How a logistics company eliminated 3 full-time data-entry roles with one Make scenario
A regional freight brokerage was employing three full-time staff whose entire job was re-keying data: pulling shipment details from carrier emails and PDFs, and manually entering them into their TMS (transportation management system). It was slow, error-prone, and impossible to scale without hiring a fourth person.
The client
A 40-person freight brokerage handling roughly 200 shipments a week across a dozen carrier partners, each with their own document format — some structured emails, some scanned PDFs, a few still faxed and scanned.
The problem
Every shipment confirmation arrived in a different format, and someone had to read each one and manually type the details — carrier, rate, pickup and delivery dates, reference numbers — into the TMS. At 200 shipments a week, that was the full-time workload of three people, and typos in rate entry were a recurring source of billing disputes.
The solution
We built a single Make scenario that watches a dedicated inbox, uses OCR and a structured extraction step to pull shipment data out of whatever format arrives — email body, PDF attachment, or scanned image — normalizes it into a consistent schema, and writes it directly into the TMS via its API. Anything the extraction step flags as low-confidence routes to a human for a 30-second review instead of full manual entry.
The results
- Two of the three data-entry roles were redeployed to customer-facing work; the third left through natural attrition
- Billing disputes from rate-entry typos dropped by over 80%
- Shipment data now lands in the TMS within minutes of the carrier confirmation arriving, instead of hours later
"We thought we needed a fourth data-entry hire. Instead we needed to stop doing data entry at all."
If your team is still re-typing data that arrives in email or PDF form, there's almost always a faster way. Book a discovery call and we'll take a look at your documents.
