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SERGIO WITTENBERGER: Hello, everyone.
My name is Sergio Wittenberger.
And today, I'm going to showcase how
we can extract data from unstructured documents
like contracts.
As one of the most common challenges in automation,
we all know that document understanding
can help you extract data in a fast and reliable manner.
It is extremely difficult though doing this
with unstructured documents, or was until now.
By unlocking the power of GPT models,
this issue has been solved and will come soon for you
to play test and implement with.
Here on the left hand side, we have a sample contract
from the US government.
As you can see, if you compare it to any other contract,
the structure of it is quite different.
And it will always differ from contract to contract.
This can cause lots of headaches when
trying to extract specific information,
mainly because you would need to find a way
to understand what that text means for the extraction
to be correct.
For this, we have our robot with a sample script,
here on the right hand side.
It consists of three main steps digitizing the document,
taking the data from the PDF and turning the text within it,
creating the questions we want to find answers
for with some input dialogues, and finally extracting
the information with our ask GPT activity.
Do note, names are subject to change.
So let me just run this.
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So we select the contract.
And we input our first question.
For instance, who is the lessor?
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OK, as we can see, the answer is SAS Center Inc.
And that is correct on the document.
We have the evidence, made that entered into this date
by and between SAS Center Inc. And it asks us
if we wish to continue.
We will.
And we will ask, who is the lessee?
This would be the US government.
And as you can see here, it states that it's
the United States of America.
We have the evidence which is, and the United States
of America have enough to call the government.
And this is extracted from the actual document.
Yes, we wish to continue.
So now, we could ask another question
like, what is the annual Gantt for the first year?
And after a while, we will have the answer
which is, almost $400,000.
We have the evidence.
And we can see it here as well, annual Gantt of the amount.
And this is year 1 through 5.
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Then let's ask what's the Gantt after the eighth year?
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And this, as you can see, it's a bit lower.
And here's 8 to 10, it's $800,000.
And we have that here.
We will ask search again for the document.
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After five years, we can terminate the lease.
The lessee can terminate the piece.
And again, we have the evidence there.
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And let's try one final question,
what is the prior notice for termination?
It's prior notice of termination is 120 days.
And we have this evidence which also
states the five-year interval but also the 120 days.
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This is a simple sample script that I just showcased.
But this capability will come soon to all enterprise features
as well like the document understanding process template.
With this polygon log, you will be
able to automate any type of documents very easily.
But this is just one of the open AI integrations
UiPath is working on.
And we're really excited to see how these capabilities will
take intelligent document processing to the next level.
Check the description under the video
to find more details on this and the integration availability.
Thank you.
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