UiPath Document Understanding Reviews

UiPath Document Understanding allows us to extract information from documents, classify the document type, and optionally validate the data with a human. We also train the bot to learn from the mistakes it makes.

We extract data from boxes and tables, as well as handwritten words.

UiPath Document Understanding helps us extract from certificates, the names, addresses, states, dates, taxpayer identification numbers, and signatures. We also extract information from health insurance documents such as the name, patient's address, code, and contact information. Additionally, we have invoices that we have to extract from a table with rows.

UiPath Document Understandings' ability to handle different document formats depends on which extraction tool we use and the clarity of the documentation.

I am very satisfied with UiPath Document Understanding AI and machine learning.

UiPath Document Understanding integrates well with other tools. Some of my colleagues have integrated the solution with ABBYY.

It has helped reduce human error.

The most valuable feature is the ability for humans to train the bot. It can learn from human experience, so if we include steps in our framework for humans to validate extraction or classification, the bot can learn from them to improve document understanding and precision in future document processing.

The extraction and classification process should be simplified.

I have been using UiPath Document Understanding for six months.

UiPath Document Understanding is stable.

UiPath Document Understanding is scalable.

The technical support is good.

Positive

The initial deployment was straightforward. The deployment took three hours to complete.

I implemented the UiPath Document Understanding myself.

I would rate UiPath Document Understanding eight out of ten.

Our organization also uses ABBYY.

We saw the time to value within the first three days of using UiPath Document Understanding.

I am working with Mercedes teams from Germany. They have many handwritten documents in their archive, and they wanted to digitize them. Maybe we want to digitize them with UiPath Document Understanding tool, but it is not now. Maybe next year. Most of the document types are invoices. 

Our robots will integrate these documents with SAP because these invoices have to be in SAP. The process involves OCRing the documents, sending an email to the business units, and integrating with SAP. I prepared one framework for these tasks.

Using UiPath Document Understanding helps increase the data correction rate. For example, for one document, one business unit spends an average of five to six minutes, however, our robot does it in about 30 to 40 seconds.

The UiPath Document Understanding tool is very easy to use for document understanding. I like it for its ease of use. 

AI Hub is also useful and easy to use. Creating taxonomy and clarifications from labels is straightforward. 

The integration with UiPath Studio is smooth, making it easy to create and use machine learning models.

Right now, we are trying to use Instabase, which is another tool for document understanding. It may be one one step ahead. It is faster than UiPath.

We are currently trying to use Instabase for document understanding. We have not observed stability issues with UiPath Document Understanding so far.

We have technical support with UiPath Turkey team. Sometimes they come to our office and prepare some POCs with UiPath tools. I would rate them nine out of ten.

Positive

The implementation team sometimes visits our office to assist with POCs and provides support for UiPath tools.

Our data correction rate increased to 80% with the implementation of UiPath solutions.

I am not aware of the pricing details. My manager handles that, and as far as I know, Instabase is more expensive.

We are trying to use Instabase as an alternate solution to UiPath Document Understanding.

I'd rate the solution eight out of ten.

We use UiPath Document Understanding to extract data from invoices so that the data can be entered into SAP applications. We often receive multiple invoices in different formats. To use UiPath Document Understanding, we need to train the document. This means that we need to provide a sample template that the robot can use to learn how to read the invoices and extract the desired data. The labels in the fields are important, as they tell the robot what information to extract. 

For example, the invoice number, seller's address, quantity, and any other required information. The table of line items also needs to be extracted. We define the fields from which we need to extract data. We use the form-based extractor or machine learning extractor to extract the data. We export the extracted data to an Excel file. This allows us to collect all of the data from multiple invoices in a single location. We can also implement a validation step to ensure that the robot has extracted the data correctly. This can be done easily by using the human in the loop to manually review the extracted data before it is entered into SAP applications.

Our organization has many invoices of varying formats. To simplify the process, we implemented UiPath Document Understanding to extract the required data from all the unstructured invoices and import it into a single, structured Excel sheet.

Using the different endpoints and machine learning, we can extract data in different formats including handwriting and signatures.

UiPath Document Understanding's machine-learning capabilities are good. We use it to extract the information we need from documents of any format, and then align it with the corresponding table.

We easily integrated UiPath Document Understanding with UiPath Studio by connecting the data flow from the different applications to extract the necessary records.

We have so many invoices that UiPath Document Understanding helps us classify them. The document is first classified into a type of document, such as an invoice or a resume. This segregation of data allows us to send the invoice templates to our folder and extract the data and all the details without having to read them one by one.

With UiPath Document Understanding, less and less human validation is required over time. Each time we run the process, we can select from an option for attended or unattended automation. The other option is to use the confidence score to determine whether human validation is required.

The handling time before automation for one PDF document was five minutes and now with automation, it is one to two minutes.

UiPath Document Understanding helps to reduce human error by removing the fatigue factor and by continuing to run after hours without breaks.

There are many features that can be added to the action center to keep humans in the loop to evaluate the accuracy of the extracted data. We can also set a confidence score threshold, such that if the confidence score is greater than 80 percent, we can take a certain action. Otherwise, we need to manually correct the problem in the action center.

UiPath Document Understanding could be more user-friendly. There are so many endpoints, and entering the API is also a manual task. Currently, it is a long and complicated process with many steps. If we remove all of those steps and use the ChatGPT or OpenAI library, we can start using the solution with fewer steps.

I have been using UiPath Document Understanding for six months.

UiPath Document Understanding is stable.

UiPath Document Understanding can be scaled for multiple invoices.

The deployment was straightforward and required a few days.

I would rate UiPath Document Understanding eight out of ten.

We are processing around 12 documents using UiPath Document Understanding. The documents are in PDF format.

We have four members using the solution.

I primarily use Document Understanding to onboard clients and gather information for our databases.

Document Understanding's best features are the action center and verification mode.

Document Understanding would be improved with more AI platforms. Its localization and OCR could also be better. In the next release, Document Understanding should include custom and pre-defined reporting.

I've been using Document Understanding for two years.

Document Understanding is scalable.

Document Understanding's technical support is good and affordable.

The initial setup was straightforward.

We saw an ROI from Document Understanding within six months.

Document Understanding's price is quite high.

I would recommend Document Understanding to other users and rate it eight out of ten.

The primary use cases of our solution involve data extraction from various sources, such as checkbooks and cards issued by the bank, specifically within the banking process. Our clients also specifically require data from certain accounts and passbooks. We facilitate this by extracting the necessary data from passbooks and storing it in databases such as Excel or SQL.

At times, the scanning functionality of the tool may fail to generate accurate output, resulting in the need for manual input. However, during the proofreading process, all details can be verified and corrected as necessary. Scanning is position-based and may be subject to distractions, which can cause inaccuracies in the results. In short, the solution's scanning functionality is an area that is problematic.

Since the solution's scanning feature fails to perform its task accurately, UiPath should consider adding some extra features to the tool, like an AI tool that can help the solution in its extraction process.

I have been using UiPath Document Understanding for years now. Also, I am using the solution's latest version and working as a consultant for the solution.

It is a stable solution. Stability-wise, I rate the solution an eight out of ten since we don't get the desired results from the solution. Some features need to be added to the solution in the future to make it more stable.

It is a scalable solution. Scalability-wise, I rate the solution a nine out of ten. Also, my company deals with enterprise-sized clients.

The solution's technical support is good. Basically, it's a platform for developers. If there are any questions, one can contact technical support. Also, I have read many questions and their solutions on the internet. I rate the technical support a ten out of ten.

Though the solution's initial setup process wasn't easy, it can be managed. On a scale of one to ten, where one is difficult and ten is easy, I rate the solution's initial setup an eight and a half. To be more concrete, I would rate it an eight.

The deployment process took a year. Also, there were more than ten RPI consultants involved in the deployment process. We require two system engineers for the maintenance of the solution in our company.

I believe that the solution is reasonably priced. Someone who is not a technical person would consider it expensive. However, it is a reasonably priced solution for technical people. On a scale of one to ten, where one is cheap, and ten is expensive, I rate the solution a nine. The payments for the solution are to be made on a yearly basis.

I would tell those planning to use the solution that they need to consider using UiPath Document Understanding since then UiPath will consider adding more features to the tool in the future. Overall, I rate the solution a nine out of ten.

My use case for UiPath Document Understanding is invoice processing, as most vendors share invoices that need to be posted in the ERP (Enterprise Resource Planning) software. The vendor sends invoices via email whenever my organization purchases goods or products, so the ERP is updated whenever a product is received. Still, before making a payment, there needs to be some validation. For example, you have to check that the invoice sent matches the product received, so this is where I use UiPath Document Understanding.

I use the solution to extract the data from the invoice. Then I validate it with the ERP software in terms of which product has been received, including the unit price, shipping charge if applicable, the tax, which depends on the state, and product quantity. My organization needs to validate all data before paying the vendor, which is where UiPath Document Understanding becomes useful.

The most common type of invoice processed within the organization is the purchase order, which is more structured, so extracting the data is easier. It's unlike a utility bill, such as an Internet or electricity bill, with no purchase order and a different invoice structure. It makes it a bit more challenging to train the machine learning model in UiPath Document Understanding when processing utility bills versus purchase orders, though not impossible.

Currently, my organization focuses on purchase orders in terms of UiPath Document Understanding usage. Still, once that's successful, there is a possibility of using the solution on other invoice types.

About sixty-five to seventy percent of the invoices are automatically processed with the help of UiPath Document Understanding, though it could vary from business to business. Failures rarely happen and are usually the result of the business rules or conditions. About twenty percent of the processes require human intervention.

UiPath Document Understanding helped improve my organization by reducing human errors and intervention. Humans handled the entire task before the organization used UiPath Document Understanding, and it was a huge task that could only be completed within the year. In contrast, UiPath Document Understanding makes it possible to complete the task within a month, specifically completing all invoices that flow monthly within the same month. In the past, you had to delegate more work to humans, but after integrating with UiPath Document Understanding, the task required less human work, and the solution used up fewer resources compared to the amount of resources used up by humans doing the job. The solution enabled humans to work on other tasks, helping free up staff time by sixty to seventy percent.

I found UiPath Document Understanding helpful in automating different types of processes. One process can be split into multiple processes or sub-processes.

For example, when the vendor sends emails, I can download all emails through automation, mainly via UiPath Document Understanding. Downloading the emails can be a continuous process, scheduled at different intervals in a day. The next sub-process made possible by the solution is extracting the data from each invoice.

You can also use UiPath Document Understanding for ID management or extracting data from IDs, such as passports or government-issued IDs, which I like. 

Signature matching is another feature of the solution that you can use, which I found good, though I only tested it. That feature wasn't used in production because, in my organization, UiPath Document Understanding is primarily used for data extraction from invoices.

I also found the parking feature in the solution interesting. For example, when you train the model but have data that's not extracted completely, or you are unsure, you can park that invoice and then have a human interfere to validate it.

UiPath Document Understanding can handle varying document formats, such as handwriting and signatures. Still, accuracy in data extraction from handwriting is fifty-fifty and would depend on the quality of handwriting. For example, the digit seven could be mistaken as one. For signatures, about sixty percent of the data matches or is correctly extracted by the solution.

The AI or machine learning capability of UiPath Document Understanding works and is successful seventy to eighty percent of the time. My organization trains the model with a set of invoices from each vendor to pick specific data, such as invoice number and date. Data extraction success varies with the number of invoices trained and if there's a new template, but you can expect the success rate of the machine learning module in UiPath Document Understanding to be between seventy to eighty percent.

UiPath Document Understanding has two parts: the AI, similar to integrating ChatGPT to your API, and the forms, where a human can validate the form as the solution acts as a parking station where the data extracted by the bot, if you're unsure of the data or the data wasn't extracted properly, may be parked there so a human can intervene and validate it.

In terms of how often human validation is required for UiPath Document Understanding outputs, you'd put specific logical conditions after the extraction. For example, the quantity, unit, and total amount would be integrated. Suppose the quantity and unit price are extracted correctly, but the total amount was incorrectly extracted. In that case, that won't always need to go through human validation because UiPath Document Understanding can automatically calculate. For one hundred invoices, at most, five to six invoices go through human validation, while the rest don't have to.

UiPath Document Understanding can be integrated with other systems and applications, and my organization has tried it by integrating the solution with Python. My organization built a machine learning model with another system where UiPath Document Understanding was integrated with UiPath data services, and there was even a dashboard that gave insights on how many invoices were processed, which invoices fell under which state, and so on.

An area for improvement in UiPath Document Understanding is data extraction from scanned documents. It could be better because I've observed a ten percent failure or flaw whenever the vendor scans and converts documents into PDFs.

I've been using UiPath Document Understanding for two and a half years.

Sometimes, the bot or system resources cause UiPath Document Understanding to crash, but not frequently, and it's covered by the one-hour maintenance a month. I find the solution stable.

Scalability in UiPath Document Understanding is very good because my organization can scale the same logic or script it developed with UiPath Document Understanding into any of its systems. If you deploy the solution in one ERP system, for example, you can install it in another system at any point in time, and configuration would take about one to two days, plus testing. UiPath Document Understanding is scalable.

I've contacted the technical support for UiPath Document Understanding. At the initial deployment stage, a specific resource was allocated to my organization, mainly because the solution was new. It was in the beta stage so that I could contact the resource for any difficulties or doubts, and I've contacted the resource multiple times.

I'd rate the UiPath Document Understanding support team a nine out of ten because sometimes there was a delay in communication as the previous resource was from a different time zone, so another resource was allocated to my organization, which belonged to the same time zone as mine.

Positive

My organization used to do the tasks manually and tried Power Automate for automation, but found that solution a bit difficult compared to UiPath Document Understanding. UiPath introduced UiPath Document Understanding to my organization, and the organization found that it was a proper solution.

I was involved in the initial deployment of UiPath Document Understanding, and I found the process straightforward. You just had to activate the licenses one time. After that initial license activation, you can quote the number of licenses you need to UiPath, and it will be automatically updated in the Orchestrator. The setup would take around one week to fifteen days in the initial stage, and the maintenance comes after, which takes just one to one and a half hours.

I deployed UiPath Document Understanding with about three to four team members. The solution was deployed in-house.

I've seen ROI from UiPath Document Understanding for the past two and a half years because the solution helped reduce about seventy to eighty percent of human tasks, which meant that my organization could utilize the staff in other processes that could not be automated or tasks that had non-standardized logic or conditions. Though the ROI could also depend on how much you invested and your actual staff, I've observed ROI from UiPath Document Understanding.

My organization saw the potential in the solution after some demos but could not see the value immediately. Initially, the results weren't as good, with some difficulties. Still, with the help of the UiPath support team, improvised data extraction, and two to three months after moving UiPath Document Understanding to a production environment, the organization saw value from it.

UiPath Document Understanding offers fair pricing, primarily if you consider the number of resources and human work the solution reduces, plus the ROI you can get from it.

My organization had a native PDF document extraction tool that could have worked if the vendors had similar invoices or templates, but that wasn't the case. My organization also tried a machine learning tool with some outsourced Python, but it wasn't very standardized or robust, so it wasn't good to use versus UiPath Document Understanding.

UiPath Document Understanding has a public cloud deployment within my organization, as there was a constraint with deploying it on-premises, requiring a VPN within the same network. Hence, my organization chose to install it on the cloud via a cloud orchestrator that even allowed access from anywhere in the world.

The initial deployment of UiPath Document Understanding required two people, but as the number of businesses increased, my organization had to add five to six people to the deployment team to handle the installation, script maintenance, and connecting to the client center. The installation requires three to four people and two more for support. Six people to deploy UiPath Document Understanding for thirty to forty businesses would be sufficient.

UiPath Document Understanding doesn't require much maintenance because UiPath usually does automatic updates or feature updates to the cloud orchestrator and sometimes to the scripts my organization built. Maintenance is minimal and would only take about one hour per month.

My rating for UiPath Document Understanding is eight out of ten because it still needs improvement in the quality of scanned documents and data extraction.

My advice if you're looking into UiPath Document Understanding is to check whether it's a solution you really need. Think about the tasks requiring UiPath Document Understanding and the features you're looking for. Consider the costs and time associated with the solution, as the clock would start ticking after you buy the license. You need to analyze precisely why you need UiPath Document Understanding and what features you need from it. The team responsible for it should also be well aware of the solution's features to maximize its use.

My organization is a UiPath partner which buys the license and provides it to businesses.

We are partners with UiPath.

Machine learning is a very good feature. In general, this is a great tool that can be used in any industry and integrated with other technologies such as iPhones, C-Sharp, and .NET.

The solution is quite expensive. 

I've been using this solution for five months. 

The solution is stable. 

The solution is scalable. 

The customer support is very good. 

Positive

The initial setup is straightforward and doesn't take long. 

The solution is expensive because of its consumption of AI units when working with a huge amount of buckets. There are additional fees for the robots if you purchase the action center. 

If you have the budget, this is a great option for building and deploying the IDB solution. It's not hard to develop, it's the issue of licensing costs. 

I rate this solution 10 out of 10. 

We primarily use Document Understanding to read various invoices and PDFs in different languages, including Chinese. Our processes aren't completely automated. We have a human-in-the-loop model in which the data is validated.

With Document Understanding, tasks that would normally take eight hours manually can be completed in three to five hours. It still requires some human intervention, but 90 percent of the processing can be automated.  It has significantly reduced the amount of human effort required for these tasks. 

Automated processing is Document Understanding's most valuable feature. 

The coding machine learning could be a little bit better.

The deployment is quick once we develop the active location.