The context
The enterprise needed timely, structured data from many public online sources, but manual collection and static reports slowed every decision. It needed an automated way to extract, normalise and deliver real-time insight at scale — across sectors and teams.
What was holding them back
Time-consuming collection from unstructured online sources.
Formats varied across regions and websites.
Pricing and regulatory changes were spotted too late.
Missing trend data for forecasting and analysis.
Legacy tools failed at large data volumes.
What they set out to achieve
- Extract and structure data from diverse public sources
- Monitor changes in real time
- Configurable alerts and trend dashboards
- Serve multiple departments from one platform
- Enterprise-grade security, compliance and scale
What we built
Automated crawlers
Python-based collectors that handle dynamic sites at scale.
Data normalisation
Cleaned, standardised data ready for analysis.
Change detection
Watches target pages and flags key updates.
Custom alerts
Notifications by email, API or webhook for critical events.
Integration-ready
REST APIs and ETL pipelines into ERP, CRM and BI tools.
Scalable architecture
Containerised services on Kubernetes that scale on demand.
The results
- Historical data capture enabled robust forecasting and trend analysis
- Structured, up-to-date intelligence improved cross-team decisions