UiPath Document Understanding Reviews

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.

UiPath can handle normal, structured documents like forms and editable PDFs, but the data cannot be extracted from some unstructured documents with normal instructions. Non-standard documents are the most challenging thing for us. For example, let's say you have a hard copy of a receipt you get from a store, and you want to keep a record of it. You need to extract specific types of data and store it in Excel.  Document Understanding can deal with these documents. You can configure it to scan the receipt and identify the data we're interested in. 

We can provide a set of optimizations, classifications, and preconfigurations before we process the document. We created a taxonomy that we've predefined that these kinds of documents can conform to our security purposes. Using the taxonomy, Document Understanding can first classify the type of document, the arguments or variables we want to use, and the data we need to extract or store. Document Understanding can scan a written document and identify if a signature is present. 

We keep a person in the loop in between because we can't 100 percent rely on the extraction. Document Understanding uses OCR which sometimes struggles with handwritten material. For example, it might mistake a six for a five. There must be a human in the loop to ensure quality. The device will send it to the validation station on your mobile phone. The bot will learn from the choices you make, and it will be more accurate the next time.

Document Understanding helps us to reduce human error. It can reduce the time staff spends on some tasks, but the amount of time saved depends on a few factors. We still need to validate the data because before proceeding, we sometimes collect and share sensitive data for our clients. We need a validation step in between to check before we send any data. 

One benefit of Document Understanding is machine learning. As we process more data, we train Document Understanding to classify information more accurately. Document Understanding can extract and interpret information similar to the way a human can. A human can read a paragraph and distinguish between types of information, but our UiPath bots can't. Document Understanding integrates with artificial intelligence to interpret information within that. 

The newer versions of Document Understanding can integrate with ChatGPT or any generative AI tools so that it can better interpret the information autonomously, and we don't need to create the taxonomy or classify the documents. We only need to give a prompt and input the document. 

It will read documents similar to the way a human would. Let's use a contract as an example. You want to extract data like the buyer, seller, property address, etc. It will take the information from the document and give it to you. It can also scan for checkboxes and identify which ones are checked, but there are some limitations. 

It uses a document object model to map which data is on what page of the document. For example, let's say the data you are interested in is on the third page of the document. The model knows where the data is, so it directly jumps to that particular page and extracts the information. The mapping is very perfect. 

We always use attended processes because it's a good practice. The bot can do it without a human in the loop, but I would only do that if you are certain about which information you want to extract. If you're working with a handwritten document or signatures, you need a human in the loop to validate the data and help the machine learning component learn the difference between correct and incorrect information. 

The time required for the validation process varies depending on the number of fields. For a small number, it only takes two or three minutes. When you have more fields, it may take a little longer to create and configure the document understanding model. You need to create the taxonomy, classifications, and model.

The validation process is easy. The Validation Station shows you the extracted data on one side and the document on the other, so you can easily scroll down and check if the data is accurate. You just need to click a checkbox. If you don't think it is fine, you have the option to add an exception. Based on that exception, you can create multiple conditions for how to address the same issue if it happens again. 

Document Understanding is about 75-100 percent accurate depending on the type of document, and it increases as you train the model. 

I would like to see more integration of artificial intelligence. That's being implemented, but it would be a massive improvement to the solution's document processing. If UiPath achieves intelligent document processing, it will be far better than anything on the market. There are currently some limitations with the fields that could be addressed using a GPT engine. With an integrated AI model, you wouldn't need to create your taxonomy. You would only need to provide some prompts, such as "What is the property name?" It will store that as a variable.

I started using Document Understanding six months ago. 

In the community version, there is a limit on data extraction using a form-based extractor. There are limitations on digitization in the community version. You can do only 50 or so in one hour. The enterprise version can handle a larger volume of data, but we aren't dealing with huge amounts of data. We can still use multiple types. It allows you to scale with multiple types of extractors in the same document. If I'm confident in how the model is processing a particular field, it can be adopted into the regular business structure and reused. 

I was involved in the deployment only as a developer. We created the taxonomy and the model for Document Understanding, then tested multiple cases with multiple documents. We see which extractor would be the best fit for a particular value. We can classify it according to the values we want and we can set up an accuracy also. We can set a confidence level for each variable, so the confidence is different for a regular extractor versus a complex one. I set the confidence level high on the regular extractor. 

Initially, the deployment is somewhat complicated for a developer. However, it gets easier once you understand everything. We didn't need a consultant. I could complete the job by myself. It isn't rocket science. UiPath Academy has a free course on Document Understanding. Anyone can use it for free. 

We use the free community version. Anybody can use it, but it has some subtle limitations. The enterprise license gives you far better results without limitations.

Document Understanding can handle handwriting and signatures in most cases. The community version limits handwritten document processing, but it's enough for our needs and gives us the correct data every time. 

I haven't worked with any other document processing solution besides UiPath. I researched some tools, but Document Understanding seemed like the best fit for me, so I used it.

I rate UiPath eight out of 10. I deduct two points because creating the configurations can be time-consuming. 

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.

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. 

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.