UiPath Document Understanding Reviews

We are a system integrator in the manufacturing industry and our clients use UiPath Document Understanding for their invoicing cycle processing.

Previously, our clients manually entered invoices into their systems, seeking a solution to automate this process while still maintaining controls for verification and audit purposes. We implemented UiPath Document Understanding to address this need.

Data entry is the most common use for UiPath Document Understanding.

In Italy, a common document format for simplified sales invoices is the BBT, which lists the total cost of the entire merchandise unit.

Since our clients are primarily small and medium-sized businesses, UiPath Document Understanding processes around 10,000 documents annually.

The documents contain a header and a large table where data is extracted.

Around 30 percent of the documents are fully completed by UiPath Document Understanding.

AI and machine learning do a great job sorting and identifying fields and documentation orientation. Managing different layouts is the most valuable attribute of AI.

Companies that use the UiPath platform can easily integrate UiPath Document Understanding using a few modules.

Human validation is required for 20 to 30 percent of cases and it takes less than one minute to complete.

UiPath Document Understanding helps reduce human error by 70 percent.

UiPath Document Understanding has helped free up around 70 percent of people's time to work on other projects.

For most of our clients, the time to value is usually six months.

The prebuilt algorithm for extracting foreign invoices is the most valuable feature because it eliminates the need for us to build anything from scratch.

The signature and handwriting are a pain point for the OCR and have room for improvement.

The extraction logic portion of the UI is not as user-friendly as the rest of the platform and has room for improvement.

I would like to have generative AI integration added to a future release.

I have been using UiPath Document Understanding for two years.

The stability of UiPath Document Understanding is good. The higher the stability the less maintenance is required.

The scalability can be improved with the help of generative AI. It is difficult to build an algorithm and move to another project without making important changes to it.

The technical support is good.

Positive

The initial deployment process is not user-friendly. It requires a lot of steps, although depending on the size of the deployment, one person can usually manage it.

The average deployment takes around one month to complete. 

We implement the solution for our clients.

The price for UiPath Document Understanding is a bit expensive.

I would rate UiPath Document Understanding a nine out of ten.

The number of people required for maintenance depends on the project. It can go from one person up to seven.

Our use cases are in the banking sector. We have certain PDFs that contain customer data, and we have to extract data from them and compare it with the data provided earlier. The PDFs can be scanned, handwritten, or native PDFs.

It gives us a complete, unattended process for extracting details from PDFs. Otherwise, that would been redundant work done by specialists. It is saving us around 800 workers.

For one record, we used to take about half an hour, but Document Understanding completes things in seconds. It's very quick. It has also reduced human error a lot.

In UiPath Document Understanding, I use two extractors. One is the Intelligent Form Extractor and the other is the Machine Learning Extractor. The first one is very useful and user-friendly. When we have a single template or two to three templates, Intelligent Form Extractor is much easier to use. But if we have multiple templates, it's better to go with ML Extractor as you can train the model with different templates.

The artificial intelligence features are very good. The more you train it, the more you will benefit from it.

Also, integrating Document Understanding with other systems or applications is not complex. It's quite easy. Even for attended processes, if you combine UiPath Document Understanding with Action Center, it's almost like unattended processing.

Document Understanding is a combination of the extractor and OCR. Depending on the taxonomy that you give and the classification that you define, it is accurate. But there are multiple OCRs and I find UiPath Document Understanding to be as accurate as Microsoft or Textract OCR. If the accuracy was improved, it would be much better.

There are also problems with handwritten documents. The results from those are not 90 percent accurate. With scanned and native PDFs, it is 90 to 99 percent accurate, but the accuracy of handwritten documents is somewhat less. If they could improve on that, it would also be helpful.

Also, in the banking sector, we can't use the cloud-based Orchestrator or cloud endpoints for OCR. Getting approval for ML Extractor takes a lot of time for us. Instead, if we could go with Intelligent Form Extractor, it would save a lot of approval time. If they could improve and add features to Intelligent Form Extractor, it would make our lives easier.

I have been working with UiPath for five and a half years and with Document Understanding for one and a half years.

I have not experienced it crashing or lagging.

The scalability is good. I haven't seen any waste of money.

We have not contacted their technical support very much.

We do not use UiPath in the cloud; we have our own internal version, so the initial setup took time for us. But that had nothing to do with UiPath. It was because of our company's policies.

It took time for people to understand it, but once they got it, it went well.

It took some time because Document Understanding is pricey compared to other tools in UiPath. After 10 to 15 days, when there were fewer errors, that's when we started to see profits.

I would suggest combining other technologies with UiPath. That's when you see more benefit from Document Understanding.

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.

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 use the tool for a couple of my client projects. My clients receive physical mail and may need to scan data to run processes like automation on it. Another use case is document classification. The solution helps with processes like classification, data extraction, and automation. 

UiPath removes manual processes and reduces human dependency. It takes a lot of effort to go through each physical mail or email, extract the data and transfer it to Excel. However, the solution automates the process and works 24/7. 

It gives a complete package to process and understand documents. The valuable features include taxonomy modification, classification, workstation, etc. There are out-of-the-box features like ML models which you can custom build.  

We have saved time with UiPath Document Understanding. We have seen a 50 percent improvement in scanning processes. Compared to humans, the tool runs 24/7. The human error rate has also been reduced. Our human error rate is five percent now compared to the previous 15 percent. 

UiPath Document Understanding has also freed up our staff who now spend more time on critical tasks. 

There is room for improvement in UiPath Document Understanding's pricing. It is expensive for small clients. Currently, there is a big gap between the basic package and the 200,000 packages. There is no package in the middle for small agencies. 

I have been working with the solution for more than five years. I started to work on the product when it was still under development. 

I have not encountered any performance issues. 

The exact number of documents processed per client varies. However, it ranges between 1000-3000 per week. The documents processed are very less. We process 10-15 documents daily. 

UiPath Document Understanding's support is always ready and helpful. 

Positive

UiPath Document Understanding was easy to implement and put into production. The timeline can change when you create your ML model. 

We have seen ROI with UiPath Document Understanding. 

The document format is mostly PDF and can be structured or semi-structured documents. We have not dealt with handwritten documents. Our real-time use case is for structured documents like emails and invoices. Most of the client documents go through without any errors. However, there is a five percent failure rate that needs to be considered since the document may contain unexpected data. 90 percent of documents go through it. 

The solution handles signature-based documents. We are still working on that prototype. We faced issues with seals. It differs from department to department and state to state.

The tool's AL and ML features work fine for us. We leverage these features for driving licenses. AL and ML keep a check on document generation. UiPath Document Understanding has come up with an API-based document understanding model which we will leverage soon. 

We implement human validation when we use anything new so that everything works as expected. 

I would rate UiPath Document Understanding an eight out of ten. 

Our primary use cases for UiPath Document Understanding are processing invoices for five different clients and importing/exporting documents to extract vital information, mainly from unstructured documents. These five clients are from various industries, including transportation, scientific research, food services, and clothing.

I processed 400 documents per day for one client and 20 documents per day for the second client.

The documents processed were in PDF format.

90 percent of the 400 documents processed per day for a single client were fully automated. However, only 50 percent of the 20 documents per day were automated due to their greater level of unstructured nature. As a result, the remaining 50 percent had to be sent to the action center.

AI and machine learning for Document Understanding are game changers. Machine learning was helpful in identifying the various areas of the documents from which I needed to extract different types of information, making the process quicker.

The default model didn't work for me because I needed to extract information from documents written in French. Thus, I had to create my own model using AI, which proved to be exceptionally beneficial for handling the French text and its accents.

Integrating UiPath Document Understanding with other systems and applications in our environment works well. The solution was able to retrieve the PDF document from an email, extract the details using the command, and apply those details to an application, saving a substantial amount of time.

UiPath Document Understanding serves as a safeguard in relation to cost and time savings, as it diminishes the manual workload for employees and minimizes errors. For a job that took a human eight hours to complete, the bot was able to do it in three hours.

The extent of human validation needed for Document Understanding varies for each client. For one client, no validation was necessary as the solution effectively extracted all required information from the documents. However, for another client dealing with diverse document types, errors occasionally occurred due to character placement. This was particularly evident when email addresses were positioned differently, some at the top and others at the bottom of the documents, posing challenges to the robot's detection capabilities. In such instances, a validation process was implemented. Every seven days, ten percent of the batch would be sent to the Action Center for validation.

The time saved with UiPath Document Understanding is exemplified by an organization that previously had to spend three days manually extracting information from 400 documents every month. However, with UiPath Document Understanding, this task now only takes two hours.  

Machine learning is the most valuable feature of UiPath Document Understanding.

I encountered difficulties with UiPath Document Understanding in determining the appropriate OCR to use for certain files. These files required extracting both the company logo from the page and the digitized text, posing a challenge. The OCR engine faces difficulties when processing signatures and scanned documents with unclear handwritten text.

The robot faces difficulties in recognizing when there are multiple documents on a single page. This necessitates manual intervention by first splitting the document and then re-digitizing each part separately. 

I would like a split feature in a future release of UiPath Document Understanding.

I have been using UiPath Document Understanding for one month.

UiPath Document Understanding is extremely stable.

UiPath Document Understanding is scalable.

The technical support responds promptly and strives to resolve our issues quickly. However, there is room for improvement. For instance, we encountered an issue with the Action Center, and the support team was unable to determine the cause for three days. Eventually, someone from my team resolved the issue.

Neutral

The initial setup was a bit complex.

I also assessed FlexiCapture, but I discovered that UiPath Document Understanding was more user-friendly. Coming from a scientific background, I found that UiPath Document Understanding offered a more logical and less complex solution.

I would rate UiPath Document Understanding nine out of ten.

It took me one week to study UiPath Document Understanding and to present it to my organization.

I realized the benefits of UiPath Document Understanding once I completed my first project.

The quantity of personnel needed to maintain the solution relies on each project. In the most recent project I participated in, we needed a total of two individuals, one of whom was an administrator from our team.

When using UiPath Document Understanding, always ensure that the number of structures is the same each time to prevent errors.

I believe that utilizing communication mining would be more effective with the AI Center.

Our client has PDF invoices and we use the solution to extract the details from them. We are using it in finance and health care. We have about 16 templates that we process now. The data is in semi-structured format and we mostly process things like signatures and tables. Out of the 16 templates, about 12 are completely processed automatically.

It has helped us automate finance statements and invoice billings.

Another benefit is that it has mostly helped reduce human error. We have a criteria of 75 percent matching. Out of 10 PDFs we have been getting eight PDFs with at least 75 percent matches. It has also helped free up staff time.

The taxonomy and Validation Station are among the most helpful features for us. If anything is extracted incorrectly, we can manually extract it there.

And we have included the AI Center for our customers to interact with PDFs to be extracted. Based on the approval or rejection feature, our customer can determine which kinds of PDFs they can automate.

I also like the table extraction feature. UiPath is very good with structured data.

Handwriting is more complex. We have not been able to get handwritten signatures correctly extracted in different languages. Our customer is in Dubai, and the solution cannot accurately process signatures in the local language. But it is a great tool for handling structured and semi-structured formats.

Another of the disadvantages is that we cannot include another tool. For example, with ABBYY extraction, we can integrate the process with any other product. We can integrate Document Understanding using JSON templates, but it is a bit of a complex model to extract the data from the JSON.

There is also room for improvement in long-running table extraction. If a table continues for more than 10 pages, in some cases, we have observed that it only extracts six or seven pages and skips the last pages.

I have been using UiPath for about 10 years.

Overall, the product is stable.

In our case, the use of Document Understanding is restricted to a particular group of users, around six or seven people.

The technical support from UiPath has been pretty good in the last year. It has been a very good experience. 

We used Azure DevOps for the deployment and we faced some issues regarding the deployment with UiPath and Orchestrator. We had a very good response from the UiPath technical team.

There is some room for them to improve the speed of the response because we often used to get late responses. But the resolutions are good.

Positive

We were using ABBYY, but it is more like a developer's tool with everything a developer needs for extracting fields. But we can train and retrain Document Understanding. In that way, I feel it's a better tool.

The pricing is reasonable.

As for additional costs, the solution is based on OCR, and sometimes the OCR cap is exceeded. It's not a major cost. Per month, we will have two or three scenarios like that. With ABBYY, once the cap was reached, we had to wait until the next day to use it again.

We did not evaluate other solutions. Using Document Understanding was a requirement from the client's side.

In terms of human validation for Document Understanding output, we have a limit of 75 percent correct scenarios. If it is below 75 percent, the user will be notified.

The solution doesn't require any maintenance unless the client requires more fields to be extracted. Only then are there changes that I need to make.

My advice is that if you are starting to learn about Document Understanding, you need to learn more about the taxonomy and what fields you are extracting. You need to have clarity on which position you are extracting, as it mostly depends on the position.

We are a service-based company, having a team size of 120 RPA developers.

The core technology of UiPath Document Understanding is OCR, which can be either ABBYY or Google Vision. The drag-and-drop facility, decision-making accuracy, and user-friendly actions make UiPath Document Understanding helpful for users.

UiPath Document Understanding offers a feature that allows users to process specific documents, such as purchase orders or invoices. The tool provides the ability to define and segregate actions and highlight fields using something similar to rubber band techniques. This means, in an invoice, users can easily highlight specific fields within the document and train the model accordingly. The process is user-friendly and can be easily executed even by those with basic knowledge of flow.

One area of improvement for UiPath Document Understanding is the accuracy of handwritten documents. While the solution supports handwritten documents, the accuracy percentage is not as high as desired. Therefore, it would be preferable if UiPath could focus on improving the accuracy of handwritten documents in their next release. Although they currently support handwritten documents, the accuracy is still low.

Also, the documents for the solution are lengthy, and because of this, I say that the solution should incorporate some video sessions.

I have experience with UiPath Document Understanding, in which the users are provided with multiple types of Document Understanding and a process-wise range of available documents. Overall, I have been using the solution for three to five years. Also, I am using the latest version.

The solution is quite stable, and I am happy with its performance in my programs and processes. I would rate the solution's stability an eight out of ten

The solution's scalability with the cloud version is really good. I rate the solution's scalability an eight out of ten.

Around 30-40 people work on UiPath Document Understanding in our company. The extent to which we use the solution, particularly in terms of utilization of bots or document processing, is dependent upon the volume of usage. In other words, if there is an increase in the number of customers, we will likely increase the usage of the solution.

UiPath's technical support is good. Even in emails, they are responsive and helpful. They also have a very good library of troubleshooting documents and other resources. Overall, I am happy with the solution's technical support.

I have experience with Automation Anywhere. Also, UiPath Process Mining is a pretty good tool since they have documents which provide its users with a clear understanding. On top of that, they also provide videos. So it's quite easy for an end-user to understand.

Although the solution's initial setup is not overly complex, UiPath could work to make it more understandable for a layperson. They could improve the setup process by creating a more structured flow.

The time needed for deployment varies, depending on factors like the use of cloud services. With a Plug and Play model, UiPath provides cloud services and a link, after which we can begin using the solution. However, deploying on a private cloud may take longer, up to three to five days or even two weeks, depending on the hardware readiness.

In terms of the maintenance of the solution, let's consider a scenario where we need to initiate a programming project to develop a bot that can help in invoice transactions. Typically, this process can take anywhere from three to five weeks and requires at least one project manager who oversees a team member who understands UiPath and business processes. To successfully develop the bot, we need to first analyze and understand the relevant business processes. Once we have derived the business processes, we need to document the solution, including creating a PDD. This document outlines the process and how it will appear once automated. Apart from that, you should have a good set of developers who understand UiPath.

In our organization, we implemented the solution with the help of integrators.

The ROI from using UiPath Document Understanding is really good, and we currently see a return on investment of around 40% to 50%.

For the licensing part, in our company, we initially started off with a monthly plan, but yearly plans are also there. Cost-wise, UiPath  Document Understanding is good since it provides flexibility in its pricing. So, smaller firms, especially MSMEs, can opt for monthly or yearly plans based on their budget. As an end user, I wish they reduce the price of their pro version to 300 dollars since UiPath Pro starts somewhere around 400 dollars per month. UiPath's competitor, Automation Anywhere, offers a plan for 99 dollars per month.

I can definitely recommend the solution to other users. Overall, I rate this product somewhere around eight out of ten.