A logistics analytics company
We replaced brittle manual data wrangling with reliable streaming pipelines and a well-modeled lakehouse — so analytics and ML start from clean, traceable data.
The problem
Reporting ran on stale, hand-assembled exports that nobody fully trusted; data broke silently, and every new question meant another week of cleanup before anyone could answer it.
Our approach
- 01
We cataloged every source and modelled a layered warehouse — raw, staging, curated — so data became consistent and analytics-ready.
- 02
We built batch and streaming ingestion with schema enforcement, tests, and retries so data arrives complete and on time.
- 03
We added lineage, monitoring, and alerting so freshness and quality issues surface early — not in a board meeting.
The outcome
Analytics and ML the team can trust, because the data behind them is tested and traceable — and pipelines that run themselves instead of breaking quietly.
Impact (figures pending confirmation)
- Data freshness
- 24h → 5 min [confirm]
- Pipeline reliability
- 99.9% [confirm]
- Manual data wrangling
- −90% [confirm]
Build something like this
One senior team, end to end. Tell us what you're building and we'll architect the path to ship it.