Data Collection & Web Scraping
Data collection and pipelines built responsibly — rate-limited, self-validating, and turned into information your team can act on rather than a pile of raw rows.
Data collection engineered to keep working
Reliable data collection is an operational data-engineering problem, not just parsing HTML. Pages change without notice, collectors fail silently, and the value is in what happens after collection: validation, deduplication, change detection and scheduled pipelines that alert when something stops arriving.
We build collection that identifies itself, respects robots.txt and rate limits, collects incrementally rather than re-downloading everything, and validates its own output — then delivers into whatever you actually use.
How an engagement runs
- 1
Scope and lawful basis: What data, from where, how often — and the terms of use, robots.txt and privacy constraints that apply.
- 2
Source assessment: Stability, structure, rate limits and change frequency of each target.
- 3
Pipeline build: Collectors with retries, rate limiting, structured parsing and schema validation.
- 4
Validation at the boundary: Volume, null-rate and range checks — a silent collection failure is treated as an incident.
- 5
Monitoring and upkeep: Alerts on source changes and anomalies, with fixes as the sources evolve.
- 6
Delivery: Clean datasets to your warehouse, API or files on schedule, with documentation.
Discuss Your Data Requirements
Reliable data collection is an engineering problem: validation, monitoring and change detection, not just parsing HTML.Scope the Pipeline
Tell us which decisions the data should support, which sources matter and how fresh it must be — we will come back with an approach and its constraints.
How we approach data collection
Before any code, four questions decide whether a project is straightforward, needs legal review, or should not be built:
- Is the data personal? If so, it needs a documented lawful basis and sign-off before we start. “It was public” is not a lawful basis.
- What do the terms of service say? Collecting from behind a login you agreed terms for is a materially different risk from public pages.
- Is the content itself protected? Facts generally are not; articles, reviews and photographs generally are.
- What is the impact on the source? Considerate collection is both the ethical choice and the most effective way to avoid being blocked.
What we deliver
- Collectors that identify themselves, honour robots.txt, and rate-limit conservatively with backoff on errors.
- Incremental collection — fetching what changed rather than re-downloading everything daily.
- Validation that alerts on silence. Scrapers fail quietly: markup changes, the parser returns empty, and the pipeline keeps running while collecting nothing. Volume and null-rate alerting catches that within hours.
- Cleaning, deduplication and entity resolution, with a review queue for uncertain matches rather than confidently wrong ones.
- Delivery into whatever you actually use — warehouse, database, sheet or API.
Common questions
Is this legal?
It depends on jurisdiction, the data, and the method. We will tell you which category your project falls into and when you need advice from a qualified lawyer. We do not build systems that defeat authentication or access controls.
How much maintenance does a scraper need?
Ongoing. Sites change without notice. Treat it as a running system with monitoring, not a one-off build.
Could we use an official API instead?
If one exists, yes — it is more stable, cheaper to maintain and unambiguously permitted. We check for one before proposing collection.
Collected data usually feeds something downstream — often the classification and matching work covered under AI Development. For the legal side, see our guide to lawful and ethical web scraping.