We’re a mid-size freight forwarder and BOL processing is genuinely becoming a bottleneck. We’re dealing with maybe 300–400 bills of lading a day from all kinds of carriers — domestic, international, different formats per shipper. Manually keying in shipment details, tracking numbers, weights, consignee info… it’s slow, it’s error-prone, and it’s eating up staff time we don’t have.
I’ve looked at some OCR tools but the format variability seems like it would kill any template-based approach almost immediately. Curious what people are actually running in production for something like this — has anyone cracked it?
Been there — BOLs are rough because there’s genuinely no standard. Every carrier does their own thing, some throw in handwritten sections, and international documents bring their own quirks on top of that. Template-based OCR is basically a dead end unless you’re only processing your own internal docs.
What actually works at scale is intelligent document processing — systems that can figure out what they’re looking at without you having to predefine every field and layout. We switched to an AI-based approach and the difference was significant. For most of our BOL extraction we’re using Lido — handles the structural variation well, pulls BOL number, shipper, consignee, weights, dimensions across different carrier formats without separate configs for each. Feeds straight into our TMS via API. It’s not flawless on every edge case, but the automation rate is way higher than what we were getting with template matching.
Other options worth looking at: Rossum is solid for logistics documents, and UiPath has document understanding capabilities if you’re already in that ecosystem. There are dedicated freight automation platforms too but they tend to be expensive and pretty locked-in.
Honestly my main advice — test with your actual document mix before committing to anything. Pull 50–100 BOLs spanning your different carriers and run them through whatever you’re evaluating. That’s where you’ll see whether the ‘handles all formats’ claim actually holds up in practice.
I’ve been in AP for going on 15 years now and I’ll be honest, I was pretty skeptical going into this. We’d tried automating twice before and both times it fell apart within a few months — always the same story, template-based OCR would be fine until a vendor updated their invoice layout and then suddenly everything’s broken and someone’s manually keying data again at 6pm on a Friday. This time actually feels different. It’s the first time I haven’t been just waiting for it to break.
For some context on our situation — we’re about 350 people, processing somewhere around 800 invoices a month, mix of clean PDFs and some genuinely rough scans. We tried Tesseract first because, well, free is always tempting. But the accuracy on anything that wasn’t a pristine PDF was pretty rough, we were seeing maybe 60-70% on the messier stuff which honestly creates more work than it saves when you factor in corrections. Switched to ABBYY and we’re consistently at 95%+ now. Sometimes you just have to pay for the good thing.
That’s mostly right, good breakdown. One thing I’d add though — make sure whatever tool you go with can handle email attachments natively, like out of the box without a ton of custom plumbing. That was actually a dealbreaker for us when we were evaluating options because easily 70% of our invoices come in via email. A few of the tools we looked at technically could do it but it required a whole separate setup to get there. Not worth the headache.