Trucking runs on paper. Every load generates a stack of documents that someone has to process: driver tickets, bills of lading, carrier invoices, fuel receipts, lumper fees. Most of that paper is handwritten, photographed in bad lighting, or scanned through machines that haven't been updated since the Clinton administration.
This is the reality trucking companies deal with every day. And it's why so many of them end up with entire teams doing nothing but data entry.
Lido handles the document types that break other extraction tools in trucking: handwritten driver tickets, photographed bills of lading, carrier invoices from hundreds of different formats, and fuel receipts captured in bad lighting. Disney Trucking replaced six full-time data entry employees by running their field documents through Lido’s AI extraction instead of manual processing.
One trucking company we work with had six full-time employees processing driver tickets. As their operations lead put it:
"This is all they're doing."
Six people, full-time, on data entry. Their weekly workflow looked like this:
Monday opening and sorting, Monday through Tuesday scanning, Wednesday through Thursday manual data entry into their accounting system, Friday printing and pairing documents. A four-day process that repeated every single week.
The documents themselves weren't complicated — driver names, dates, load information, amounts. But the tickets were handwritten. Their accounting software was 15 years old with no API. And nobody trusted automation tools to read the messy inputs accurately.
So they staffed for it.
This pattern shows up across the industry. A logistics team at a manufacturing company described their invoice processing as "stopping the bleeding." They process thousands of carrier invoices and BOLs monthly, and their analyst — hired to do actual analytics work — spends most of their time pulling up PDFs to troubleshoot variances.
"If we were to do it effectively, it would be like two full-time jobs of just data entry reading PDFs."
Another trucking company deals with invoices that have order numbers scattered across multiple pages in different formats — sometimes labeled "reference number," sometimes "bill of lading," sometimes handwritten on page three. Their previous extraction tool couldn't figure out which field was which, let alone tie it back to their inventory system.
Most document extraction tools are built for clean, digital documents. Trucking documents are neither.
Handwritten tickets. Drivers fill out paperwork in truck cabs, on loading docks, in parking lots. The handwriting is rushed, abbreviated, and often illegible to anyone except the person who wrote it.
Photographed and scanned inputs. Documents get photographed on phones, faxed between carriers, scanned at terminals, and forwarded through email chains. By the time they reach your AP team, they've been compressed, rotated, and degraded multiple times.
Multiple document types bundled together. A single PDF might contain an invoice, supporting documentation like washout tickets, and backup paperwork. Extraction tools need to know what to pull and what to ignore — most can't tell the difference.
Format variance across carriers. Every carrier has their own invoice layout. Every terminal has their own ticket format. Template-based tools require configuration for each one, which means constant maintenance as you add carriers or they update their systems.
Legacy system integration. Many trucking companies run accounting software that's been in place for decades. No API, no modern import options, just manual data entry or CSV uploads. Automation tools that require direct system integration don't help.
The result is that trucking companies either staff up with data entry teams or limp along with partial automation that still requires heavy manual review.
The requirements are specific:
Handwriting data extraction tooling that works. Not "works on neat handwriting in controlled conditions" — works on the rushed scrawl that drivers produce in the field. One of our sales engineers puts it this way: "Lido has extracted things that I cannot read."
No templates per carrier. Adding a new carrier shouldn't require building a new template. The tool should handle format variance automatically, whether it's seen that carrier's invoices before or not.
Multi-document handling. When an invoice comes with supporting documentation, the tool needs to know what to extract and what to skip. When multiple invoices are bundled in one PDF, it needs to split them correctly.
Flexible output. Some companies need CSV exports for legacy system imports. Others need JSON paired with the source PDF for downstream inventory systems. The output format needs to match whatever the existing workflow requires.
Fast iteration. Extraction won't be perfect on the first try for every document. The tool should allow refinement without charging for each attempt or requiring vendor support tickets.
Lido uses a custom blend of AI vision models, OCR, and LLMs to extract data from any document — including handwritten driver tickets, photographed BOLs, and carrier invoices in any format. No templates, no model training, no configuration per carrier.
The typical workflow:
Trucking companies choose Lido over other tools because it:
One trucking company freed six employees from manual data entry and compressed their four-day workflow into same-day processing. A logistics team eliminated the PDF review that was consuming their analyst's time. Another company finally solved the order number matching problem that their previous tool couldn't handle.
The documents don't change. The staffing requirements do.
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