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CALIBRATING OPTICS_
CASE STUDYNORTHLINE LOGISTICS *DATA AUTOMATION

Manual data ops, cut by 80% in one quarter.

How a 40-person analyst team stopped copying carrier data by hand — and started trusting the numbers again.

−80%
Manual data operations
12M
Records ingested daily
99.98%
Pipeline uptime since launch
14 wks
Discovery to production
PROBLEM

Forty analysts spent most of every day copying rates, schedules, and capacity data from 300+ carrier portals into spreadsheets. Errors compounded downstream; pricing decisions ran on week-old numbers. Two prior automation attempts had collapsed under portal changes and IP blocks.

[ image zone — pipeline architecture diagram ]
APPROACH

We built a distributed scraping fleet with per-portal adapters, automatic schema-drift detection, and rotating residential egress. Extracted data flows through a cleaning and enrichment layer into their warehouse, with anomaly alerts before bad data reaches a dashboard.

Analysts moved from data entry to data review: a triage UI surfaces only the records the pipeline isn't confident about.

OUTCOME

Manual data operations dropped 80% within the first quarter. Pricing now runs on same-day numbers, and the pipeline has held 99.98% uptime through hundreds of portal changes — each absorbed by adapters, not analysts.

"They rebuilt in four months what our previous vendor couldn't stabilize in two years."

M. Keller * — VP Engineering, Northline