UiPath IXP Reviews

We use the solution in pharmacy health care, and our role is to enable doctors so that they can set up a personalized clinic - everything a patient requires. We get information in the form of a document and we can break it down into sheets and JSON files, for example. We use a UiPath documentation tool. 

Document understanding has helped us increase our efficiency and accuracy. We don't have to manually check data again and again. 

After the first month, we discussed how the solution was benefiting us, and we decided to continue with it.

It helps with data and consistency. It helps us receive information and convert it so the systems we have in place can understand a problem and generate responses accordingly.

We've used it in one process where we received a patient's pharmaceutical documents from other sources that come in different formats. We receive the formats, convert the information into a standard format, and then process the information to provide information for insurance forms. 

The average document size is not very large, likely 80-100 MBs. However, the total count of the patients is somewhere around 10,000. 

We have 50% to 60% of clients directly onboarded via an insurance form. Therefore, we are provided with the exact form we need and can run a complete automation on that. There's no type of manual involvement there. 

The format for setup is a great thing. Earlier, the tool that we used was pretty manual. In this case, it's a bit easier for our developers.

The solution can detect signatures to let us know that there's a signature there. You can construct tables or any other format of data based on pure text information. 

They are employing an ML model for detection conversations. They are also trying to deploy a written-to-text conversion. They are convinced AMR systems will replace other manual work.

The main value of AI for us is to convert data formats from one type to another. We receive data stating two or more complex data points mixed later, for example, the license number and the serial date of operation for the doctors or the patient code; sometimes these things are mixed together. We want all those to be arranged. Their AI does the job very well.

We can integrate document understanding with other systems and applications. With it, we can simply write down a code to communicate with the ML model, for example, how to convert the data and which datasets to look for precisely in the documentation. We were able to communicate easily what would be the format of the PDF documents that we would be providing. The integration part and communication was the best aspect of the entire application.

We have Veracode integrated with it. We will do a manual check if we get a security flag where the data may be inconsistent. We usually get an alert like this once or twice a week. The human validation process usually takes an hour since we have to manually check the parameters. Before implementing the solution, the handling time before automating the process was pretty much the same. With this, we may have reduced it by half an hour. Also, previously, we'd have more manual interventions happening, maybe three or four times a day; however, now, with everything automated, that only happens one or two times a week. It's reduced the frequency by about half an hour on average. 

Using the solution has freed up staff time. We've reduced our team size in regards to quality checking. We've reduced the amount of work by 40 to 50 hours a week. 

UiPath's documentation tool is not great with converting handwriting to text, so we only used it for the conversion of insurance documents into other formats.

They could modulate the ML model in the future. When it comes to working with data and processing reports, we have to target the datasets we had earlier targeted and redefine the parameters, which takes a lot of time. If the ML model, at the time it is analyzing the data, could in itself provide the insights we will need for future reporting, that would be great. There needs to be better real-time analytics since we aren't getting the data for reporting until we go and seek it out. 

If there were more integrations with Veracode or the AWS server, so we don't have to completely transfer our data and keep data on our servers, that might increase security. 

I've used the solution for a year or so. 

The solution is good. It's very stable.

It's not deployed across multiple departments. We have this deployed across one department. We have two developers working with the stream of data. 

For small to medium firms, the solution scales well. However, if you are going for a global scale, you should develop your own models and not rely on outside models. 

Support is good. That said, sometimes they have problems understanding what we want to do with the data since we cannot provide the data in its raw format. We have to decrypt it. This makes it a bit harder. That's why we would like integration on our servers instead of theirs. 

We did use a different solution previously. We switched since the number of tags we were getting was pretty high. We had to do more manual interventions a lot more often. The parameters we used to communicate were also manual. It required setting up a decision tree in the whole of the document. A lot of the time, we would not know what the document type would look like. It required the developers to look at the documents, create a decision tree, and go from there. With UiPath, we don't need to do all that manual upfront work. 

I was a project manager, not a developer, deploying the solution. My understanding is the process was moderate. It was eight too easy or too complex. 

The implementation involved discussing the work with the insurance firm. We explained we were moving from one system to another. Once we had that conversation, we received the documentation in the format we wanted. 

Then, we looked at how we encrypted our data before sending it to UiPath servers. We did have a lot of compliance issues and had to be careful. 

Once we came to the physical implementation, that was easy. Managing other stakeholders and their clients was the hardest part.

We had three developers from our team working on the deployment. It took us about 10 to 11 days to deploy.

Twice a week, maintenance is needed whenever there's a flag raised when data points do not match. We can simply ignore the solution and change the data file, or we can go in and see what is wrong with the file type and adjust it so that it doesn't happen again. 

We did not use any outside assistance beyond the help of UiPath's support team. 

The ROI is pretty good. We did not do any calculation for ROI. However, the accuracy percentage and time reduction which we noted, have made us happy.

We originally noticed a time to value for UiPath within 10 to 12 days.

The pricing is pretty fair. It is quote-based. Overall, it's fair. If you are a small firm looking to scale up, it is good. Enterprises should create their own ML model instead of relying on some outside product.

We looked at a few other options and did a few POCs. UiPath is able to sense and analyze a document and create a hierarchy for you. You can also create a manual code if you want something done differently. The only issue is we have to upload the information to UiPath servers, which may be a security issue. 

We're end-users, not integrators. 

It's a good idea to have a call with the support team and managers and do a review to understand the solution to see if the product would work with your type of data. It's important to test it out, ideally using your own data. 

I'd rate the solution nine out of ten. 

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.

We implemented UiPath Document Understanding for our first project with a pharmaceutical insurance company. They were receiving invoices from over 2,000 different vendors in a variety of formats on a daily basis, and they wanted to automate the process. We are receiving invoices in their email, and we are automating the download and processing of these invoices. If the confidence level of the automated data extraction is low, a user or client can correct the data according to the invoice and then submit it. The data will then be improved. We will be automating this project in two parts: first, reading specific emails and downloading the attachments; and second, checking if the attachments are normal documents or invoices.

We have implemented UiPath Document Understanding for two companies: one in the insurance industry and the other in the financial industry. We have completed the document creation process, which includes OCR and automatic signature imposition by different lawyers on the finalized documentation. We also use Document Understanding to read the document after analyzing it, and we then update the PDF with a front page signature and other components. This is a small process, but the first project was very large and we gained a lot of business from it. It was a very good project overall.

We process between 100 to 200 documents per day using Document Understanding.

The documents include checkboxes and barcodes. Some of our vendors only provide handwritten invoices, which Document Understanding could not read. These invoices had to be processed manually by the user.

UiPath Document Understanding can handle varying document formats including handwritten documents.

We have implemented a machine learning model to sort vendor names and important information related to those vendors into our system. When the model encounters a vendor that it has already seen, it automatically grabs the important information from the invoice. The model is continuously training on the new data that it receives, so it can become more accurate over time.

Machine learning was very good. We don't think we can implement without any ML model.

We integrated Document Understanding with Dynamic CRM so that the information extracted by Document Understanding is automatically input into CRM.

The amount of human validation required is based on the confidence level of the ML model. Each time human validation is required, the ML model learns and the need for human validation decreases. At the start, the ratio of documents requiring human validation was 50/50, but this ratio decreased with each iteration.

Document understanding helps reduce human errors. For example, if we receive 150 emails daily, we must analyze and process each email accordingly, such as sending invoices, checking invoice values, and investigating all relevant information. We must then read each invoice and enter the data into the system. This is a very active task that requires around 15 people to perform daily. Document understanding has reduced the need for human interaction by allowing us to automate this process. Now, only one person needs to analyze the email invoices. Once the invoices have been checked and analyzed, they are passed to a UiPath bot, which handles all the subsequent steps, such as reading the invoices and entering the data into the system.

Document understanding has helped free up staff time.

UiPath provides a useful feature that allows us to classify documents as invoices or not.

If the confidence level is low, we can check it and provide the product value to move forward. In this step, the user can sometimes skip or delete pages, especially if we receive a large PDF with the first two pages being invoices, followed by some relevant documents, and then more invoices in the same period. This is a very good feature of UiPath Document Understanding, as it allows the user to skip pages within the PDF document to move forward. For example, the user can specify that the first two pages and pages nine and ten are invoices.

UiPath Document Understanding's ability to read handwritten files has room for improvement.

The price of Document understanding is high, and we are constantly struggling to get our clients to use it because they find it to be expensive.

I have been using UiPath Document Understanding for one and a half years.

UiPath Document Understanding is stable. We have not encountered any downtime.

UiPath Document Understanding is scalable.

The technical support was helpful.

Positive

The initial deployment was straightforward.

Two people were required for deployment.

The implementation was completed in-house. We have a large team that includes technical consultants, architects, and developers.

The last time we implemented UiPath Document Understanding the price was high.

I would rate UiPath Document Understanding six out of ten.

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'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. 

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 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.