Data aggregation & content platforms
From scattered sources to one clean feed — collected, normalised, and served.
/ The problem
Valuable data is usually spread across sources that were never meant to be read by machines: portals, PDFs, spreadsheets emailed weekly, APIs with three different date formats. Aggregating it by hand doesn’t scale; aggregating it naively produces a swamp of duplicates and silent gaps.
The difference between a data product and a scraping script is everything around the fetch: change detection, deduplication, schema normalisation, and monitoring that notices when a source quietly changes shape.
/ How we deliver
We build ingestion pipelines source by source — scrapers, API pollers, file processors — feeding a normalisation layer that enforces one canonical schema, with lineage kept so every record traces to its origin. Freshness and volume monitors alert on anomalies before your users notice them.
This is the engineering behind our own data aggregation platform, so the patterns are proven in production: the same stack can power your news feed, price index, listings service, or market-data product, delivered by API, export, or a full white-label frontend.
/ What we build with it
Market & price indexes
Prices scattered across portals and PDFs turned into one queryable, dated series.
News & content feeds
Multi-source editorial content deduplicated, categorised, and served fresh, ready to republish.
Listings aggregation
Jobs, property, events, or tenders collected from many publishers into one clean catalogue.
Monitoring & watchlists
Track specific entities or topics across sources and alert the moment something changes.
/ Capabilities
- 06.1
Source scraping, API polling, and file ingestion
- 06.2
Deduplication and canonical schema normalisation
- 06.3
Change detection and source-drift monitoring
- 06.4
Record-level lineage and audit trails
- 06.5
Delivery via REST APIs, exports, and webhooks
- 06.6
White-label content frontends
/ Spec sheet
The short version
How this service runs, in title-block form. Ask us for the long version.
- Ingestion
- Scrapers, API pollers, file processors
- Schema
- One canonical model, record-level lineage
- Freshness
- Near-real-time to daily — monitored
- Drift
- Anomalies quarantined, never mixed in
- Delivery
- API, exports, webhooks, white-label site
/ How delivery runs
- 1
Source audit
Each source is assessed for terms, stability, and structure — and we tell you plainly which ones shouldn’t be used.
- 2
Pipeline build
Ingestion is built source by source, each with its own change detection and retry behaviour.
- 3
Normalise & monitor
Records map into the canonical schema with lineage, and freshness/drift monitors switch on.
- 4
Serve & iterate
Feeds go live by API or frontend, and new sources join the pipeline without touching the old ones.
/ Common questions
Is scraping legal?
We assess each source’s terms and robots policy before building against it, prefer official APIs and licensed feeds where they exist, and will tell you plainly when a source shouldn’t be used.
How fresh is the data?
Per-source, per-agreement — from near-real-time polling to daily batch. Freshness targets are monitored and alerting is part of the standard build.
What happens when a source changes its format?
Drift monitors catch schema and volume anomalies automatically; the affected pipeline quarantines suspect records instead of polluting the canonical store while we ship the fix.
/ Related services
/ Next step
Talk to us about data aggregation & content platforms
Describe where you are — greenfield, half-built, or on fire. We answer with a technical read within one working day.
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