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!