Ever lost data somewhere between SFTP and a message queue? ShipmentInsights did—and it nearly sank their biggest customer.
This mini-case study is about a vertical search company that processed shipping data into insights. Their architecture was clever: RAG over an inverted tree, as well as a vector db. Their data preboarding? Not so much.
One day, Couture Du Sol—a major fashion house—called support. Their shipping costs had spiked, deadlines were slipping, and the culprit was ShipmentInsights’s data. It was wrong. Very wrong.
Cue three weeks of panic across three continents. Over 500 feeds. Manual workflows. Inconsistent metadata. And a message queue that might as well have been parked over the Bermuda Triangle.
Eventually, they found the issue: four non-contiguous days of production data had gone missing… years earlier. The ripple effects had just reached Couture Du Sol.
The root cause? Faulty preboarding:
• No durable file identity
• Mutable data with poor versioning
• No manifesting or lineage tracking
• Manual handoffs across geos
The fix? A better preboarding architecture.
An automated ingestion process that treats raw data like a product, not a mystery.
CsvPath Framework is what good looks like. It’s open source, scalable, and built to prevent disasters like this one.
For the full story, read here about how one shipping information provider lost their data in the deep.
Stop shipping data through the Bermuda Triangle. Start with CsvPath Framework.
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