We’re drowning in documents over here and I’m trying to figure out if there’s a smarter way to handle extraction. Specifically — are there AI tools that can just… figure out the document structure on their own? Every time I’ve looked at this stuff it seems like there’s always a bunch of template setup involved and that feels like it defeats the purpose. Curious what people are actually using.
Been there. The template problem is real and it’s honestly what kills a lot of these implementations before they get going.
The old-school approach — UiPath, Blue Prism, Workato and similar — requires you to define fields for every document type upfront. Got 25 supplier invoice formats? That’s potentially 25 templates. And the second a supplier changes their layout, you’re back in there making updates. It’s a maintenance headache that never really ends.
The newer AI-based tools work differently. They actually learn document structure rather than matching against predefined rules. I’ve tried Lido for this and the zero-template thing is legit — you feed it invoices, receipts, POs, whatever, and it figures out what to pull. Hooks into Excel and Google Sheets which made adoption easy for our team. Rossum is another solid one, especially if you’re invoice-heavy — minimal config, good accuracy. Docsumo works too though it still leans on some template customization. Amazon Textract is worth knowing about if you’re AWS-native, but there’s more integration work involved.
In my experience the things that actually matter day-to-day are accuracy (anything below 95% and you’re just creating manual review work), how gracefully it handles bad scans or weird formatting, and whether it plugs into your existing workflow without a whole IT project.
If you’re processing a real mix of document types, the template-free approach is genuinely worth it. The time you’d spend building and maintaining templates adds up fast. My suggestion: grab a representative sample of your actual documents — not cherry-picked ones — and run a trial. That’s the only real test.
This. So much this. Nobody talks about the exception handling piece and it burned us bad early on. Before you automate anything, you need to sit down and really map out what happens when the extraction gets it wrong — who catches it, who fixes it, what’s the SLA. We just assumed it would “mostly work” and didn’t have a clear review process in place. Took us an embarrassing amount of time to clean up that mess. Get your exception workflow solid first, then automate. Don’t do it the other way around like we did.
Jumping in here because this is something we stumbled onto kind of by accident and it made a huge difference — just set up a dedicated inbox like invoices@yourcompany.com and start routing everything there. Sounds almost too simple but training our vendors to send there instead of to whoever they happened to have a relationship with cleaned up the whole pipeline massively. Way easier than trying to monitor 6 different people’s inboxes and hoping nothing slips through.
Oh man, YES! We were in the exact same boat trying to figure out the best AI data extraction for our business. It’s such a pain point when you’re dealing with different document types, right?
After spending a good bit of time kicking the tires on a few different options, we ended up going with ABBYY. Honestly, it’s been pretty solid for us. It totally delivered and really helped streamline things. Glad we put in the legwork!
Hey, thanks so much for all this info, really great stuff you’ve put together here! Super helpful insights, I appreciate you sharing your experience.
I’ve got a quick question about Lido, if you don’t mind. For us, credit notes and refunds are a pretty big deal – we get a decent number of them, and honestly, that’s usually where our current system completely falls apart. It’s a constant source of manual headaches trying to get everything reconciled without tripping up the whole data flow. How does Lido actually handle those? Does it manage them pretty gracefully, or is it more of a workaround situation?
Hey, solid overview there! Really covers a lot of ground. But if I can just jump in with a thought from the trenches – and trust me, I’ve seen this play out too many times – the real game-changer isn’t just how accurate your extraction is. Don’t get me wrong, accuracy is crucial, you absolutely need clean data.
However, if that super-accurate data can’t seamlessly flow into your existing systems, like your accounting software or CRM, what’s the point? You’re basically creating a beautiful data island. I’ve seen folks invest in the “best OCR in the world,” only to end up with huge manual reconciliation headaches because getting that data out of the extraction tool and into where it needs to be is a whole separate nightmare.
So yeah, for me, integration capability is every bit as critical as the extraction accuracy itself. It’s the difference between a real solution and just another fancy tool.
Hey everyone, quick question that’s been rattling around in my head about these AI data extraction tools. We’re really pushing some pretty high volumes here, and even with the best setups, there’s always that stubborn 5-10% that just needs a human eye on it, right? I mean, the AI gets us 90-95% of the way there, which is awesome, but that last bit is always the bottleneck when you’re talking massive amounts of data.
So, for those of you also deep into high-volume extraction, how are you actually handling that manual review chunk? Are you guys running with a dedicated person or even a small team just for that specific task? Or does it kinda rotate through different people, like whoever has some free cycles that day? Just trying to figure out the most efficient way to tackle it without burning anyone out. Would love to hear what’s working for you all!
Oh man, totally! This thread has been a goldmine of info, seriously.
Which actually brings up a specific point for me. I’m really curious if anyone here has gotten their hands dirty with Rossum’s API? We’re in a phase where we’re desperately trying to avoid adding another separate dashboard to our already crowded daily workflow, you know?
Ideally, we’d love to just build whatever we use directly into our existing systems, make it super seamless. No one needs another login or another tab to keep track of, right? So, yeah, just wondering what folks’ experiences have been like on the integration side with Rossum specifically.
Oh, totally seconding Rossum here! I saw someone else mention it, and yeah, that’s exactly what we’ve been using. Honestly, in my experience, we made the switch to Rossum maybe two months back — or was it three? Time flies! — and it’s been pretty rock solid for us since then. But seriously, the thing that really sold us, and continues to impress, is its accuracy. We deal with a lot of really messy, sometimes half-scanned, sometimes crumpled documents, and it just handles them with surprisingly few errors. That was the game-changer for us, really.
Hey everyone, just wanted to throw in a quick thought here that’s maybe a bit off-topic from which tool is best, but honestly, it’s a huge game-changer when you’re setting up any AI data extraction for invoices.
Before you even start looking at Zapier integrations or how to train your AI on specific layouts – seriously, like the very first thing – go set up a dedicated email address just for those incoming invoices. I’m talking something simple like ap@yourcompany.com or invoices@yourcompany.com.
Trust me on this one, this one small step makes the entire pipeline so much cleaner and easier to manage. Your AI has one dedicated spot to pull from, your team’s inboxes don’t get flooded, and troubleshooting any issues down the line becomes way less of a nightmare. It sounds basic, but it’s a pro tip I wish someone had hammered into me early on. It just keeps everything incredibly tidy.