UiPath IXP Reviews

We have processes for purchase orders. We need to analyze the content of these files and some invoices. Based on that, we are able to perform qualifications and post them to the CRM system. Overall, we call this our invoice control process.

We wanted to optimize the performance, meaning the time the process takes, and the quality. We had some problems with the quality of transferring the data because people would make mistakes. If they were doing 80 documents per day, there was a high possibility that they would forget to look for some information or they would copy and paste the wrong fields.

The main benefit for us has definitely been a faster process. We have sped up the process of analyzing the data. A second one is the improvement in the quality of our implementation.

In our organization, we are now at 30 percent of our documents being completely processed automatically. And in terms of human validation required for output from Document Understanding, we need it for 15 percent of the cases. We have decreased the time needed for such processing by 70 percent. And regarding human error, we have seen a decrease of about 60 percent.

One of the most valuable features is the intelligent recognition of the fields. The algorithm is able to recognize them based on the pattern. Also, the machine learning model enables you to use predefined solutions. The machine-learning capabilities of the solution are very cool. I really like that part, and I hope it will be developed even more in the future. I'm really excited to see how it will develop.

Integrating Document Understanding with other systems and applications is very easy if you already have some background. It just requires some mature developers to do so, and we are just about at that stage. It's very user-friendly, but the documentation could be a little more detailed. Besides that, it is fine.

With handwriting, we had a problem. It wasn't able to extract it because we have handwritten documents in Polish, and that language is not supported at this time.

Also, the documentation should be more clear, or better training should be provided.

We have been working with UiPath Document Understanding for about eight months.

Scalability is very easy to manage. It's very natural. The only thing that changes is the number of processes and the number of licenses. The more money we save with it, the bigger we will scale it.

We are using it across four departments.

I really like their customer support. They are very responsive.

We had ABBYY. The advantage we see with UiPath is that it's easier to implement things with RPA. Some of our teams are still using ABBYY, but our team has declined to use it anymore for automation.

I'm the system owner and architect, so it was on me to set it up. The initial setup was very complex in the sense that we had to play with the firewalls and other things to make it work.

It included the entire cloud, not only Document Understanding. It was very tricky to do it the correct way. We had to do a lift-and-shift. We updated the on-prem environment to the latest possible version and then copied the entire base to the cloud. Later, we upgraded each process, and, once the process was upgraded and ready to work in the cloud, we moved it to the target tenant.

At this moment, it does not require any maintenance.

We did it ourselves. We had a team of 20 people, but that's because we have a lot of processes.

We will need to have the solution for at least one year to have a clear view of ROI. It's the same for time-to-value with the solution.

It's expensive, but you can reduce the price per license by getting more licenses. Overall, the pricing is okay.

One area for improvement would be a different licensing model. Right now, we have to assign a license to allow a user to do validation. We think that standard access to Orchestrator should allow a user to validate.

Definitely talk first with a UiPath representative to get someone who will take care of you and the implementation. Do not waste your time reading through the documentation because it's very messy.

I used UiPath Document Understanding to create a report by reading invoices and V9 tax documents. I employed specific taxonomies to facilitate document analysis and populate my database with extracted information. The primary objective was to accurately identify and store relevant data from these documents within the database.

The idea arose from the observation that many companies lack a centralized repository for essential documents, such as invoices. In response, I created a website where a robot automatically uploads and interprets these invoices, presenting key details about each document on the website.

By using taxonomies, I could interpret the documents and make them easily accessible through a website database. This way, website visitors could find all the documents themselves, eliminating the need for them to repeatedly ask employees for specific documents like invoices or V9 tax forms. UiPath's visual processes further simplified this by allowing me to implement and manage the system effortlessly.

I used UiPath Document Understanding to process invoices and V9 tax documents.

All the documents processed were in PDF format.

The documents contain tables, boxes, check marks, and handwritten text.

All the documents were processed 100 percent automatically.

UiPath Document Understanding was able to handle the handwriting and signatures with no issues.

UiPath Document Understanding helped make the automation process easier for me.

The manual validation of each document took one second.

Using UiPath Document Understanding, all the documents were processed in just a minute. While I didn't have many documents, it still surprised me how quickly it worked. Manually, it would have taken me about five to ten minutes.

UiPath Document Understanding has saved me time to work on other projects in parallel.

The highly visual and user-friendly interface was a standout feature. Selecting taxonomies was as simple as clicking the corresponding areas on the invoices, enhancing the visual nature of the interaction.

UiPath Document Understanding requires more database connectors. I encountered difficulty connecting to Workbench from MySQL, necessitating a workaround.

I have been using UiPath Document Understanding for three months.

I did not face any stability issues with UiPath Document Understanding.

The scalability of UiPath Document Understanding is fine.

The initial deployment was straightforward. The deployment took a few minutes to complete and I did it myself.

Originally, I spent some time building the automation robot. However, once I completed it, I realized the value of UiPath Document Understanding.

I used the community version, so there was no fee.

I would rate UiPath Document Understanding nine out of ten.

I was the only one using the solution in our organization.

I recommend evaluating both the free and paid versions of UiPath Document Understanding. 

We use UiPath Document Understanding to process purchase orders and order confirmations.

We implemented UiPath Document Understanding because we wanted a more efficient way to process the documents we were receiving.

We process purchase orders and order confirmations in PDF format. The documents we process are tables and standard data.

We process anywhere from 150 to 200 documents per day with UiPath Document Understanding. We process between 60 to 70 percent of the documents completely with UiPath Document Understanding.

We do not use Document Understanding for signatures or handwriting, but we do use it for various document formats, which it handles moderately well.

The AI and machine learning with UiPath do the job moderately well.

We leveraged API calls to seamlessly integrate UiPath Document Understanding with other systems and applications within our environment.

UiPath Document Understanding has helped save us a lot of time.

We required human validation between 30 to 40 percent of the time.

Prior to implementing UiPath Document Understanding, it took us one hour to process each document. With the implementation, processing time has been reduced to one minute, saving us 59 minutes per document.

UiPath Document Understanding has helped reduce human error.

UiPath Document Understanding has helped save our people time to focus on other projects. This time savings is equivalent to the productive output of a full-time employee. 

I like the clear and organized way in which UiPath has structured the labeling process, as well as the user-friendly development environment.

Several areas require improvement in UiPath. The licensing model poses a significant challenge due to the fee charged for posting a model, which impedes the development of productivity-enhancing models. Additionally, UiPath's pricing is substantially higher than that of its competitors, approximately three to four times higher.

UiPath's AI quality needs substantial improvement. Several issues have persisted for at least two years, such as the inability to handle breaks in tables. When data in a table extends from the first page to the second, UiPath fails to process it effectively. Additionally, UiPath struggles to handle multiple items.

I have been using UiPath Document Understanding for over two years.

I would rate the stability of UiPath Document Understandings six out of ten, because of the AI issues.

I would rate the scalability of UiPath Document Understanding eight out of ten.

The technical support is good.

Positive

The initial deployment is straightforward. We implemented a hybrid model of on-premises and cloud deployment throughout the organization. The robots are physically located on-premises, but their operations are managed and controlled through a cloud-based platform. One person was required for the deployment.

We have seen a return on investment with UiPath Document Understanding.

UiPath Document Understanding compared to the competitors is high. I would rate the price nine out of ten with ten being the most expensive.

I would rate UiPath Document Understanding seven out of ten.

We have seen time to value with UiPath Document Understanding.

UiPath Document Understanding requires constant maintenance because of the AI issues. One person can handle the maintenance.

I recommend thoroughly testing UiPath Document Understanding to verify that the organization is genuinely deriving value from the tool and to assess whether the AI model can effectively handle the capabilities advertised by UiPath.

I use the solution in different ways. There are different ways to deal with the documents and extract data using OCRs, et cetera. I focus on document understanding with machine learning capabilities baked into it. 

I have worked on different types of documents. Both structured and unstructured documents as well as purchase orders, invoices, and time sheets as well. Depending on the type of the document or form, the solution would be different. If we have a structured document to retrieve the known information, then that would be easy. We would just use regular methods and simple extraction methods to get the data. However, if the documents are unstructured and the formats of those documents are different, that's where we would be using understanding and machine learning capabilities.

I've used it in healthcare, finance, and investments.

We're dealing with multiple vendors right now. When we deal with multiple vendors, each vendor has different structures for documents, and some of them provide data within papers while some of them provide just data as paragraphs. So for each of those types of documents, we have to extend the data based on the need.

How much the process is automated depends on the use case. It depends on the scenarios of the different types of formats of users. Sometimes there are different functionalities that we have to use within UiPath. If we're using Action Center, we probably would be able to automate almost all documents and that's where we would need users' input to validate the right information. In any case, we would be able to automate the majority of the documents since the bot would be able to expand the data. In such cases, the process might be longer. That's where we have to spend more time. However, if the documents are structured, it would be very easy for us to identify the data in those documents and then build the workflows.

The ability of UiPath to handle variant document formats, including handwriting is decent. Extracting the data is fine. However, it depends on what solution you implement and how much time you are ready to spend to implement that solution. If we have a plan to involve the Action Center within the solution, then that's where we would need a few inputs from the users to make sure that the automation is working fine, and that's where you would be able to achieve the majority of the success rate. However, if it is something that we just want to automate and we don't want to involve humans in it, then that's where we might result in a few exceptions as the data might not be right. That's where we would face some challenges. 

The machine learning capabilities have been quite fine. We've been able to digitize and classify documents and use them in our processes. That said, when compared to AI fabric, that's where we need to spend more time creating our own packages for it and then deploying those packages into the workflows. We need to make sure that we have a handle on all the documents.

I have found that 70% to 80% human validation is needed if we are trying to deal with sensitive data or if we are trying to deal with some confidential data. In those cases, we need to make sure that all the data is right. So as long as the document is structured and is well defined, and well-formatted, we might leave it 100% to automation. If any of these details are confidential or if any of these details require evaluation, then we will need user interaction. 

The validation process can be pretty quick. A code document doesn't take much time. It depends on the data. In any use case, I need to extract more than ten or 12 fields. If we're dealing with that number of fields, I'd estimate we need between two to 22 minutes.

The Average Handle Time, the AHT, depends on the cases. If there's no human involvement, then it would definitely take less time. If there's human involvement, the product could at least reduce the effort. The human involvement may drop from 20 to 30 steps down to one and they are just needed for validation. That scenario shows UiPath as a great time saver. Initially, we used to take 15 to 20 minutes to work on a document. Now, with automation, it might only take two to three minutes. It saves 65% to 75% in terms of time. 

There's still a chance of human error or tasks taking a few minutes as users would need to input some data into the document in the Action Center. That said, there is definitely a reduction and less of a chance of human errors over there. 

It's helped us free up time for other staff projects. That's the intention of implementing automation. Users can reduce their time on tedious tasks and focus on more important business needs. 

Document understanding works fine, however, it depends on what information you are providing it with. If the data is right, the data is good, however, in cases where the data is not right, it gets a bit difficult.

They could work on the digitizing and classification of documents. That would play a major role in document understanding since that's where we need to make sure that bots are able to extract data from multiple formats or multiple structures of the documents. The better they get at data extraction, the better we can automate. 

I've used the solution for two and a half years. 

I have worked on different use cases. There was one use case where I just worked on a similar type of document that had data entries for more than 300 to 400 users. I have worked with more than 400 to 500 documents of different formats and different sources. That's where I had to use machine learning packages. Right now, we are working on documents, with set formats and different structures, however, the volume of the documents is about 400 to 500.

They have a good technical team that supports the needs of the developers. 

Positive

It could actually take one year if you consider the effort which we had put into building that solution as well. We have a few developers working on it. We wouldn't see a return on investment within eight to ten months as we would just be starting with building the processes. 

We have witnessed some ROI. For example, it reduces review work from 60% to 70%. That's where the full-time employees would definitely save their time and can then focus on much more important business needs, which could help them get more projects or increase revenue as well. That's where you would see the most ROI. 

It definitely reduces the hours of work. The bots have the potential to also cover offline hours. 

While it depends on the use cases, the document understanding is good. I'd rate it eight out of ten.

The product is very, very nice. The tool relieves the data entry team from manipulating data. It shortens the time of extracting data by 70 to 80%.

UiPath should localize the built-in features for supporting Arabic scripts so we do not rely on third-party tools. It will take some investment. Sometimes, when we use third-party tools, we need to collect multiple samples of the same document in Arabic. It creates some errors in understanding. 

Sometimes, we need to do multiple formats for the same document. It happens due to the nature of the Arabic language. For the same document in English, we only supply one template. The output of handwritten documents in Arabic is very poor, regardless of the solution. For computer-printed documents, we need to tune the system.

I am using the solution currently.

The product is stable.

The tool is scalable.

We were using our own intelligent business processing solution. It is more convenient to use because it's our own product, and we rely on Amazon Web Services. It provides big advantages for Arabic scripts. It is more understandable.

The product is easy to implement to an extent. If we are familiar with the input, processing, output, and how to declare variables, it's easy. If we are new to UiPath, it would be somewhat difficult to understand. UiPath Academy and certifications are very important.

The difficulties in implementation depend on the environments, document sources, document types, and customer understanding. Sometimes, customers think the product can pass anything without configuration, which is not true in some cases. We have to be very clear with the customer about the type of documents and layout they can expect. Two working days are sufficient for configuring, extracting, and linking the process. One person is enough to deploy the solution.

The pricing depends on the context of the project. UiPath is a pricey tool for small customers. If the customer agrees on the cloud product, we discuss the cost accordingly. In some cases, even though the customer sees value in the product, return on investment, productivity, and enhancements of their processes, they decide not to choose UiPath due to budget constraints. They choose a different vendor.

Sometimes, the customer is small, but we could see the potential for using the tool because they might have multiple processes. The price we offer is based on the context and the size of the opportunity we get for references. Document Understanding has helped us automate processes like contract management, reconciliation of invoices against purchase orders, claims management, and HR processes.

We process 100,000 to 500,000 documents in a year. We also had a project for Dubai Customs to reconcile the customs clearance, which involved 500,000 to 1,000,000 documents. These documents contain tables, bar codes, dates, and checkboxes. Tables might span over multiple pages, and we must capture it completely. It becomes challenging sometimes. If there is an invoice of three pages, everything must be captured, but sometimes the values are inaccurate.

Around 70% of our organization’s documents are completely processed automatically. We haven't used the product for signatures. Customers often see from a productivity point of view. If their employees process ten documents an hour, and the tool processes 50 documents an hour, using the tool is an advantage for them.

The tool does not work for integrations. It extracts data. When we extract data successfully, we rely on other business tools for integration. Document Understanding serves as a first milestone in the process. The tool at the second milestone would pick up the extracted data and post it according to the processes or applications used.

We need human validation of Document Understanding outputs three out of ten times. It does not take more than 20 to 30 seconds per document. An employee would take ten documents per minute. RPA could handle 30 to 40 documents per minute. Document Understanding has helped us reduce human errors by 70% to 80%.

Document Understanding has helped free up staff’s time for other projects. From a development point of view, it has freed up 70% of staff's time. From an employee's point of view, it has freed up 80 to 90% of their time. We saw the value of the product after a couple of projects, especially when we implemented it and saw the value for ourselves. As a customer, vendor, or implementer, when we see the execution, we see the value.

If someone wants to take advantage of this technology, they must think multiple times about different scenarios. They must centralize the capture or the source of data. They can use any product that is easy to use and configure and link it to the processes. They must sync the templates and the configuration multiple times and link it to the automation strategy.

Overall, I rate the solution a five 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.

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.

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.