UiPath Document Understanding Reviews

Our primary use cases for UiPath Document Understanding are processing invoices for five different clients and importing/exporting documents to extract vital information, mainly from unstructured documents. These five clients are from various industries, including transportation, scientific research, food services, and clothing.

I processed 400 documents per day for one client and 20 documents per day for the second client.

The documents processed were in PDF format.

90 percent of the 400 documents processed per day for a single client were fully automated. However, only 50 percent of the 20 documents per day were automated due to their greater level of unstructured nature. As a result, the remaining 50 percent had to be sent to the action center.

AI and machine learning for Document Understanding are game changers. Machine learning was helpful in identifying the various areas of the documents from which I needed to extract different types of information, making the process quicker.

The default model didn't work for me because I needed to extract information from documents written in French. Thus, I had to create my own model using AI, which proved to be exceptionally beneficial for handling the French text and its accents.

Integrating UiPath Document Understanding with other systems and applications in our environment works well. The solution was able to retrieve the PDF document from an email, extract the details using the command, and apply those details to an application, saving a substantial amount of time.

UiPath Document Understanding serves as a safeguard in relation to cost and time savings, as it diminishes the manual workload for employees and minimizes errors. For a job that took a human eight hours to complete, the bot was able to do it in three hours.

The extent of human validation needed for Document Understanding varies for each client. For one client, no validation was necessary as the solution effectively extracted all required information from the documents. However, for another client dealing with diverse document types, errors occasionally occurred due to character placement. This was particularly evident when email addresses were positioned differently, some at the top and others at the bottom of the documents, posing challenges to the robot's detection capabilities. In such instances, a validation process was implemented. Every seven days, ten percent of the batch would be sent to the Action Center for validation.

The time saved with UiPath Document Understanding is exemplified by an organization that previously had to spend three days manually extracting information from 400 documents every month. However, with UiPath Document Understanding, this task now only takes two hours.  

Machine learning is the most valuable feature of UiPath Document Understanding.

I encountered difficulties with UiPath Document Understanding in determining the appropriate OCR to use for certain files. These files required extracting both the company logo from the page and the digitized text, posing a challenge. The OCR engine faces difficulties when processing signatures and scanned documents with unclear handwritten text.

The robot faces difficulties in recognizing when there are multiple documents on a single page. This necessitates manual intervention by first splitting the document and then re-digitizing each part separately. 

I would like a split feature in a future release of UiPath Document Understanding.

I have been using UiPath Document Understanding for one month.

UiPath Document Understanding is extremely stable.

UiPath Document Understanding is scalable.

The technical support responds promptly and strives to resolve our issues quickly. However, there is room for improvement. For instance, we encountered an issue with the Action Center, and the support team was unable to determine the cause for three days. Eventually, someone from my team resolved the issue.

Neutral

The initial setup was a bit complex.

I also assessed FlexiCapture, but I discovered that UiPath Document Understanding was more user-friendly. Coming from a scientific background, I found that UiPath Document Understanding offered a more logical and less complex solution.

I would rate UiPath Document Understanding nine out of ten.

It took me one week to study UiPath Document Understanding and to present it to my organization.

I realized the benefits of UiPath Document Understanding once I completed my first project.

The quantity of personnel needed to maintain the solution relies on each project. In the most recent project I participated in, we needed a total of two individuals, one of whom was an administrator from our team.

When using UiPath Document Understanding, always ensure that the number of structures is the same each time to prevent errors.

I believe that utilizing communication mining would be more effective with the AI Center.

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.

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.

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 have processes for purchase orders. We need to analyze the content of these files and some invoices. Based on that, we are able to perform qualifications and post them to the CRM system. Overall, we call this our invoice control process.

We wanted to optimize the performance, meaning the time the process takes, and the quality. We had some problems with the quality of transferring the data because people would make mistakes. If they were doing 80 documents per day, there was a high possibility that they would forget to look for some information or they would copy and paste the wrong fields.

The main benefit for us has definitely been a faster process. We have sped up the process of analyzing the data. A second one is the improvement in the quality of our implementation.

In our organization, we are now at 30 percent of our documents being completely processed automatically. And in terms of human validation required for output from Document Understanding, we need it for 15 percent of the cases. We have decreased the time needed for such processing by 70 percent. And regarding human error, we have seen a decrease of about 60 percent.

One of the most valuable features is the intelligent recognition of the fields. The algorithm is able to recognize them based on the pattern. Also, the machine learning model enables you to use predefined solutions. The machine-learning capabilities of the solution are very cool. I really like that part, and I hope it will be developed even more in the future. I'm really excited to see how it will develop.

Integrating Document Understanding with other systems and applications is very easy if you already have some background. It just requires some mature developers to do so, and we are just about at that stage. It's very user-friendly, but the documentation could be a little more detailed. Besides that, it is fine.

With handwriting, we had a problem. It wasn't able to extract it because we have handwritten documents in Polish, and that language is not supported at this time.

Also, the documentation should be more clear, or better training should be provided.

We have been working with UiPath Document Understanding for about eight months.

Scalability is very easy to manage. It's very natural. The only thing that changes is the number of processes and the number of licenses. The more money we save with it, the bigger we will scale it.

We are using it across four departments.

I really like their customer support. They are very responsive.

Positive

We had ABBYY. The advantage we see with UiPath is that it's easier to implement things with RPA. Some of our teams are still using ABBYY, but our team has declined to use it anymore for automation.

I'm the system owner and architect, so it was on me to set it up. The initial setup was very complex in the sense that we had to play with the firewalls and other things to make it work.

It included the entire cloud, not only Document Understanding. It was very tricky to do it the correct way. We had to do a lift-and-shift. We updated the on-prem environment to the latest possible version and then copied the entire base to the cloud. Later, we upgraded each process, and, once the process was upgraded and ready to work in the cloud, we moved it to the target tenant.

At this moment, it does not require any maintenance.

We did it ourselves. We had a team of 20 people, but that's because we have a lot of processes.

We will need to have the solution for at least one year to have a clear view of ROI. It's the same for time-to-value with the solution.

It's expensive, but you can reduce the price per license by getting more licenses. Overall, the pricing is okay.

One area for improvement would be a different licensing model. Right now, we have to assign a license to allow a user to do validation. We think that standard access to Orchestrator should allow a user to validate.

Definitely talk first with a UiPath representative to get someone who will take care of you and the implementation. Do not waste your time reading through the documentation because it's very messy.

The primary use case for UiPath Document Understanding is to identify and classify documents, extract metadata, and use this data in automation workflows. This can be particularly useful in HR processes where various documents need to be submitted during hiring, such as graduation certificates, IDs, etc. UiPath Document Understanding helps classify these documents and extract the necessary data to process internally or initiate workflows.

UiPath Document Understanding is a tool that assists with processing documents containing various formats, including tables, handwritten text, and checkboxes.

We leverage machine learning and artificial intelligence to train UiPath Document Understanding on various documents. This integrated capability significantly simplifies extracting and comprehending information from these documents within the platform.

We can incorporate human validation into the training process to ensure accurate data classification and extraction. This valuable step, while adding a few minutes to the process, allows for human oversight and correction, ultimately improving the reliability and quality of the results.

UiPath Document Understanding helps reduce human errors, especially in data entry functions. 

By automating processes, UiPath Document Understanding can save approximately 70 percent of the time.

Customers realize value quickly with UiPath Document Understanding, typically seeing results within a few weeks of implementation.

UiPath Document Understanding offers valuable features like Arabic language support, which is crucial for effective communication and automation in the Arabic-speaking world. Furthermore, its integration with advanced language models leverages AI to quickly understand and classify document data, improving efficiency and accuracy in processing information.

One area where UiPath could improve is by including pre-trained models for general-use documents specific to the Middle East. This would enhance the platform's utility in the region by allowing users to more effectively automate tasks involving documents in Arabic and other Middle Eastern languages.

I have been using UiPath Document Understanding for almost five years.

The premium support UiPath offers is speedy and satisfactory. However, basic support may be somewhat limited.

Positive

For cloud deployment, the initial setup is fast and straightforward. On-premises setup, however, can be complicated and requires more effort.

Deploying UiPath Document Understanding in the cloud takes only a few minutes, while on-premises deployment requires three to five days.

UiPath Document Understanding is considered a bit expensive compared to other options like Microsoft Azure, which can offer similar quality at a more affordable rate.

I would rate UiPath Document Understanding seven out of ten.

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.