UiPath IXP Reviews

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.

Our old process involved manual data extraction from a large volume of documents with varying types and templates. This labor-intensive task required a significant workforce. We implemented UiPath Document Understanding to automate this process and eliminate the need for hand-coding solutions.

UiPath Document Understanding helps prepare data for machine learning by labeling documents used to train the models that will ultimately automate document processing tasks. We also use it to extract information from various identity documents like passports and ID cards, financial statements, credit card statements, and bank statements, and it can even process bank transaction data.

The documents we process using Document Understanding include tables and sometimes handwriting.

Around 70 percent of our documents are completely processed using Document Understanding.

The UiPath OCR works perfectly to extract handwriting, signatures, and multiple formats.

AI and machine learning prove valuable in training Document Understanding systems by analyzing data and identifying patterns, improving the system's ability to extract information from new documents.

AI streamlines Document Understanding by eliminating the need for manual coding. Users input documents into the AI, which then automatically classifies and extracts relevant information from each file. This saves staff over 20 hours a week.

UiPath Document Understanding integrates well with other systems.

For any newly implemented processes, human review will be necessary every day until Document Understanding is fully trained. The validation takes one minute per document.

The implementation of UiPath Document Understanding has saved us 50 percent of the time spent previously processing documents.

UiPath Document Understanding significantly reduces human error in processing documents, with complete accuracy achievable for standardized formats. However, its effectiveness in handling handwritten data varies depending on complexity.

UiPath Document Understanding helps save 20 percent of staff time to work on other tasks.                                          

The most valuable feature of UiPath Document Understanding is the AI Center.

UiPath Document Understanding, while effective for its own platform, could be even more valuable if it integrated with other commonly used platforms, allowing for a more universal approach to document processing.

I have been using UiPath Document Understanding for three years.

UiPath Document Understanding is stable.

UiPath Document Understanding is scalable.

The technical support is easy to access through the UiPath portal.

Positive

I've used IQ Bot from Automation Anywhere, Microsoft Intelligent Document Processing, and UiPath Document Understanding. IQ Bot and Document Understanding offer similar functionality, but only Microsoft's solution works across different platforms. We mainly use UiPath Document Understanding because it aligns with our client's preferred platform.

The deployment was straightforward. One person is enough for the deployment.

UiPath has a higher upfront cost, but its Document Understanding feature is not a significant additional expense compared to the overall platform.

I would rate UiPath Document Understanding nine out of ten.

We have six people that use UiPath Document Understanding.

I recommend UiPath Document Understanding to others.

We use UiPath Document Understanding to extract insurance document data related to vehicle accidents in a pilot project to better understand who is at fault. We open the documents, connect with the insurance companies, and obtain the police reports describing the scene of the accident. We extract the information into a single spreadsheet to charge the third party.

Before implementing UiPath Document Understanding, humans required around 15 minutes to extract the required information per document. Now, using Document Understanding, we can process 11 documents per minute with 98 percent accuracy, saving a significant amount of time and money.

The data we extract using Document Understanding relates to vehicle accidents. The insurance company uses this data to charge third parties involved and recover most of their money with minimal human intervention.

We are extracting information from 50 insurance documents to prove the concept of showing coverage for those involved in the accident.

The information from the insurance company is in PDF format and is structured so that we can easily extract information from it. The other public policy documents vary in quality depending on the state and district in Brazil. We have 27 states, and these documents are unstructured, making it difficult to extract data from them. Sometimes they are in image format, and other times they are in PDF format.

The 50 structured PDF documents from the insurance company are all 100 percent automated using UiPath Document Understanding.

We are impressed with the machine learning and AI capabilities of Document Understanding. We can use APIs to identify whenever a vehicle accident is reported, which helps our business.

Using the AI we can access vehicle accident reports and train the machine learning to understand the majority of the documents we extract information from to help improve our business processes.

We use the UiPath platform orchestrator with an API to integrate with OpenAI.

UiPath Document Understanding has helped our organization demonstrate to potential customers how the solution, together with AI and machine learning, can benefit their businesses.

Before Document Understanding our process took 15 minutes per document and now it takes under one minute.

Document Understanding has helped free up our staff's time to work on other projects.

UiPath Document Understanding is easy to configure and user-friendly.

It is easy to insert a human into the loop.

Explaining the UiPath Document Understanding process to my customers is easy for them to understand.

UiPath Document Understanding produces a high accuracy rate of 98 percent.

The interface has room for improvement.

I have been using UiPath Document Understanding for ten months.

UiPath Document Understanding is stable.

We can easily scale UiPath Document Understanding by adding licenses.

We have only used the UiPath User Community site for support on issues or questions related to Document Understanding.

Positive

We previously used Microsoft solutions and switched to UiPath Document Understanding because it is the best. 

We can demonstrate a return on investment for our customers with the huge amount of time saved using UiPath Document Understanding.

UiPath Document Understanding is expensive, with the basic annual package including 6,000 documents and the next package including one million documents, a huge price difference.

We evaluated Automation Hero and a Microsoft platform. We chose UiPath Document Understanding because we are a UiPath partner in Brazil, and as a global leader, UiPath can support us on all of our projects.

I would rate UiPath Document Understanding nine out of ten.

We are currently conducting a proof-of-concept using UiPath Document Understanding, so we must use human validation for all processes to demonstrate the accuracy of the bots to the customer. Human validation takes five minutes per document.

Three professionals are required for the maintenance.

We primarily use it for invoice processing as well as receipt processing or expense processing.

It has advanced capabilities compared to other competitors, whether Blue Prism or Automation Anywhere. We can select the ML models based on the type of process that we are automating. 

It has helped process around two thousand documents per month, in formats including PDF, text, image, and even handwritten documents. This solution has played a significant role in drastically reducing human errors by ensuring that 95% to 98% of tasks are done through the system.

They can include some features in utilizing the product of assessment understanding, or more specifically, a better efficient handling of the ML skill, which right now is not that efficient. The integration could also be simplified as it's somewhat complex at present.

I have used the UiPath Document Understanding for one year.

They often ask us to go through the documentation first instead of directly explaining or addressing the root cause of issues.

Neutral

We did not use any previous solutions for document undertsnading. This is the first one we have used.

The initial setup is not straightforward. One needs to have knowledge to set it up.

The implementation team involved an architect, senior developers, and another architect.

Return on investment could be high if you are using the product for multiple processes. The more automation you achieve, the more ROI you will see.

It is expensive/ It's not easy to accommodate in the budget.

We didn't evaluate other options since we didn't have time to explore that much.

I would rate UiPath Document Understanding a seven out of ten, although the platform doesn't accommodate half ratings.

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.

A recent use case was for an insurance company based in the United States. For that, we were recording or collecting the data from the insurance brokers who used to fill their documents. We had to find a few segments on the basis of them. We were collecting the data and confirming whether those brokers were coming from an authentic source. They had a stamp or a legal insurance number, and we were maintaining a few dictionaries containing the images of their signatures. Once we received a document from a broker, we passed the whole document into different segments, and then we just validated the signature part to see if it was coming from an authentic source. We validated that the signature and the image looked similar, and there was at least 80% similarity.

We were extracting the IPIN number from the Microsoft Intelligent OCR. We were able to extract almost 85% to 90% of the numbers. It contained digits that were being imposed on a stamp that we had provided to them, so there was less complexity because there was less human intervention. They were not manually writing those numbers where it could be a bit difficult for us to diagnose whether it was a four or a nine. With a digitized number imposed on the stamp, it was a bit easier for us to read it out. This is the use case that we just finished and deployed, and it is processing 150 to 230 requests on a daily basis.

I have mostly been automating banking, financial services, and insurance (BFSI) processes.

With Document Understanding, we have been able to process both structured and unstructured documents. It does not matter whether a document is structured or unstructured. The only thing is that data should be concise, and it should be constant. If we are getting 70% unstructured data and 30% structured data, we are good to go, but we should be aware of how much structured and unstructured data we are getting. If we get a picture, then based on that, we serialize them. It is either a standardized process, or we have to use some APIs or some logic to make it structured. We initially filter out based on the picture view. If the visibility of the data is less than 45% or 65%, it means that the data is not as structured. We then move it to a different folder to process it later. If it is standard and structured, we process it immediately. We do not need to worry about the chunks. There is a positive output in our hands when we have achieved 45% or 65% of our target. We can then work on the remaining part to make it more centralized, so it is a bit easier for us.

With Document Understanding, we are able to handle things like varying document formats, handwriting, and signatures. The approach we take depends on the nature of the data that we are getting. For example, a requirement from the insurance company was to mandatorily verify whether the source is authentic or not. They had metrics at their end to say who were the legal brokers and who were not legal brokers. It was not challenging for us there to extract that data from their backend because they already had all the information. We just used their APIs. We just read the data out and compared the data from there.

In terms of human validation required for Document Understanding output, we needed to finalize if the data coming from Document Understanding was correct or not. If it was not correct, we moved it to the process folder. As we marked it as incorrect, it asked us the exact location that we were looking for to get, for example, the grand total. We defined that, and then it got stored in its knowledge base system, and then it got processed. It can be processed as an attended bot or as an unattended bot. It totally depends on how much data or knowledge it has been gaining from humans, and day by day, with more knowledge, it becomes more capable of processing the data independently.

The average handle time depends on the number of cores that the operating system has. If you have 14 to 16 cores CPU in your machine, 3 minutes would be required to process a 3 MB file. It also depends on the number of pages or the complexity. If data visibility is clear and the page number is not more than five, it can process the file in 3 minutes.

After automating the process with Document Understanding, it takes two minutes to process a single PDF. I do not have the exact data of how much time humans used to take. They were probably putting in nine hours per day, and after automating the process with Document Understanding, they are putting in two hours per day, so they are saving seven hours per day. Monthly, there is a saving of 150 hours.

In terms of error reduction, in the beginning, we were getting a lot of machine errors, but as the process got smoother and the knowledge base system stabilized, the machine errors reduced, and the human errors also reduced.

Document Understanding helped free up the client's staff’s time for other projects. Before automation, they had seven people on their team, and after automating the process, they cut their budget and reduced the manpower from seven to four. They were able to free three staff members for other projects. They saved 35% to 45% of manpower.

Document Understanding has better machine learning or ML capabilities, and that is why I prefer Document Understanding.

It would be much easier if UiPath increased the count of pages. Currently, they are allowing one million pages for $10,000 per month. I would prefer to increase the page count or reduce the dollar count in terms of processing the documents. I would prefer $6,000 per month for processing 2 to 3 million pages per month. It will then be much easier for companies with a low budget to use this product.

I have been using UiPath Document Understanding for more than two years. 

It is stable. They always come up with a proper and stable approach. 

It is scalable. If they increase the page count or file count, our solution will not have any issues, and it will process them. The more you train the bots, the more the efficiency of the processes.

They were helpful. If you have a paid license key, they will help you a lot.

Neutral

I have worked with IQ Bot, but as Document Understanding got more stabilized and more well-known in the market, I started to move from IQ Bot to Document Understanding. I used IQ Bot when Document Understanding was not there. In 2021, when UiPath came out with the Document Understanding solution, I left IQ Bot behind and started developing my skills in Document Understanding. I have expertise in Document Understanding and IQ Bot. Document Understanding has better ML capabilities, so I prefer Document Understanding.

My whole six years of development experience is in the BFSI sector. I did only one retail sector project, but for that, we did not use UiPath Document Understanding. We used Magic OCR, which is not a Document Understanding or IQ Bot model. Those who are not willing to invest that much amount in UiPath or Automation Anywhere prefer to automate by using some open APIs. We used Magic OCR to scale the picture into a proper frame. We used to scale them as per our dimension or as per our frame, and then we used to perform all those activities that were required. If they came up with a cash memo, we had defined a few parameters for the grand total, discount, advance payment, overdue payments, and so on.

UiPath provides two options: the first one is a public cloud and the second one is on-premises. It is based on the package that you purchase from them. If you purchase the cloud version, then they will share with you the public cloud. If you go with the on-premises option, they will ask you to arrange a server. They deploy or install Orchestrator on the IIS server, and from there, we operate it.

We are using it on the cloud because AI fabric and lots of functionality are available on the cloud. Our cloud provider is Microsoft Azure.

The deployment process depends on the approach or SOPs of the company. The company I have been working with recently has its own DevOps team, but one of the companies I have worked with did not believe in the DevOps part. The developers were the ones gathering the data, developing the requirements, and fulfilling those requirements by doing the development and then deploying it on the production. It depends on the company model. I have worked on both scenarios, and there was not much issue with the deployment of the Document Understanding model. It is already based on the package. We added that package and then directly deployed it on Orchestrator. From Orchestrator, we operated them.

In terms of maintenance, it does not require any maintenance from our side.

The ROI is in terms of efficiency. There are time savings for humans and the accuracy of the results.

I would recommend Document Understanding. I prefer Document Understanding over IQ Bot as they have multiple flavors of machine learning models. If a person is capable, they can also easily achieve the same thing with programming.

I would rate Document Understanding an eight out of ten, but they can improve the costing part.

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.

Positive

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.

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.