first.
Prove
We validate against production-shaped data, failure paths, performance, and real use.
Ship
Deployment, monitoring, documentation, and a clean handover are part of the build.
Verified client reviews
In their words.
“Very knowledgeable. Deployed my project successfully and was kind and patient throughout.”
“Rapid and great work, with high technical knowledge. Highly recommend.”
Before you ask
Questions,
answered.
How do you tell whether a scrape is feasible?
Most of the cost and risk is decided by the target site, not by the data you want off it. We check whether the data is genuinely public, how hard the site fights back with challenge pages, JavaScript rendering, CAPTCHAs or IP blocks, whether it sits behind a login, what the terms of service and robots.txt say, what volume and freshness you truly need, and how likely the layout is to change. That checklist runs before we quote, because it is the difference between a two-hour job and a permanent engineering commitment.
Is web scraping legal?
This is not legal advice. The honest framing is that scraping public, non-personal data a site makes freely visible sits on far safer ground than collecting data behind a login, harvesting personal information, or ignoring a site's stated terms. Terms of service, robots.txt, copyright and data-protection law such as GDPR and CCPA are all real constraints and they vary by jurisdiction and use case. For anything touching personal or authenticated data we tell clients to get their own legal sign-off before the build.
Should we check for an API first?
Always, and first. A surprising number of scraping projects exist only because nobody looked for an official API or a data-licensing option, which is often cheaper than building the scraper and far cheaper than maintaining it. The cheapest scrape is the one you never have to build, and we would rather say that in the first conversation than bill you to discover it.
Why does a scraper need ongoing maintenance?
Because it is not a finished product; it is tightly coupled to HTML that changes without warning. Sites redesign, add anti-bot defences and tighten rate limits, and any of those can silently break extraction. A live feed needs monitoring that shouts when data stops flowing, retry and proxy logic, and someone to fix the extraction when the layout drifts. A flat one-time price with no plan for upkeep is a scraper that works until the target's next release.
How do you know the data is still correct?
By defining success on content rather than HTTP status. Anti-bot systems rarely return a clean 403 any more — you get a 200 with an interstitial, the real layout with prices stripped out, a geo variant priced in another currency, or an A/B bucket where the markup you built against no longer exists. We require fields to be present and inside plausible ranges, compare body size against a rolling median for that domain, and run a canary set of twenty to fifty known-value URLs per domain outside the customer job flow so drift is caught before another batch is written.
What does the output actually look like?
A typed, versioned schema, because you are buying fields rather than pages. Prices are integers in minor units with a currency code, not the string a merchandiser typed; availability is a fixed enum; nullability is declared or it is a bug. Validation runs before a row is written, failures go to a quarantine table with the compressed HTML snapshot that produced them, and a row where nine of twelve fields extracted is marked partial with the failed fields named, rather than returning nulls that a pricing model will read as zero.
Can we call it as an API?
Yes, and the architecture differs from a script on a schedule. Never make the caller wait on the target site: a job endpoint validates and returns immediately with an ID, the work goes onto a per-domain queue, and the caller polls or receives a webhook. Idempotency keys stop a caller retrying your own error from paying for a second fetch — one retry-happy client burned about $2,800 of residential proxy credit in an afternoon before that existed. And jobs need a real state machine rather than a boolean, so a blocked job lets the caller choose between escalating the fetch and serving this morning's cached row.
How do you keep the running cost down?
By making freshness a parameter instead of a promise. A plain request through a datacenter proxy costs a small fraction of a cent; the same page through a headless browser on a residential IP costs thirty to sixty times that once bandwidth, CPU and retries are counted. Callers state a maximum age, cached rows serve anyone whose tolerance allows it, and the fetch is tiered per domain — a plain request first, a real browser only when a content check fails. On one commerce client that change took monthly infrastructure from about $4,100 to $700 and raised the success rate at the same time.
What if the data needs cleaning before it is useful?
Then the scrape is the cheap part and enrichment is the product. For lead generation in particular, verification, deduplication and identity resolution decide the real yield: unverified addresses burn a sending domain, and duplicate records inflate a list that converts no better. Validation and deduplication happen inside the pipeline, before anything reaches an API, a table or an export.
When is the answer that we should not build it?
When the data sits behind a login or a paywall, when it is personal data at scale, when the terms explicitly forbid it, when the defences would have to be actively defeated, or when there was an official API all along. Those are stop-and-reassess projects, and the right move is legal sign-off, the API, or a licensing deal. We would rather tell you a project is a red light up front than take the work.
From the journal
Further reading.
A web scraping service API is not a cron job with a URL in front of it5 min readIs web scraping feasible? A checklist before you build a scraper8 min readHow to hire a web scraping company that keeps the data flowing8 min readWeb scraping for sales leads that your reps won't throw away7 min readHow much does a web scraping service cost?7 min read
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