Carrier data pipelines
Scheduled Python jobs that collect events from carrier APIs, parse emailed updates and reconcile them into one clean shipment record.
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An operations dashboard, Python data pipelines and a customer tracking portal that replaced a freight team's spreadsheets.
Carvell · Operations
Live1,284
Active
96%
On time
3
Flagged
( The challenge )
Carvell moves freight for manufacturers across the Gulf and South Asia. Its operations ran on twelve shared spreadsheets, updated by hand from carrier portals, emails and phone calls. Two coordinators spent most of Monday building the weekly report.
Customers had no way to check a shipment themselves, so they emailed. Every status question meant opening a spreadsheet, then a carrier site, then writing a reply. Nobody had a reliable picture of what was late until a customer complained.
( Our approach )
We started with the data, not the screens. Python pipelines now pull shipment events from carrier APIs and parse the carriers that only send emails, normalising everything into one PostgreSQL database every fifteen minutes.
On top of that sits an operations dashboard for the team and a tracking portal for customers, both reading the same records. Exceptions surface automatically, and the weekly report builds itself and lands in inboxes before anyone starts work on Monday.
( What we built )
Delivered in 6 weeks, with the source code, accounts and documentation handed over in full.
Scheduled Python jobs that collect events from carrier APIs, parse emailed updates and reconcile them into one clean shipment record.
Every active shipment, its status, its margin and its next milestone, filterable by lane, customer and carrier.
Shipments that miss a milestone or stall at customs are flagged the moment the data shows it, not when the customer calls.
Customers log in and see their own shipments, documents and estimated arrivals, which answers most questions before they are asked.
Weekly and monthly performance reports generated from the live data and delivered by email, with no manual assembly.
CI and CD, error tracking, database backups and alerts on every pipeline, so a failed job is noticed and fixed quickly.
( The outcome )
( How it was delivered )
We traced where every spreadsheet column came from and agreed one data model the whole business could share.
Carrier integrations and email parsing, running against real shipments in parallel with the old spreadsheets to prove the numbers matched.
The operations view and the customer portal, designed with the coordinators who would use them every day.
The spreadsheets were archived, customers were invited to the portal and the automated reports went live.
( Services used )
( Built with )
Mainstream tools with deep talent pools, so the client is never dependent on us to maintain what we built.
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