UiPath Document Understanding Reviews

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 old process involved manual data extraction from a large volume of documents with varying types and templates. This labor-intensive task required a significant workforce. We implemented UiPath Document Understanding to automate this process and eliminate the need for hand-coding solutions.

UiPath Document Understanding helps prepare data for machine learning by labeling documents used to train the models that will ultimately automate document processing tasks. We also use it to extract information from various identity documents like passports and ID cards, financial statements, credit card statements, and bank statements, and it can even process bank transaction data.

The documents we process using Document Understanding include tables and sometimes handwriting.

Around 70 percent of our documents are completely processed using Document Understanding.

The UiPath OCR works perfectly to extract handwriting, signatures, and multiple formats.

AI and machine learning prove valuable in training Document Understanding systems by analyzing data and identifying patterns, improving the system's ability to extract information from new documents.

AI streamlines Document Understanding by eliminating the need for manual coding. Users input documents into the AI, which then automatically classifies and extracts relevant information from each file. This saves staff over 20 hours a week.

UiPath Document Understanding integrates well with other systems.

For any newly implemented processes, human review will be necessary every day until Document Understanding is fully trained. The validation takes one minute per document.

The implementation of UiPath Document Understanding has saved us 50 percent of the time spent previously processing documents.

UiPath Document Understanding significantly reduces human error in processing documents, with complete accuracy achievable for standardized formats. However, its effectiveness in handling handwritten data varies depending on complexity.

UiPath Document Understanding helps save 20 percent of staff time to work on other tasks.                                          

The most valuable feature of UiPath Document Understanding is the AI Center.

UiPath Document Understanding, while effective for its own platform, could be even more valuable if it integrated with other commonly used platforms, allowing for a more universal approach to document processing.

I have been using UiPath Document Understanding for three years.

UiPath Document Understanding is stable.

UiPath Document Understanding is scalable.

The technical support is easy to access through the UiPath portal.

Positive

I've used IQ Bot from Automation Anywhere, Microsoft Intelligent Document Processing, and UiPath Document Understanding. IQ Bot and Document Understanding offer similar functionality, but only Microsoft's solution works across different platforms. We mainly use UiPath Document Understanding because it aligns with our client's preferred platform.

The deployment was straightforward. One person is enough for the deployment.

UiPath has a higher upfront cost, but its Document Understanding feature is not a significant additional expense compared to the overall platform.

I would rate UiPath Document Understanding nine out of ten.

We have six people that use UiPath Document Understanding.

I recommend UiPath Document Understanding to others.

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.

UiPath Document Understanding is a key tool we use to automate document processing for our clients, including tasks like invoice and sales order processing. We can create multiple workflows for different clients and even use it internally. To handle even more complex documents, we've also built custom models for specific data extraction needs.

UiPath Document Understanding helps our clients streamline data entry by accurately and consistently extracting information from both paper and digital documents. This extracted data can then be seamlessly integrated into their existing ERP or finance systems, eliminating the need for manual data input.

Document Understanding automates the processing of our invoices and sales orders, which are our most common tasks due to their semi-structured format. These documents share a typical organization with common fields, though we also handle custom documents like certificates and licenses across various states.

Document Understanding helps us process thousands of documents each day.

Thousands of documents are processed completely by Document Understanding each month.

Machine learning is the core of Document Understanding, where trained models extract data from documents. For simple forms, basic tools suffice. But in most cases, Document Understanding's built-in machine learning tackles complex documents. Generative AI features are new and basic for now but hold promise for the future.

The human validation required for Document Understanding outputs depends on the use case. We aim to get above 80 percent without human intervention. For some use cases, we're well above 90 percent. In just one minute, the human validation process can be completed for the small percentage of tasks, typically between 10 and 20 percent, that necessitate it.

While average handle time varied greatly before automation ranging from eight to ten minutes or even longer, data entry for sales orders with hundreds of line items was especially slow, taking up to 30 minutes per order. Automating the process with API integration significantly reduced this time to just one to two minutes.

Document Understanding helps significantly reduce human error, especially in crucial tasks like sales order entry for manufacturing clients. Mistyped entries can lead to incorrect production, rework, and unhappy customers. While the error reduction varies, estimates range from 18-20 percent to potentially as high as 40 percent in some cases.

Document Understanding significantly reduces manual data entry, freeing up staff time. For instance, one client eliminated a data entry role entirely, allowing that employee to focus on higher-value tasks. This is a consistent benefit – whenever we implement Document Understanding, the staff previously responsible for data entry can be redeployed to different teams, roles, or more strategic work.

UiPath's Document Understanding significantly reduces the effort needed to train a machine-learning model for our documents. Their pre-built models and tools for customizing them minimize the need for manual tasks like creating bounding boxes and training on uncommon examples. This allows us to achieve high accuracy and certainty in data extraction with minimal human intervention.

The rising annual licensing cost of UiPath's Document Understanding product is a major turnoff for users. This constant price fluctuation incentivizes companies to switch to competing solutions, potentially hurting UiPath's market competitiveness.

The technical support has significant room for improvement.

I have been using UiPath Document Understanding for three and a half years.

The technical support is bad.

Negative

Document understanding projects deliver a significant return on investment in two ways. First, by automating data entry tasks, they free up customer service agents to focus on client interaction, improving service quality. Second, this automation can eliminate the need for offshore data entry teams, potentially bringing those jobs back onshore and saving tens of thousands on overall costs.

UiPath's pricing model is complex and based on AI units, which are consumed during model training and use. This makes it difficult to predict costs upfront, unlike a simpler pay-as-you-go model offered by Microsoft. With UiPath, you purchase a bundle of AI units, and even if you don't use them all, you're still charged for the entire bundle. This can be less cost-effective compared to Microsoft's approach where you only pay for what you use.

I would rate UiPath Document Understanding nine out of ten.

We use the solution in pharmacy health care, and our role is to enable doctors so that they can set up a personalized clinic - everything a patient requires. We get information in the form of a document and we can break it down into sheets and JSON files, for example. We use a UiPath documentation tool. 

Document understanding has helped us increase our efficiency and accuracy. We don't have to manually check data again and again. 

After the first month, we discussed how the solution was benefiting us, and we decided to continue with it.

It helps with data and consistency. It helps us receive information and convert it so the systems we have in place can understand a problem and generate responses accordingly.

We've used it in one process where we received a patient's pharmaceutical documents from other sources that come in different formats. We receive the formats, convert the information into a standard format, and then process the information to provide information for insurance forms. 

The average document size is not very large, likely 80-100 MBs. However, the total count of the patients is somewhere around 10,000. 

We have 50% to 60% of clients directly onboarded via an insurance form. Therefore, we are provided with the exact form we need and can run a complete automation on that. There's no type of manual involvement there. 

The format for setup is a great thing. Earlier, the tool that we used was pretty manual. In this case, it's a bit easier for our developers.

The solution can detect signatures to let us know that there's a signature there. You can construct tables or any other format of data based on pure text information. 

They are employing an ML model for detection conversations. They are also trying to deploy a written-to-text conversion. They are convinced AMR systems will replace other manual work.

The main value of AI for us is to convert data formats from one type to another. We receive data stating two or more complex data points mixed later, for example, the license number and the serial date of operation for the doctors or the patient code; sometimes these things are mixed together. We want all those to be arranged. Their AI does the job very well.

We can integrate document understanding with other systems and applications. With it, we can simply write down a code to communicate with the ML model, for example, how to convert the data and which datasets to look for precisely in the documentation. We were able to communicate easily what would be the format of the PDF documents that we would be providing. The integration part and communication was the best aspect of the entire application.

We have Veracode integrated with it. We will do a manual check if we get a security flag where the data may be inconsistent. We usually get an alert like this once or twice a week. The human validation process usually takes an hour since we have to manually check the parameters. Before implementing the solution, the handling time before automating the process was pretty much the same. With this, we may have reduced it by half an hour. Also, previously, we'd have more manual interventions happening, maybe three or four times a day; however, now, with everything automated, that only happens one or two times a week. It's reduced the frequency by about half an hour on average. 

Using the solution has freed up staff time. We've reduced our team size in regards to quality checking. We've reduced the amount of work by 40 to 50 hours a week. 

UiPath's documentation tool is not great with converting handwriting to text, so we only used it for the conversion of insurance documents into other formats.

They could modulate the ML model in the future. When it comes to working with data and processing reports, we have to target the datasets we had earlier targeted and redefine the parameters, which takes a lot of time. If the ML model, at the time it is analyzing the data, could in itself provide the insights we will need for future reporting, that would be great. There needs to be better real-time analytics since we aren't getting the data for reporting until we go and seek it out. 

If there were more integrations with Veracode or the AWS server, so we don't have to completely transfer our data and keep data on our servers, that might increase security. 

I've used the solution for a year or so. 

The solution is good. It's very stable.

It's not deployed across multiple departments. We have this deployed across one department. We have two developers working with the stream of data. 

For small to medium firms, the solution scales well. However, if you are going for a global scale, you should develop your own models and not rely on outside models. 

Support is good. That said, sometimes they have problems understanding what we want to do with the data since we cannot provide the data in its raw format. We have to decrypt it. This makes it a bit harder. That's why we would like integration on our servers instead of theirs. 

Positive

We did use a different solution previously. We switched since the number of tags we were getting was pretty high. We had to do more manual interventions a lot more often. The parameters we used to communicate were also manual. It required setting up a decision tree in the whole of the document. A lot of the time, we would not know what the document type would look like. It required the developers to look at the documents, create a decision tree, and go from there. With UiPath, we don't need to do all that manual upfront work. 

I was a project manager, not a developer, deploying the solution. My understanding is the process was moderate. It was eight too easy or too complex. 

The implementation involved discussing the work with the insurance firm. We explained we were moving from one system to another. Once we had that conversation, we received the documentation in the format we wanted. 

Then, we looked at how we encrypted our data before sending it to UiPath servers. We did have a lot of compliance issues and had to be careful. 

Once we came to the physical implementation, that was easy. Managing other stakeholders and their clients was the hardest part.

We had three developers from our team working on the deployment. It took us about 10 to 11 days to deploy.

Twice a week, maintenance is needed whenever there's a flag raised when data points do not match. We can simply ignore the solution and change the data file, or we can go in and see what is wrong with the file type and adjust it so that it doesn't happen again. 

We did not use any outside assistance beyond the help of UiPath's support team. 

The ROI is pretty good. We did not do any calculation for ROI. However, the accuracy percentage and time reduction which we noted, have made us happy.

We originally noticed a time to value for UiPath within 10 to 12 days.

The pricing is pretty fair. It is quote-based. Overall, it's fair. If you are a small firm looking to scale up, it is good. Enterprises should create their own ML model instead of relying on some outside product.

We looked at a few other options and did a few POCs. UiPath is able to sense and analyze a document and create a hierarchy for you. You can also create a manual code if you want something done differently. The only issue is we have to upload the information to UiPath servers, which may be a security issue. 

We're end-users, not integrators. 

It's a good idea to have a call with the support team and managers and do a review to understand the solution to see if the product would work with your type of data. It's important to test it out, ideally using your own data. 

I'd rate the solution nine out of ten. 

We use UiPath to automate invoice generation in our manufacturing process. One large project I worked on was for electricity bill payments. This project involved document processing, and I gained some experience with document processing and process mining. From there, we started using UiPath Document Understanding for the bulk of invoices we were receiving. We then had to put those invoices into the document processing model because they had a uniform structure, but there were also some handwritten notes and values in different places. So, we had to opt for document processing. Right now, we are developing a proof of concept for one of our government websites. This involves tender documents. We download and process the tender documents, extracting data such as the quotation, validity period, and other details, and putting it into a database.

We are processing documents in the hundreds using UiPath Document Understanding.

The standard document contains header information such as the company name, quoted value, quotation price, and expiration date. There are also tabular details regarding the items to be delivered. The tabular structure has headers, but checkboxes are not present in this particular use case. In addition to the header and tabular details, the document may also contain handwritten notes.

We have deployed UiPath Document Understanding on-premises but given the choice we always recommend the cloud because it includes more features.

UiPath Document Understanding eliminated the manual process of extracting data from 50 different websites each day.

Our customers' documents vary by website, but their structure is fairly uniform. As a result, we were able to process approximately 70-75 percent of the documents automatically with very good accuracy.

UiPath Document Understanding can identify and export signatures and handwriting from clear documents, using machine learning.

AI and machine learning feed the unprocessed raw data to some of the custom machine learning models. I have been working as a backend developer, so I have experience with machine learning as well. I tried with some of my own models, and it was clear that the customization of these models to our specific data requirements is very impressive.

UiPath Document Understanding's ability to integrate with all the systems and applications in our environment depends on the specific requirements of our use case. If it is generating a good return on investment, then I will consider using it for document processing. However, if my requirements can be met without using document processing, I will definitely choose to use simple OCR techniques instead. Traditional OCR engines can extract data from documents and place it into databases, where it can then be manipulated. However, this approach can be time-consuming and error-prone.

UiPath Document Understanding has helped our organization improve. It is especially useful when there is ambiguity in documents, which is a common real-life scenario. Inbuilt OCR engines are often unable to perform data inspection accurately in such cases. Whenever we have a large volume of documents to process and need to ensure high accuracy, UiPath Document Understanding is our first choice. One of the key benefits of UiPath Document Understanding is that it provides a dedicated model for document processing. This means that developers do not need to worry about other details and can focus solely on the task at hand. Additionally, UiPath Document Understanding integrates seamlessly with machine learning and AI models, which further enhances its capabilities.

Some of our customers were reluctant to switch over, and for a long time, they did everything manually, so their documentation was very outdated. As a result, we were required to manually validate 30 percent of the documents. The time to manually validate depends on each document. If two or three fields are mismatched, it does not take much time to correct them. However, if the entire document is showing errors, that will add time to the manual validation process.

It reduces the risk of human error and the time we spend processing documentation, freeing up our staff to work on other projects.

OCR technology is undoubtedly the most valuable feature and the feasibility of integrating data processes with AI and machine learning models is fascinating.

The identification and extraction of signatures is the most difficult part of the process, and there is room for improvement.

The machine learning model needs improvement, as we receive more and more unstructured documents from clients that require a lot of manual validation.

I have been using UiPath Document Understanding for three years.

UiPath Document Understanding is stable.

UiPath Document Understanding is scalable.

I've seen many clients refuse to purchase the licensing when they see the pricing. They're quite impressed with the results, as the bot does so much work in less time with accuracy. However, when it comes to pricing, I've seen clients refuse to spend that much on the licensing cost for UiPath Document Understanding.

On a scale of one to ten with ten being the most expensive, I rate UiPath Document Understanding an eight on cost.

I would rate UiPath Document Understanding eight out of ten.

I definitely recommend UiPath Document Understanding to anyone who is trying to do any kind of document automation. In fact, I have some friends who are working on an RPA project using UiPath, and we have been discussing it. I recommended Document Understanding when it first came out, and I think they have been using it for the project.

I use UiPath Document Understanding to extract data from scanned images using OCR technology. For example, when we have invoices, we can extract data from them by creating a model for that particular template using OCR technology, artificial intelligence, and machine learning. Every invoice has its own template, so we can create a template model and implement it in UiPath to run a bot for the data extraction process. After extracting the data, we can store it in an Excel file or database, whichever we prefer.

We deploy UiPath Document Understanding in the cloud and then integrate it with our on-premises architecture using a single key.

UiPath can automate any repetitive task, such as data entry, data extraction, file downloading, and file uploading, in any financial services, banking, or health insurance sector. The document formats include tables and checkboxes.

It can extract handwriting and signatures as long as they are legible.

Machine learning capabilities can be used to retrain prebuilt models for use with other templates.

It has helped improve our organization by reducing human tasks and errors.

Whenever data is extracted from a document using UiPath Document Understanding, we receive a confidence level rating. If the confidence level is low, we send the extracted information to the Action Center for human validation.

UiPath Document Understanding does the work of three full-time employees.

Using UiPath Document Understanding for documents without business or application exceptions reduces human error by 100 percent.

UiPath Document Understanding's image file extraction feature is the best in any OCR solution.

The signature comparison feature of UiPath Document Understanding could be improved.

To my understanding, we can only integrate UiPath Document Understanding with UiPath. I would like the ability to integrate with other solutions.

I have been using UiPath Document Understanding for two years.

UiPath Document Understanding is stable. 

We previously used an Excel automation tool but switched to UiPath Document Understanding because it is a better solution for repetitive tasks.

The initial setup is straightforward. The deployment was completed by two people including myself.

The implementation was completed in-house.

We received a 60-day free trial before having to purchase a license to continue using UiPath Document Understanding.

I would rate UiPath Document Understanding nine out of ten.

Data extraction accuracy depends on the document's quality and format. The maximum percentage of accurate data we can extract using UiPath Document Understanding is 90 percent.

We started to see the value right after implementing UiPath Document Understanding.

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.