UiPath Document Understanding Reviews

Our client has PDF invoices and we use the solution to extract the details from them. We are using it in finance and health care. We have about 16 templates that we process now. The data is in semi-structured format and we mostly process things like signatures and tables. Out of the 16 templates, about 12 are completely processed automatically.

It has helped us automate finance statements and invoice billings.

Another benefit is that it has mostly helped reduce human error. We have a criteria of 75 percent matching. Out of 10 PDFs we have been getting eight PDFs with at least 75 percent matches. It has also helped free up staff time.

The taxonomy and Validation Station are among the most helpful features for us. If anything is extracted incorrectly, we can manually extract it there.

And we have included the AI Center for our customers to interact with PDFs to be extracted. Based on the approval or rejection feature, our customer can determine which kinds of PDFs they can automate.

I also like the table extraction feature. UiPath is very good with structured data.

Handwriting is more complex. We have not been able to get handwritten signatures correctly extracted in different languages. Our customer is in Dubai, and the solution cannot accurately process signatures in the local language. But it is a great tool for handling structured and semi-structured formats.

Another of the disadvantages is that we cannot include another tool. For example, with ABBYY extraction, we can integrate the process with any other product. We can integrate Document Understanding using JSON templates, but it is a bit of a complex model to extract the data from the JSON.

There is also room for improvement in long-running table extraction. If a table continues for more than 10 pages, in some cases, we have observed that it only extracts six or seven pages and skips the last pages.

I have been using UiPath for about 10 years.

Overall, the product is stable.

In our case, the use of Document Understanding is restricted to a particular group of users, around six or seven people.

The technical support from UiPath has been pretty good in the last year. It has been a very good experience. 

We used Azure DevOps for the deployment and we faced some issues regarding the deployment with UiPath and Orchestrator. We had a very good response from the UiPath technical team.

There is some room for them to improve the speed of the response because we often used to get late responses. But the resolutions are good.

Positive

We were using ABBYY, but it is more like a developer's tool with everything a developer needs for extracting fields. But we can train and retrain Document Understanding. In that way, I feel it's a better tool.

The pricing is reasonable.

As for additional costs, the solution is based on OCR, and sometimes the OCR cap is exceeded. It's not a major cost. Per month, we will have two or three scenarios like that. With ABBYY, once the cap was reached, we had to wait until the next day to use it again.

We did not evaluate other solutions. Using Document Understanding was a requirement from the client's side.

In terms of human validation for Document Understanding output, we have a limit of 75 percent correct scenarios. If it is below 75 percent, the user will be notified.

The solution doesn't require any maintenance unless the client requires more fields to be extracted. Only then are there changes that I need to make.

My advice is that if you are starting to learn about Document Understanding, you need to learn more about the taxonomy and what fields you are extracting. You need to have clarity on which position you are extracting, as it mostly depends on the position.

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.

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.

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. 

I work for different clients. Currently, I have three clients, and I use it based on their requirements.

We have contract generations, and we extract data from contracts. This is our primary use case. We are receiving documents through an omnichannel, and we extract data based on the business requirements. After that, we automate and upload the data to Salesforce and SAP.

We process 1,000 to 1,500 invoices weekly. They are mostly semi-structured contracts. There are also some invoices or printed bills.

Document Understanding has been very helpful for my project. Its architecture and concept were very helpful for my process.

Document Understanding has saved us a lot of time. I have much more time. For example, in 2017, when I was doing the same normal extraction without it, it used to take two hours. Now it takes only 20 minutes to extract 20 to 30 documents. If our configuration and technique are very good, it would take only 10 minutes. Document Understanding is very powerful if a developer has good technical knowledge. By properly configuring the workflow, you save more time compared to other tools.

At times, we have business requirements for human approval. When required, the human approval or validation process happens immediately. We design the workflow for attended or unattended automation. Once configured, immediately after extraction, it will go for human approval. The automation happens based on approval or rejection. 

It has a good framework. It takes care of all things. We sometimes need to configure manually, but generally, it takes care of 95% percent of error handling. Its framework gives very good results.

AI Center is helpful for creating data sets. Machine learning helps with some extraction. The ML extractor gives good results after training. It also gives automatic results. It automatically identifies the same type of invoices or a different type of classification. The ML extractor is very good. 

The extraction can be better. ABBYY FlexiCapture has more capabilities than Document Understanding. It can also extract automatically without training, whereas with Document Understanding, we need to train everything. For example, we have uploaded ten invoices of a type, and when we upload the eleventh invoice, it can find approximately eight fields out of ten, but ABBYY FlexiCapture can find ten out of ten. More documents are required to train Document Understanding. 

There should be Generative AI and sentiment analysis. These two things will be very good.

I have been using this solution for three years. 

I would rate Document Understanding a ten out of ten for stability.

I would rate Document Understanding a ten out of ten for scalability.

We sometimes require technical support from UiPath. Sometimes, we get an error, and we cannot find the solution on the web. We have to contact UiPath's support team. I have already contacted them two or three times.

The support experience varies based on the type of support plan. We have a silver membership. They also have diamond and gold memberships. If an organization has a diamond membership, support will be given very fast. For silver, it takes three to four hours depending on the emergency. 

Overall, their support is good. I would rate them an eight out of ten for support.

Positive

I was not using a similar solution previously. Before UiPath, I was a .Net developer. Stanford University was providing a code-based extraction tool that I was using.

Currently, we are also using ABBYY FlexiCapture. We are not using Document Understanding for handwriting. We are using ABBYY FlexiCapture for that. Document Understanding gives good results, but ABBYY FlexiCapture is tap-and-play. For extraction, ABBYY FlexiCapture gives very fast results, whereas Document Understanding requires some processes. To save time, I am using ABBYY FlexiCapture even though Document Understanding is more accurate than ABBYY FlexiCapture.

I do not know about its price, but for large organizations, UiPath is cheap, whereas, for small organizations, UiPath is expensive. For example, if 500 licenses are needed for one company, UiPath is cheap. If only 5 licenses are required, UiPath is costly.

I would advise taking a step-by-step approach. If you miss any step, the bot will fail. For large document extractions, you need to follow the step-by-step instructions provided in the UiPath Academy.

I have not used Forms AI, but I use AI Center. In AI Center, I am using some datasets. I am maintaining some data sets, and based on the business requirement, I use the data.

Its integration should be good, but I have not tried any integration with other tools. I have integrated ABBYY and UiPath, but I have not integrated Document Understanding.

I would rate it a ten out of ten. It is now a very mature tool.

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've done multiple projects. A couple of them included invoice processing. It has a machine learning package that works out of the box. For invoices. I use that. It does a very good job. 

I also use document understanding, which doesn't have any training. I trained it for the extraction of data for some forms like car loan installments. It did a pretty good job. 

In addition, I used it for a medical department. I use document understanding. 

We wanted to have a way to do data extraction from PDF documents. It helped us automate the process. For example, if you purchase a car, the loan installment paper includes items like the vehicle number, purchase information, buyer and seller information, et cetera. It can pull that out. We can also use it similarly in the healthcare industry, to get client details. 

It's great for document understanding for invoices and installments. 

When it comes to document understanding for handwriting, it does a decent job sometimes with handwriting, however, some people have weird handwriting and the OCR can struggle to pick up the information. In those cases, you have to read it yourself. However, overall, it does a decent job. I haven't used it to read checkboxes or bar codes. It works well with tables, however.

There are thousands of documents that are completely, automatically processed. It can process close to a few thousand invoices per day.

I also integrated it with the Action Center for some projects; It's pretty neat.

I like the machine learning skills and the fact that they come out of the box. They are packages that you can just deploy. The training of the ML is great; there is this tool that comes with it called Data Manager. That's very handy when you are labeling data and then using it. 

The AI center is excellent. AI does a pretty good job covering all the needs that are needed for automating the process for semi-structured documents. The structured documents with the form extracted, overall, are pretty good. It's doing a very impressive job. I was surprised the first time I was exposed to it. Now, I actually enjoyed doing it. It allows me to automate items that are mundane. For example, if an employee is given a task to scrape data from invoices, which are PDFs, they can get the robot to do it. Due to the fact that the documents most of the time are semi-structured, machine learning can handle the task, and machine learning is doing a pretty good job of handling that instead of the employee.

I've used Forms AI. So far, my experience has been pretty good. That said, it only works for structured documents. 

In terms of the documented understanding of integrating with other systems or applications, everything is good. You can integrate it with the action center, and it does a very good job. Everything is handy and easy to use. Integration overall is good.

Human validation is not always required for the outputs. It depends on the document. For invoices, you might need human validation 5% to 10% of the time. If it processes ten documents, I would expect one document at least to need human intervention. If you are building some custom ML skills for some documents, if the document itself is scanned well and positioned well, it does a pretty good job of extracting the needed fields. If it's slightly less quality then the robot will struggle with both the OCR or extracting and digitizing data. Overall, we might need 10% to 20% human validation. The validation process itself now takes about a minute with the help of automation. It's reduced everything by a minute or two to up to five or six minutes. 

Document understanding has helped us to reduce human error by at least half.

The only problem that I can see with integration is some of the features cannot be used inside the loop. At least that was the case before. I don't know if they fixed it or not. You can't put some of the activities that are de-related inside the loop. It's going to throw an error if you do.

It would be ideal if they could include more packages for more use cases.

I've used the solution for about a year. 

I've contacted technical support and they have been helpful. 

Positive

I'm a customer and end user. I work as a developer. 

I'd rate the solution nine out of ten overall. 

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.