UiPath IXP Reviews

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 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. 

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.

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

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

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

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

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

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

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

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

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

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

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

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

I have been using UiPath Document Understanding for six months.

UiPath Document Understanding is stable.

UiPath Document Understanding can be scaled for multiple invoices.

The deployment was straightforward and required a few days.

I would rate UiPath Document Understanding eight out of ten.

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

We have four members using the solution.

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

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

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

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

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

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

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

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

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

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

I have not experienced it crashing or lagging.

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

We have not contacted their technical support very much.

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

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

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

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

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

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

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

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

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

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

It has helped reduce human error.

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

The extraction and classification process should be simplified.

I have been using UiPath Document Understanding for six months.

UiPath Document Understanding is stable.

UiPath Document Understanding is scalable.

The technical support is good.

Positive

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

I implemented the UiPath Document Understanding myself.

I would rate UiPath Document Understanding eight out of ten.

Our organization also uses ABBYY.

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