UiPath IXP Reviews

The main use case for UiPath Document Understanding is extracting data from invoices. This invoice data needs to be fed into other ERP systems.

I have worked on two projects with UiPath Document Understanding. One involved a structured format with both scanned and electronic documents. The other project involved an unstructured format with approximately 20 different formats. For these formats, we created our own custom ML model. We trained the model on the documents and formats, allowing UiPath Document Understanding to categorize and classify incoming documents and use the appropriate ML model to extract data.

For RPA automation professionals, it is very easy to extract documents using UiPath Document Understanding. There are predefined ML models available in UiPath Document Understanding. Another interesting feature is that we can create our own ML model to utilize. Recently, they have introduced GenAI, which allows us to extract documents even faster and without hurdles.

Handwriting recognition in UiPath Document Understanding is very difficult. It is particularly challenging to fetch handwriting, government official seals, or authorized signatures. When working with UiPath Document Understanding, extracting and recognizing handwriting was very difficult. Matching handwriting is also very tough. Handwriting detection and signature detection in UiPath Document Understanding could be improved.

UiPath consistently improves their product based on user, customer, and community feedback. They are still enhancing capabilities for unstructured documents through GenAI implementation. They are doing their best to handle the variety of documents worldwide. Integration with UiPath Document Understanding, compared to the last two years, is now very easy and user-friendly.

I have been working with UiPath Document Understanding for three years.

For stability, UiPath Document Understanding rates an eight out of ten.

For scalability and ability to expand, UiPath Document Understanding deserves a ten out of ten.

As customers, we receive immediate support from the UiPath team. For technical support, they deserve a ten out of ten.

Positive

It takes time, but there are predefined templates available in the project. We can use these templates for document understanding, making the process quite straightforward and not too complicated.

We just need to grasp the concept, including labeling, digitization, extraction, and validation. Once we understand these components, using document understanding becomes very easy nowadays.

UiPath Document Understanding has helped clients reduce human errors. The bot processes approximately 300-400 documents per day, which would be difficult to review manually, making it very useful.

The cost is considerably high for UiPath Document Understanding.

Compared to other tools, UiPath Document Understanding performs quite well. Power Automate RPA tool is the main competitor. While there are multiple tools such as Automation Anywhere and Blue Prism, Power Automate is introducing new features relevant to document understanding and AI capabilities, making it a strong competitor for UiPath Document Understanding.

They have introduced Agentic AI in the agent builder and AI features such as Autopilot. This is very useful for speeding up development and delivering projects faster.

With Autopilot and Agentic AI, we can write prompts and build workflows. However, these workflows still need review, understanding, and possible modification. While AI integration has made development easier, there is a growing dependency on AI, which may lead to forgetting fundamental concepts and core knowledge.

I can recommend UiPath Document Understanding to other users. I would rate UiPath Document Understanding a seven out of ten.

In a medical healthcare department, when we need to retrieve digital documents, we need to classify them. The first step is to use AI to understand what type of documents we're dealing with. Once we've identified the template, we can extract information using specific OCR tools. Depending on the confidence of the extracted results, we may need to apply additional OCR, use a more active tool, or pass the document to an agent for review if the AI doesn't recognize a specific element like the "person page of the commission." Finally, the extracted fields are classified within the system and organized into different folders. This is the process I'm using with UiPath Document Understanding.

Document Understanding can complete each document within one second.

It can be applied to the healthcare industry to streamline the processing of medical documents. This includes scanning and applying OCR to convert physical documents into digital formats.

We can tune the AI component to improve the quality and accuracy of the documents being processed.

Typically, the AI process involves several steps. Firstly, it recognizes the template, which essentially identifies the input format being used. Secondly, it applies rules configured in a JSON file. This file specifies details like the expected fields for the recognized template, such as name, age, date of birth, and security address. The AI then reads and analyzes data from the specified location based on the recognized template. It applies the predefined rules to extract relevant information and search for the required fields. If the input doesn't match any known template, it employs more general search methods to locate the desired information. This is the core functionality of the internal AI component.

Of the 1,000 documents we process, 90 percent are completely automated.

My three OCR tools each incorporate three AI components. These components work in tandem, with the activity determining which AI component takes the lead. For example, if the first AI requires a minimum accuracy of 86 percent and encounters text with 85 percent accuracy, it passes the task to the next AI component. This next component employs a different OCR tool in an attempt to achieve the required accuracy. If it still falls short, the task is then routed to a human agent.

Our integrations leverage robust API connection services. A single, secure authentication method protects access to JSON files. Requests are sent and product responses are seamlessly handled. This API-based approach provides faster and more efficient integration compared to manual interface interactions.

UiPath now includes a document understanding AI components, eliminating the need for third-party solutions like ABBYY. This allows for quick and automated extraction, analysis, and template recognition of information from various documents. By training the system with diverse examples, the AI component can become highly efficient, similar to ABBYY's global OCR capabilities. This is a significant improvement, as it eliminates the need for additional integrations like ABBYY within UiPath projects.

I found UiPath Document Understandings' ability to understand varying document formats to be good. I had no issues with the templates I was using.

Using AI and machine learning can significantly speed up the recognition of new formats, templates, customers, or entities introduced into our process. It is particularly beneficial when dealing with low-quality documents, which often require manual intervention. By implementing a machine learning model at the beginning of the process, the system can learn from successful agent solutions and incorporate them into future scenarios. Clear feedback, including agent ID and task details, further enhances this learning process. As a result, machine learning can help save time, reduce costs, and improve overall process accuracy. This makes it a valuable tool within UiPath.

Less than ten percent of processed documents require human validation. However, when customers provide input that falls outside pre-defined templates the usual 90 percent of cases, the system cannot recognize it and fails to notify agents. This means a new template will be implemented to include human-agent collaboration when training AI models.

The validation process depends on the specific template and the data being acquired. If all data is extracted from the entire template, the validation process can take less than one minute.

The manual document process took us around ten minutes and now with UiPath Document Understanding, the process is within seconds.

Since implementation, human error has been reduced by 30%.

UiPath Document Understanding has helped save 50% of our time in instances when no human validation is required.  

The quality of the input documents is crucial because sometimes healthcare providers prefer automated processing rather than human review. However, this preference depends on the complexity of the resolution required and the document type e.g., JPEG, TIFF. I find the quality of the input documents as the most valuable part of the automation.

At the end of the process, we classify documents in our external application, similar to a CRM system. This classification is based on the documents stored in the new system. The results of classifying patient documents within UiPath Document Understanding need to be more accurate.

I have been using UiPath Document Understanding for three years.

UiPath offers excellent technical support due to its high-tech nature and the complex needs of its customers. This support is crucial for several reasons. One such reason is the customer success plan, which provides dedicated API support and a specialist focused on existing customers. This fosters close communication between the customer and UiPath, facilitated by two individuals who actively monitor and manage the customer's needs every week.

Positive

Previously, we used manual processes for all our tasks. We transitioned to UiPath Document Understanding due to its integration of AI components. It is more flexible to our needs.

We saw a return on investment within three months of deploying UiPath Document Understanding.

The pricing structure is based on the number of robots installed. While a single robot may suffice for some customers, others may require more depending on their processing capacity needs and desired turnaround times.

The cost per license is significant, approaching ten thousand dollars. While not inexpensive, for high transaction volumes, the potential savings can be substantial.

I rate UiPath Document Understanding an eight out of ten.

The main use case for UiPath Document Understanding is to automatize delivery notes, foreign invoices, and nonstandard documents in general that do not have an electronic format but are PDF. Most of them are delivery notes, invoices, packing lists, or shipping documents.

We are working with all the solutions from the UiPath, such as UiPath Platform, Process Mining, Test Cloud, Document Understanding, and now we are starting to experiment with Agnetic AI.

We do use Forms AI because it's a part of the UiPath package. We have used this in some projects. We are experimenting with Agentic AI, and we are at the very beginning of the journey. The first project started but is not completed yet. 

The main benefits that UiPath Document Understanding provides include an increase in productivity, as it takes over tasks from humans, especially in Italy, where finding qualified people is challenging. It frees up resources to engage in value-added activities and enhances quality and compliance, particularly for projects with clients that have large patent portfolios.

UiPath Document Understanding helps reduce human error during the working process. For example, while reviewing results with the client, we noticed an invoice had the date of tomorrow, so human error is an issue for compliance and audit reasons.

Action Center is the most user-friendly tool I've seen in the market for validating the documents and extracted data.

The main area for improvement for UiPath Document Understanding is pricing. They are cutting out the middle market because 60,000 pages are very high for that segment. To have that, they have to pay for this package, which doesn't make sense for many clients.

For handwriting and signature understanding, the quality has to be quite good. It can read something standardized, such as the handwritten bank paper for bankruptcy, but when it comes to shipping notes, it has problems. The handwriting performance is not always good, which is understandable.

I have been working with UiPath Document Understanding for more or less four years.

For stability, I would rate UiPath Document Understanding a ten because I have never had any issues with it.

I would rate it an eight out of ten for scalability due to cost reasons, as it does have an impact, but from a technological point of view, it stands well.

As a golden partner of UiPath, their tech support is a ten out of ten for us. We are also a golden partner for Microsoft, and I would not rate that a ten.

Positive

The setup process for UiPath Document Understanding is simple.

The small bundle that UiPath sells for Document Understanding is 60,000 units or pages. Clients need to come close to 60,000 pages a year or more.

The main competitor for UiPath Document Understanding is Microsoft Azure with Power Automate. I prefer UiPath Document Understanding because Microsoft Power Automate lacks good connection capabilities to automate end-to-end processes. It's time-consuming and often results in lost data due to a lack of infrastructure control setup by the client. This is a known problem that Microsoft is likely addressing.

Overall, I would give UiPath Document Understanding a nine because, despite pricing being a pain point, the solution is really great and yields good results. 

We use UiPath Document Understanding to comb through our documents, help us prepare reports, analyze the information, and later combine the information obtained into fact sheets, results, and reports that can be used for online planning and decision-making.

We implemented UiPath Document Understanding to build an accurate and intelligent platform. We needed a platform with the necessary tools to help us automate the whole process and reduce the errors involved in document Understanding, which arise from the manual processing of our documents.

We use UiPath Document Understanding for project management, which has also been implemented in customer service and production. So it's helping us in several business processes.

We have it deployed on the cloud and on-premise. We use the cloud for remote work, and it is used on-premises at the company workstation.

UiPath Document Understanding significantly streamlines our document-intensive processes through high accuracy, ensuring reliable and well-structured extracted information. It efficiently handles a wide range of documents, including both structured and unstructured PDFs.

UiPath Document Understanding helps us process structured and unstructured PDFs as well as handwritten, typed, and scanned documents.

We process 90 percent of our documents entirely through UiPath Document Understanding without requiring human intervention.

UiPath Document Understanding has significantly reduced the need for human involvement in document processing, allowing us to focus human resources on quality control. This automation has led to a time savings of approximately 60 percent.

UiPath Document Understanding does a good job handling various document formats, including handwritten and signature formats.

Machine learning capabilities are crucial and profitable because they significantly reduce the time spent on document data tasks and the number of errors encountered. As a result, machine learning is enhancing our productivity and enabling us to adapt to change more effectively.

Forms AI has proven reliable in predicting the content of packages within our documents, significantly reducing the number of errors in our product.

UiPath Document Understanding seamlessly integrates with various third-party platforms, allowing us to combine its capabilities with web analytics tools for enhanced data analysis and processing. Additionally, its easy integration with our CRM platform further optimizes our workflow.

UiPath Document Understanding has significantly benefited our organization by enabling remote work, reducing errors in document processing, and facilitating automation. We've eliminated the need for a large workforce dedicated to manual data entry, allowing us to operate efficiently with a smaller team. Additionally, the accurate data extracted through this technology empowers other departments to work seamlessly, transforming our organization into a well-oiled machine.

Initially, in an automation process, we have human validation every 48 hours, but now that UiPath Document Understanding has been so reliable, we can move to human validation every 72 hours.

The human validation takes three to four hours to complete.

UiPath Document Understanding has significantly reduced human error due to its accuracy.

Before implementing UiPath Document Understanding, processing ten documents took eight hours. Now, with UiPath Document Understanding, the same ten documents can be processed in less than thirty minutes.

The most beneficial aspects of UiPath Document Understanding for our data extraction needs have been the UiPath Academy courses and the free trial, which proved to be an eye-opener.

Machine learning has significantly benefited our organization by enabling the automation of numerous processes. We have successfully implemented machine learning to automate not only document generation but also report writing, analytics, and production. Furthermore, our CRM and planning processes have been streamlined through automation, which is made possible by machine learning. This technology has proven to be invaluable to our operations.

AI is helping us power robots and even expand our system capacity. It is the driving force behind remote working and also the driving force behind minimal human validation.

The most valuable aspects of UiPath Document Understanding are its accuracy, ability to automate processes end-to-end, and the availability of a free trial period to conduct a proof of concept and assess its accuracy and speed.

UiPath Document Understanding's training can be improved to focus more on how to handle the robots.

Extending the trial period for UiPath Document Understanding from three to six months would allow organizations to evaluate their capabilities thoroughly.

I have been using UiPath Document Understanding for two years.

I would rate the stability of UiPath Document Understanding seven out of ten.

I would rate the scalability of UiPath Document Understanding eight out of ten.

Technical support is the best. I can say it's reliable and dependable.

Positive

Before implementing UiPath Document Understanding, we processed all our documents manually.

The initial deployment was straightforward and took between two and four months to complete. Our team of ten technicians collaborated with two UiPath experts to successfully execute the project.

We have realized a significant return on investment by reducing errors by approximately 50 percent, which has also decreased the time and cost associated with document processing. By streamlining our operations, we have reduced the need for a large team of data experts and analysts, leading to internal cost savings and increased profitability.

I find the pricing to be fair. It's neither expensive nor cheap, but rather affordable, which makes the platform appealing to users.

I would rate UiPath Document Understanding nine out of ten.

We have over a hundred users because we have various workstations in various branches. In every branch, we deploy five workstations.

Any implementation challenges we encountered were due to the need to rapidly develop and execute our cyber training program. Given the increasing complexity of our programs and the critical importance of staying current with cybersecurity best practices, we prioritized upgrading our staff's knowledge. This new training likely contributed to our understanding of the platform's inner workings.

UiPath Document Understanding requires maintenance just like any other product that has version upgrades.

I recommend UiPath Document Understanding as the first choice due to its free trial, free training, and affordability. This platform will significantly enhance accuracy, leading to improved report generation, analytics, planning, and overall workflow productivity and growth.

Our primary clients are in the pharmaceutical and hospitality sectors. We recently developed a process using UiPath Document Understanding called 'Medicaid automation' to automatically download invoices and structured data from legacy systems. We then built an ETL pipeline to further process this information. Additionally, we have experience automating contract downloads and parsing data from contracts, even for structured data sources.

Automating processes using structured data is straightforward. However, in many cases, we need to involve human workers because data extraction is not very accurate. Therefore, we need a solution to integrate human input and structured data into the automation pipeline to minimize manual intervention. Additionally, when accuracy requirements are very high, we can also set up a user interface. Conversely, for less stringent accuracy requirements, we can create a fully automated pipeline. This is the core idea behind using UiPath Document Understanding. We aim to automate processes for functions like finance, resource management, and revenue management.

I work primarily in the pharmaceutical and hospitality industries. Within these industries, specific domains have different usage requirements. For example, in the pharmaceutical industry, I work with finance teams, and their focus on unstructured data includes tasks like invoice processing. Revenue management teams might leverage unstructured data for contract management, extracting key details for further use. Both finance and revenue management teams should consider how generative AI technology can streamline their workflows. In my experience, I've implemented an agent capable of extracting data from compliance documents and providing structured responses to users. Other use cases involved HR-related document queries and automated responses. Within the hospitality sector, I've worked on customer success and revenue management projects. On the customer success side, unstructured data related to loyalty programs could be analyzed for insights. We also explored automating email generation and streamlining tasks related to standard operating procedures. Revenue management in hospitality often involves contract automation. For a large hospitality company, I worked on a project to extract data from B2B contracts stored in Salesforce, pushing that information directly into their financial system. It's important to note that while I used unstructured documents as a foundation for these projects, not all of them specifically employed UiPath.

Using UiPath Document Understanding, we have successfully processed invoice documents and contracts. We are now expanding to handle various additional contract types based on specific use cases. This could involve rebate management, B2B interactions, or other scenarios. Additionally, we can handle other document types, such as per-case order documents and various SOP documents (compliance and operational). Finally, we have also explored applying Document Understanding to marketing materials related to sales rep automation, where product information can be leveraged to generate responses.

We use UiPath Document Understanding for many formats. The format of documents depends on their type. Invoices and purchase orders, for example, are considered semi-structured. This means they contain a combination of elements, such as tables, key-value pairs, and line items, but these elements can exist in different templates and with some variation between vendors. Contracts, on the other hand, are largely unstructured. While they may contain structured elements like tables, they also often include running text and information that is difficult to categorize in a predefined format.

We can fully automate the process for 95 percent of the documents. The more high-risk financial documents may need human intervention.

AI capabilities significantly reduce development effort for handling encrypted data while simultaneously increasing its overall scope. This allows me to achieve what was previously impossible with conventional APIs, even in advanced tools like UiPath. While UiPath also utilizes a broad model for data extraction, they are now expanding towards generative AI. Consequently, we benefit from improved extraction quality and the ability to extract data in the desired structure, all with minimal development effort thanks to AI.

When human validation is required, it takes one to two minutes for a five-page document.

Previously, reviewing a difficult document like a contract could take around 30 minutes, while an easier document like an invoice took 10-15 minutes. After automation, processing invoices got significantly faster, taking less than half a minute. This is because the complexity of invoices is generally lower compared to contracts. For contracts, automation was reduced to around three minutes. In simpler cases, the processing time could even be reduced to as low as one to 15 seconds.

The significant reduction in processing time leads to a notable decrease in human errors.

Our clients can see the time to value within the first three months.

The most valuable feature is key-value pair and table extraction. While we previously relied on UiPath and Amazon APIs, we've transitioned to generative AI for its superior performance on unstructured data. However, this shift presents a challenge: while UiPath and Amazon provided consistent output and value, generative AI outputs can vary significantly across different documents. This means we still need logic-based parsing for tables, even though they often share similar formats.

The UiPath APIs lack reliable table parsing.

The accuracy of document extraction depends on the document's original format. For rich text documents, the accuracy is generally good. However, scanned documents like PDFs or images present a challenge and often yield lower accuracy. Another challenge arises when dealing with multiple documents in a single image. This scenario is common in invoice automation, where a single image might contain several invoices. Furthermore, processing files containing multiple document types, such as multiple invoices in one file, can be problematic. Currently, the system assumes each uploaded file represents a single document or invoice, which is not always the case. To address these challenges, I propose enhancing UiPath Document Understanding to analyze the entire document, not just individual pages. This would allow the system to identify individual invoices within a multi-page document and assign extracted data to the corresponding invoice.

I would like custom key value integration instead of generic key values for extraction.

The cost of UiPath Document Understanding has room for improvement.

I have been using UiPath Document Understanding and other IDP products/APIs for four years.

UiPath Document Understanding is generally considered a stable product. If we encounter issues when using it in the context of a complex backend process, the problem is likely not with UiPath itself but rather with the specific process design and the components involved in its development.

The high cost of adding bots hinders our ability to scale UiPath Document Understanding. 

The deployment takes around five days for my team to complete.

UiPath Document Understanding carries a premium price tag, but its current technological capabilities may not yet fully justify the cost.

I would rate UiPath Document Understanding five out of ten.

UiPath Document Understanding requires significant ongoing maintenance, especially when it integrates with screens or utilizes user interface automation. This is because changes to the website structure are highly likely to cause these integrations to break. Backend automation, on the other hand, typically requires less ongoing maintenance. However, it is still recommended to dedicate resources to monitor the solution approximately 50 percent of the time. This proactive approach helps ensure uninterrupted business processes even after a proper initial development phase.

For automating cloud-native platforms, scripting often proves to be a more suitable approach compared to tools like UiPath. However, when dealing with legacy systems, UiPath might offer a more effective solution.

We use Document Understanding to process invoices, purchase orders, and addresses. It extracts data from a scanned structured document and converts that in a structured manner to a spreadsheet. Predominantly, we use Document Understanding for payroll, procurement, invoice processing, and also in the finance department. Document Understanding has multiple models for extracting data from receipts. Departments have different use cases, but it's mostly used on the finance side to extract invoice data. 

The volume of documents varies from customer to customer. When everyone starts using the product, they typically process between 10,000 to 20,000 in the first year. Once you've achieved a stable environment, you might reach around 500,000 pages in the second or third year. It depends on the project and the customer's budget because pricing is based on the number of pages. 

We are not talking about 100 percent data automation end to end. If our customers work with hundreds of vendors, they deal with various templates. If a new vendor comes in, there is a possibility that the model may not identify that particular document. It's also possible that the upload quality isn't that great because of a bad scan, so there is always a channel for manual processing to handle exceptions. 

When you implement Document Understanding, we may start with 40 percent automated and 60 percent manual. As it progresses and matures, the percentage gradually improves. We may eventually achieve 80 percent fully automated processing with 10 percent manual so that exceptions can be handled with the help of human intervention.

Traditionally, the operations team has done many of these activities manually. A human takes information from the document and enters it into the system. There are many challenges inherent in performing these tasks manually. One is human error. Also, a department might receive documents in the middle of the night, and no one is around to process them. Document Understanding enables round-the-clock support and automatic processing

The implementation is fast compared to other solutions.  Documentation Understanding is more flexible because it has the artificial intelligence to understand new formats when they come in. It may read the information automatically. 

The amount of human validation depends on the type of input document. For example, let's say we are extracting data from a passport. We had to extract data from the passport. The solution can properly scan the documents. There are 192 countries with different passports. The bots are already trained with all the different types of passports. 

However, if the solution encounters a new format for receipts, invoices, etc., it may not identify it properly. During COVID, we had to process PCR tests from different diagnostic centers with different formats, so we created a model to figure out whether the person had negative results, but if a different format came in from a new diagnostics center, we might not have enough data to train the model. 

It will scan correctly without human intervention if it's a well-established document type, but if there isn't enough training for the model, a human needs to come into the picture. Also, if the data input is not properly scanned because of its model input and all those things, and the system cannot understand it, then human-in-the-loop comes in. 

The time needed for a human to validate a document depends on the number of fields and whether the file is a PDF form, invoice, etc. If you only need to validate the invoice number, you can complete that in one or two seconds, but it will take more time to validate all the line items in every field.  

Document Understanding has reduced our processing time by around 70 percent. In some cases, it may be 90 percent. It obviously takes more time for an employee to process a document with three or four pages and pull the data from various places. Using a solution with an OCR component like Document Understanding is much faster. It frees up employee time because we're not using resources to punch in data manually. We can use those employees to do other things that require more human intelligence.

The solution has reduced human error because somebody previously opened this document manually and typed whatever they saw on the screen. Now, what is happening is the data extraction is happening systematically. If things look fine and the confidence score is high, it inserts the data into the system. If the confidence score is low, it shows the screen to the user and asks them to correct it. Instead of merely typing the information, the user verifies what the solution has done. It's easily a 30 to 40 percent error reduction, and the operational efficiency is drastically increasing. 

Invoice processing is the most valuable feature. Most of my customers use Document Understanding for invoice processing. That's one of the most common use cases. Typically, each customer starts their RPA journey with the finance department because that's the area where you can see the most benefit. 

It can extract checkboxes, signatures, and printed documents. The extraction and conversion of printed letters is perfect. Document Understanding can also process handwriting and signatures using a machine learning model on the backend. UiPath's product team is constantly training this model continuously. Every two weeks, they are training it with a new set of data, so the model is constantly becoming more mature. I've seen a tremendous improvement since 2021.

The solution's machine learning model gives it the flexibility to accommodate documents with varying structures. Before document understanding came along, data extraction was done using template-based extraction tools. They created a machine-learning model that can be retrained for any number of templates. If you are actually not using machine learning, you will not explicitly identify fields like "Bill To," "Ship To" etc. You have to tell it the location where you want to find data. 

UiPath has already trained its machine-learning model, which has seen these types of invoices and trained the solution. You're building a better solution that requires less effort to implement because the product does a lot of that work for you. The deployment time is faster. It's more intelligent than conventional coding, which is just listing a set of rules. Everybody needs flexibility. It's not enough to have a solution to handle documents in a particular format. Whatever you do, it should have the intelligence to understand data in a semi-structured format even though things are returning in a different manner than the one that came before. 

Document Understanding's handwriting comprehension is improving, but it's still not as good as printed documents. Machine learning models, in general, are becoming mature, but it's still not to a point where I will give it five stars. I may give it a two or three. It is still not advanced enough to identify whatever handwritten content you give to it. It can process handwriting, but you need a human to validate it. With more training, it will become more automated. It will be better by 2025, but it is still not mature enough

Similarly, there is still room for improvement in reading printed documents. Ideally, if you have a model, Document Understanding should be able to extract every field from there. That's what customers expect. 

We have used Document Understanding for about six months.

I rate Document Understanding seven out of ten for stability. It has some room for improvement. 

I rate Document Understanding seven out of ten for scalability,

I rate UiPath support four out of 10. Their support has degraded badly. Presently, they are mainly focused on closing tickets. They have trouble communicating with our business users and end up closing the ticket because they don't understand what the issue is. It's a problem because the customer will lose interest in the product if they are not getting technical support. 

Neutral

UiPath can be deployed on the cloud or on-prem. The infrastructure costs of hosting it on-prem are high. We have done many cloud deployments, but I would say it's not that easy. Normally, we subscribe to the SaaS version of UiPath and configure it for the customer. UiPath has a cloud instance, which is a SaaS offering. I believe Document Understanding is hosted in Azure, but the customer can opt for AWS, Google, etc. There are no restrictions if customers want to put it on their private cloud.

An on-prem installation takes about two or three weeks depending on the complexity of the environment. Cloud installation is plug-and-play, so you can get it up and running in a day. They need to issue the purchase order for it, and we get the licenses. Once the customer has the license, they can log into the UiPath Cloud portal, and it will be activated. Within five days, they can start using Document Understanding. After that, you need to build the automations for your use case. The development time frame depends on the use case. It requires maintenance because you must train the model continuously as new templates come in.

The price is high, so it will take you about a year and a half or two years before you break even. 

Document Understanding's pricing is reasonable for developed markets because manual entry will be unable to match the cost of automatically processing one page. However, you can get labor for much cheaper in developing markets like India. It's not easy to sell Document Understanding in markets where you can get workers who will do this type of activity cheaply.  

I rate UiPath Document Understanding seven out of ten. It's an add-on for UiPath, so it isn't a standalone solution. If you already have a license for another third-party solution for RPA, you should consider whether it's beneficial to switch. 

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. 

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.