UiPath IXP Reviews

The use case is related to invoice processing. We extract details from the invoices, and after those details are extracted, we use the UiPath RPA bot to process those invoices.

We have installed it on the client's machine and integrated it with the UiPath RPA bot. Document Understanding extracts the details from the document, and the UiPath RPA bot picks up this data and puts it in the system to process the invoice.

We are processing 2,00,000 to 3,00,000 invoices received from the vendors. They have structured data. There is no barcode on the invoice. There is structured data with date, invoice number, fax code number, amount, etc. It is a printed invoice.

The artificial intelligence or machine learning (AI or ML) capabilities of Document Understanding are very good. It reduces the development time. We can extract the required details quickly and with far more accuracy.

Document Understanding works very well with structured documents in different formats. I have not tried it with unstructured data.

About 70% of the invoices are completely (100%) processed automatically. The human validation required depends on the logic that we write. If the match is more than 85% to 90%, we do not require any human validation. If it is less than 85%, a few things are required from a human. The human validation process does not take more than a minute per document.

The average processing time used to be 6 to 7 minutes per document, but with Document Understanding, it has come down to 2 minutes, which also includes any human validation that is required.

Document Understanding has helped to reduce human errors, but I do not have the metrics.

Document Understanding has helped free up staff’s time for other projects. Approximately 50% to 60% of the time is freed up.

The entity-level extraction is very good. The workflow is also very good.

Its pricing can be improved. 

I have been working with this solution for three to five months. 

It is scalable. There is no doubt about it.

I would rate them an eight out of ten. They can have slightly better performance.

We use another solution. It is a local solution that we have. It is a lot cheaper, and the pricing model is also a little different. They do not charge you on a per-page basis. We saw an ROI with this solution because of its cost and charging model.

It is mostly deployed on the cloud. The cloud type depends on the organization, but mostly it is on a private cloud. AWS and Azure are the most popular ones currently.

I was involved in its deployment on a couple of projects. Its deployment is a little bit complex because you have to set up a private cloud, and then you have to install this entire product from the cloud. With a public cloud, it is relatively easy because the cloud services are provided by the product company itself, whereas with a private cloud, you have to take more measures.

In terms of the implementation strategy, we have to identify the type of document that we want to process. We have to determine the volume. We have to determine the variations. We have to classify them into structured data and unstructured data. Once all of those things are done, we start training based on the sample format. After the training is complete, we put it into the UAT mode, and then it will go to production.

Usually, we do the deployment as implementers. We take help from the product company's technical support in case we get stuck somewhere.

It requires one or three people for a maximum of three days. The scope of deployment depends on the use case. If you have use cases across departments, then it will be deployed across departments. The deployment would be dependent on the number of departments or countries. If additional countries are to be added, we have to deploy in that environment. We have done multi-country deployments as well. Multi-function deployments are not very common because, usually, all the applications work in the same environment.

Any maintenance is taken care of by the product company. There are upgrades, and then there are bugs that are found in the product. They need to update the product on a time-to-time basis.

We have seen time to value with Document Understanding. Outside India, it would be somewhere around 18 months, and in India, it would be somewhere around 2 to 2.5 years or 24 to 30 months.

Its pricing can be looked into because it is on the higher side for developing economies, such as India, where the cost of labor is a little cheaper compared to advanced technologies.

I would rate Document Understanding an eight out of ten.

I used UiPath Document Understanding to create a report by reading invoices and V9 tax documents. I employed specific taxonomies to facilitate document analysis and populate my database with extracted information. The primary objective was to accurately identify and store relevant data from these documents within the database.

The idea arose from the observation that many companies lack a centralized repository for essential documents, such as invoices. In response, I created a website where a robot automatically uploads and interprets these invoices, presenting key details about each document on the website.

By using taxonomies, I could interpret the documents and make them easily accessible through a website database. This way, website visitors could find all the documents themselves, eliminating the need for them to repeatedly ask employees for specific documents like invoices or V9 tax forms. UiPath's visual processes further simplified this by allowing me to implement and manage the system effortlessly.

I used UiPath Document Understanding to process invoices and V9 tax documents.

All the documents processed were in PDF format.

The documents contain tables, boxes, check marks, and handwritten text.

All the documents were processed 100 percent automatically.

UiPath Document Understanding was able to handle the handwriting and signatures with no issues.

UiPath Document Understanding helped make the automation process easier for me.

The manual validation of each document took one second.

Using UiPath Document Understanding, all the documents were processed in just a minute. While I didn't have many documents, it still surprised me how quickly it worked. Manually, it would have taken me about five to ten minutes.

UiPath Document Understanding has saved me time to work on other projects in parallel.

The highly visual and user-friendly interface was a standout feature. Selecting taxonomies was as simple as clicking the corresponding areas on the invoices, enhancing the visual nature of the interaction.

UiPath Document Understanding requires more database connectors. I encountered difficulty connecting to Workbench from MySQL, necessitating a workaround.

I have been using UiPath Document Understanding for three months.

I did not face any stability issues with UiPath Document Understanding.

The scalability of UiPath Document Understanding is fine.

The initial deployment was straightforward. The deployment took a few minutes to complete and I did it myself.

Originally, I spent some time building the automation robot. However, once I completed it, I realized the value of UiPath Document Understanding.

I used the community version, so there was no fee.

I would rate UiPath Document Understanding nine out of ten.

I was the only one using the solution in our organization.

I recommend evaluating both the free and paid versions of UiPath Document Understanding. 

In Italy, one of the most prevalent use cases involves automating the processing of invoicing cycles. The issue we aimed to address through the integration of this solution is essentially the manual input of data into systems by humans and the need for checks and balances between invoicing and other physical documents. Our organization is in the manufacturing realm. We primarily use Document Understanding to process invoices, specifically a common document in Italy known as the BDT. Regarding the document format, it includes structural elements like tables, checkboxes, and headers. Some documents may feature large tables, and the header contains essential information that needs to be extracted. In terms of volume, for a medium-sized or small company, we handle approximately ten thousand of these documents annually.

The advantage stems from the seamless integration of this solution with the UiPath platform. If a customer already has the standard, robust UiPath platform operating within their systems, adding these smaller modules is all that's required to enable Document Understanding. It functions as an integrated ecosystem.

It facilitated the automation of our data entry processes.

Approximately twenty to thirty percent of our customer's documents undergo full automation in processing.

In our scenario, Document Understanding operates independently as a standalone module, not integrated with any other systems. The robots, however, interact with the systems.

The average processing time, before and after automating with Document Understanding, improved in speed for a minute.

Human errors have been reduced by seventy percent.

Document Understanding has contributed to freeing up approximately seventy percent of people's time for other projects.

I believe the most valuable feature is the prebuilt algorithm for extracting information from foreign invoices. This efficient algorithm eliminates the need to create one from scratch.

It has the capacity to manage diverse document formats, including handwriting and signatures.

Leveraging artificial intelligence or machine learning capabilities is beneficial. These technologies excel in field identification tasks, even when adjustments such as moving or rotating the identified fields may be necessary. The primary benefit of artificial intelligence lies in its ability to handle various layouts.

Around 20 to 30 percent of cases necessitate human validation for Document Understanding outputs. The human validation process typically takes less than one minute per document.

There is room for improvement in handwriting processes. It should enhance the user interface for constructing extraction logic. It is not as user-friendly as other parts of the platform. An additional feature that could be considered is the integration with generative AI. The deployment process should be more user-friendly and streamlined. Scalability capabilities should be improved, as well.

I have been using it for two years now.

It offers good stability. The need for maintenance decreases with the highest level of stability.

Scalability is limited as it relies on the document layout. Integrating generative AI could potentially address this aspect. Moving an algorithm to another project without making significant changes can be quite challenging.

Our experience with its technical support is quite satisfactory. I would rate it nine out of ten.

Positive

The deployment process is not as straightforward as a seamless deployment, such as with App Studio. The number of people required for a project depends on its nature. Typically, one or two individuals are sufficient for most deployment cases.
Maintenance requirements vary depending on the projects. The team size can range from one person to five, six, or seven people. The deployment of this solution required one month.

I believe a six-month payback period is reasonable for the time-to-value. A shorter duration would be more favorable for customers.

I find the pricing to be somewhat on the higher side. User decisions are impacted by the pricing structure.

Overall, I would rate it nine out of ten.

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 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.

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. 

Positive

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. 

I am working with Mercedes teams from Germany. They have many handwritten documents in their archive, and they wanted to digitize them. Maybe we want to digitize them with UiPath Document Understanding tool, but it is not now. Maybe next year. Most of the document types are invoices. 

Our robots will integrate these documents with SAP because these invoices have to be in SAP. The process involves OCRing the documents, sending an email to the business units, and integrating with SAP. I prepared one framework for these tasks.

Using UiPath Document Understanding helps increase the data correction rate. For example, for one document, one business unit spends an average of five to six minutes, however, our robot does it in about 30 to 40 seconds.

The UiPath Document Understanding tool is very easy to use for document understanding. I like it for its ease of use. 

AI Hub is also useful and easy to use. Creating taxonomy and clarifications from labels is straightforward. 

The integration with UiPath Studio is smooth, making it easy to create and use machine learning models.

Right now, we are trying to use Instabase, which is another tool for document understanding. It may be one one step ahead. It is faster than UiPath.

We are currently trying to use Instabase for document understanding. We have not observed stability issues with UiPath Document Understanding so far.

We have technical support with UiPath Turkey team. Sometimes they come to our office and prepare some POCs with UiPath tools. I would rate them nine out of ten.

Positive

The implementation team sometimes visits our office to assist with POCs and provides support for UiPath tools.

Our data correction rate increased to 80% with the implementation of UiPath solutions.

I am not aware of the pricing details. My manager handles that, and as far as I know, Instabase is more expensive.

We are trying to use Instabase as an alternate solution to UiPath Document Understanding.

I'd rate the solution eight out of ten.

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.

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.

We use UiPath Document Understanding for P2P processes to extract document information for ingestion, processing, and classification.

The key problem our clients faced, which we were trying to solve by implementing UiPath Document Understanding, was the large amount of unstructured data in the events. They want a solution that can solve this problem right from the beginning, from the document ingestion phase to the document classification and streamlining the document for the data taken right inside the documents. So driving all those analytics and the ROI in the end is a major key asked by most of our clients.

Our clients deploy UiPath Document Understanding both on-premises for our banking clients and also on the AWS cloud for others.

UiPath Document Understanding has helped us automate a large number of accounts payable processes for our clients such as P2P and O2C. 

It helps us process many types of file formats primarily PDF. We are able to process a large volume of documents using UiPath Document Understanding.

In our P2P process, we have encountered some handwritten invoices. The handwriting text recognition feature offered by UiPath is good, and it has been very helpful in converting these handwritten documents to a more structured format. Apart from handwritten invoices, there are other documents that require extensive merging and sorting, which has always been a concern for many of our clients. I believe that UiPath has effectively solved this problem.

Our clients process over 90% of documents using UiPath Document Understanding are processed straight through without human validation.

When we use Document Understanding to analyze data, the AI works in the background to process the document seamlessly.

The ability to integrate with other systems and applications is really great. I would rate it a nine out of ten.

It has improved our clients' cost savings and time savings, in turn improving productivity and providing a better ROI.

The time required to manually validate information depends on the type of document. A handwritten document takes longer than a PDF file and can take up to half an hour.

The average handling time has improved and is now under ten minutes.

It is very effective at reducing human error in identifying incorrect fields in documents. This is where I think it excels. We have seen a reduction in human errors by up to 90 percent.

UiPath Document Understanding has helped free up staff time for other projects.

We typically see a time to value after four to five days from starting the process, but again, this depends on the process.

Document classification is very good. We have received great feedback from customers who use it to classify bank documents, sort them, and generate formal documents. I think the overall presentation of the final document is amazing.

UiPath Document Understanding can improve its handwriting and signature recognition. We have also been engaging with other intelligent document processing companies such as ABBYY and Kofax, which have superior features for handwritten text recognition. UiPath offers a good solution, but ABBYY has far more support for handwritten text recognition, especially in the latest version.

It is still in its infancy and has room for more advanced AI features.

They need to strengthen their relationships with IDP partnerships.

They should expand its library.

I have been using UiPath Document Understanding for almost six months.

UiPath Document Understanding is a stable solution that our clients are comfortable using.

UiPath Document Understanding is highly scalable if I want to extend support to the maximum number of subprocesses within a single process. Therefore, I believe there is no scalability issue.

The support is good but sometimes the response time is slow.

Neutral

The initial deployment complexity depends on the document. Therefore, we must be cautious when integrating with third-party vendors. I believe it takes more time to deploy critical documents with sensitive data. We must be very careful when choosing a vendor, such as AWS or Azure, to ensure that we can integrate with them successfully.

We use a team of three to four people for Document Understanding deployments.

UiPath is more expensive than ABBYY and Kofax.

Our clients are concerned about the volume-based pricing model, as UiPath charges more than other vendors in the market.

I would rate UiPath Document Understanding seven out of ten.

UiPath Document Understanding requires maintenance from time to time, and we are currently experiencing a slowdown in the oral solution. Therefore, I believe that maintenance is required. Perhaps they need to develop a newer, more intelligent, and more efficient version, as Kofax and ABBYY have done. The same team of people that deploy UiPath Document Understanding also handles the maintenance.

There are other vendors who are excelling further in the intelligent document automation space. They offer more advanced capabilities and AI intelligence than Document Understanding, which is still an evolving solution. When we read customer reviews and have first-time conversations with clients, we notice that they often start by naming vendors like ABBYY, which are known for their technical expertise in the IDA space.