UiPath Document Understanding Reviews

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. 

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 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.

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.

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.

I work for an electronics company that deals with a lot of tedious tasks on a daily basis, such as processing PDFs from different vendors in different formats. Initially, we used a tool to extract this information for later processing. However, last year, we implemented UiPath Document Understanding with a self-learning model so that it could learn to identify all the fields even when the format changed.

We use UiPath Document Understanding to process purchase orders and invoices that are in PDF format.

We process PDFs in many languages, and UiPath Document Understanding can extract data from thousands of PDFs for our partners with high accuracy.

The AI and machine learning model has helped to solve many of the inaccuracies in our PDF data extraction, and it will continue to improve.

UiPath Document Understanding has helped reduce the amount of manual intervention and helped scale up the number of documents going through the process with over 600 partners in production.

Out of 200 documents processed each day, 50 undergo human validation. In most cases, manual validation takes under two minutes to review two fields in a document. More complex cases with errors in multiple line items may take five minutes to validate, but we prioritize these cases and train the model to improve its accuracy in the future.

UiPath Document Understanding helps reduce 40 percent of human error. Although we do encounter errors with the solution when the PDF is not clear or when it sometimes swaps the day and year on documents, overall the solution has helped correct many human errors.

Once we implemented the right methods we started to see value in Document Understanding immediately.

UiPath Document Understanding is user-friendly, with an easy-to-use self-trained model, and the OCR it provides does a good job even with scanned PDFs.

Every PDF contains simple fields, such as header fields, and line fields that are three to five lines long. Sometimes, a line field contains multiple fields, like a table within a table. Document Understanding cannot extract this type of data. We are exploring other ways to obtain the data, such as using an embedded table feature. We have discussed with UiPath that an embedded table feature would be beneficial.

Existing models have room for improvement. Sometimes, after we train a model, we still don't get the expected results.

The technical support has room for improvement.

I have been using UiPath Document Understanding for one year.

UiPath Document Understanding is stable but we have had some issues in the last few months.

We currently have a few hundred partners and would like to scale up to a few thousand, but the manual intervention required to use Document Understanding at our current results level would prevent us from scaling up until better training models are available to reduce the need for manual intervention.

Technical support does not always provide a proper solution to our problems. Instead of providing an actual solution to our current enterprise system, they suggest that we upgrade the solution or move to the cloud.

Neutral

In my previous organization, I used a tool called Conexiom. UiPath Document Understanding is easier to use and train the models with. We have people in our organization who are not trained and are still able to use Document Understanding.

The initial setup was straightforward and it was completed in one day. 

The price is on the high end.

I would rate UiPath Document Understanding seven out of ten.

We do not include handwritten PDFs in our process because we conducted a proof of concept and the results were not accurate. I believe this is because we did not use the required machine-learning model for handwritten PDFs.

We have a team of ten people who use UiPath Document Understanding.

Maintenance is required to validate the data.

I would recommend UiPath Document Understanding to anybody considering it. 

I use UiPath Document Understanding to extract data from digital invoices.

We implemented UiPath Document Understanding to eliminate the need for manual invoice data extraction.

We process 50 invoices per day for a logistics organization. We receive the invoices from the user, and a bot reads them one at a time, extracts the details and stores them in an Excel file. If the extraction does not match the confidence score, we send the invoice to the Action Center for manual validation. Once the data is extracted, the bot enters it into SAP and validates it against the system information.

We used machine learning to train a model to process the different formats we were receiving.

Seventy percent of the documents we process are completed automatically without any manual intervention.

The AI and machine learning capabilities of UiPath Document Understanding are good.

Once trained on a large dataset, AI can save us significant time by performing tasks that are beyond our capabilities.

Human validation is required for about 25 percent of the documents we process. For smaller documents, it takes a human a few minutes to validate the information.

Our average handle time after implementing UiPath Document Understanding is five minutes.

UiPath Document Understanding has helped reduce human error.

UiPath Document Understanding has helped free up our staff time.

We can see results as early as two months, and definitely by six months.

UiPath Document Understanding offers multiple types of extractors, which we can use to extract information from documents in a variety of ways. Machine learning is a particularly useful method, as it allows us to train custom models to meet our specific needs.

UiPath Document Understanding has difficulty identifying handwritten documents, and there is room for improvement.

Some of the invoices we process are stamped, and the AI has difficulty understanding them because the stamp format varies between a square and a circle. Resolving this issue will allow us to process more complex documents using UiPath Document Understanding.

I have been using UiPath Document Understanding for one year.

UiPath Document Understanding is stable.

UiPath Document Understanding is scalable.

I would rate UiPath Document Understanding eight out of ten.

The solution was deployed in a single department for multiple users.

Maintenance is required for continuous training whenever an invoice format changes or a new vendor is added.

I recommend conducting a POC with the community to ensure that UiPath Document Understanding meets the organization's requirements before fully implementing it.

I have been using it for processing invoices, receipts, and multiple forms of documents. We have structured and unstructured documents. Structured documents include forms, and unstructured documents include written documents and receipts. For identifying the type of document and for processing those documents, we use UiPath Document Understanding. It helps to identify the type, extract the information, and process the information.

We are able to process multiple documents and extract information from them. We can put that information into an Excel or a PowerPoint file. We can also put that on a website or in an application such as SAP. We can generate a PDF. We can do all this with UiPath Document Understanding. So far, I have only exported information into an Excel file. I have not integrated it with any other system.

UiPath Document Understanding can extract information from unstructured or handwritten documents. It can extract information from structured documents such as forms. It can process invoices. I have mostly tried it with structured documents, and I have not had any problems with it.

UiPath Document Understanding makes the work easy. It can classify a document and capture the information. It is able to process a document quicker than a human. Its AI and ML capabilities improve productivity and accuracy. If we have a lot of invoices of a certain pattern, we can feed that pattern. It can then process multiple documents of that pattern. This way machine learning helps us to easily process more documents.

We can now design or develop a process much faster. Previously, we had to do string manipulation to extract information from invoices, which required a lot of coding knowledge, but with UiPath Document Understanding, that is not required. It automatically identifies the type of document and extracts information. It saves time and improves accuracy. It is a low-code or no-code solution, and even someone who is not skilled in programming can design a process. From half an hour to one hour, process designing now takes 10 to 15 minutes. 

Human validation is required for certain business processes. The Present Validation Station activity enables you to review and confirm whether correct values are extracted or not.

UiPath Document Understanding reduces human errors. Humans can make mistakes while extracting information or while coding. To avoid all those problems, we are using UiPath Document Understanding. It helps us reduce human errors because no coding is involved. We are directly teaching the system how to identify a document. It automatically identifies a document and extracts information from it. Because the system does that by itself, we can reduce a lot of human error. There is a 40% to 50% reduction.

The machine learning (ML) extractor is valuable. It helps in extracting information from even unstructured documents. It can sometimes also extract information from a written document. Without much manual intervention, it is able to process the documents. This is a unique feature of UiPath Document Understanding.

There could be a feature similar to ChatGPT where when you ask about a document, you get an answer about what is there in that document. If possible, this kind of feature can be incorporated so that you do not need to open a file and take the value. It should directly detect that instead of opening and validating the document. Such a feature will speed up the process.

I have been using this solution for the past two or three months. I have been using it only for practicing. I am not using it for any industrial purpose.

UiPath is giving very good support. They also have some webinars, which are very helpful in developing our solution. I would rate their support a nine out of ten.

Positive

Before UiPath Document Understanding, I was using string manipulation and screen scraping methods for extracting values from invoices. It used to take more time to build a solution. 

It is deployed on-prem. Its deployment was straightforward. It took 10 to 15 minutes.

In terms of maintenance, bots require some maintenance. Bots can fail, so continued maintenance may be required.

I deployed it myself. I am using it on my own.

I have only been using its free version.

It is a very good solution. It improves productivity and accuracy. It reduces development time.

You can use UiPath Document Understanding even when you do not have programming knowledge. You just need to know the pattern of documents. It has ML capabilities, and the bot will automatically learn and start to identify and process documents. It is very easy.

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