DocuPipe
DocuPipeReviews from AWS customer
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Fantastic onboarding, great support, incredibly accurate document processing
What do you like best about the product?
The most striking feature to me was the instant onboarding into the product so that it made sense and I could try it out immediately. It uses AI to help with this process - creating starting schema that are likely to work well with your documents.
Even if you have a reason that you can't go with DocuPipe in the end, start experimenting with DocuPipe because it will help orientate you in your project and help you understand what the capabilities of AI might be for your documents.
There is a flexible API with a lot of useful different (and inter-related) features. It is very easy to implement.
I enjoyed valuable support interactions with the team.
Most importantly for the long term, the document/text recognition and classification was extraordinary, and much better than anything else I could get to work.
Even if you have a reason that you can't go with DocuPipe in the end, start experimenting with DocuPipe because it will help orientate you in your project and help you understand what the capabilities of AI might be for your documents.
There is a flexible API with a lot of useful different (and inter-related) features. It is very easy to implement.
I enjoyed valuable support interactions with the team.
Most importantly for the long term, the document/text recognition and classification was extraordinary, and much better than anything else I could get to work.
What do you dislike about the product?
I think it's a fantastic product, but if pushed... I found some of the API slightly inconsistent to start with, but it has now been improved following feedback.
What problems is the product solving and how is that benefiting you?
Our business workflow used to involve a lot of alt-tabbing from emails/PDFs into other software, copy-and-pasting values. This has now been completely automated, saving time and reducing errors. It allows our staff to focus on more meaningful and value-added parts of their roles.
Reliably structured output that cites its sources (!)
What do you like best about the product?
For our purposes, knowing that we were going to get an output that strictly adhered to what we were asking of the model was crucial. We experimented a fair bit with other services, and found that none of them were reliably taking our giant pile of unstructured documents and pulling out the data we needed *in the format we required*, without hallucinating wildly.
This is also one of those delightful startups whose founders are still actively engaged with their customer base. You get an email from them upon signup that assures you it isn't from a bot and invites you to talk to them directly, and I was pleasantly surprised to discover that is actually the case and that they're very responsive and eager to make their service work the way you need it to. While this state of affairs can't last forever, it's great to have now.
This is also one of those delightful startups whose founders are still actively engaged with their customer base. You get an email from them upon signup that assures you it isn't from a bot and invites you to talk to them directly, and I was pleasantly surprised to discover that is actually the case and that they're very responsive and eager to make their service work the way you need it to. While this state of affairs can't last forever, it's great to have now.
What do you dislike about the product?
The web interface is still a work in progress, and while it's perfectly serviceable, there are nice-to-haves like being able to search / filter through a dataset by filename, upload date, etc. which would be appreciated. No surprise there for a new service, though, and I expect they're working on it.
What problems is the product solving and how is that benefiting you?
We were faced with the prospect of taking thousands of large, dense, unstructured legal documents with wildly inconsistent formatting and terms, and dissecting them for numerous data points which would give us a feel for the landscape of a legal practice area in a particular state. This could in theory be done manually, but it would take hundreds of person-hours with no economy of scale: every document takes approximately as long as the last, per page, to the upper limit of the person reading the document, stripping data out of it, and inputting it into a spreadsheet. In the end, you get your output, but the next time you have to do it - and we anticipate this being an ongoing process - you're making another huge investment of person-hours. If the end product was vital, requiring perfect accuracy in the dataset, that investment of person-hours would unfortunately be necessary: nothing LLM-powered is accurate enough yet that I'd be willing to stake the fate of anything seriously important on one's performance. But for a project like this which provides some real benefit to our decision-makers, but isn't so critical that we *absolutely must* ensure the accuracy of every data point, we were willing to consider automation. Our team was willing to accept around 95% accurate output, but even that low bar was unattainable by most of the services we trialed, and I was looking for at least 99% on integer fields. DocuPipe not only managed to meet and exceed those requirements, it "showed its work" by citing sources within the documents to back up its claims. Having the dataset processed through DP also opened up the possibility of interrogating the dataset itself in a RAG-like fashion, but we haven't dived into those features yet.
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