UiPath Document Understanding Reviews

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.

Our client has PDF invoices and we use the solution to extract the details from them. We are using it in finance and health care. We have about 16 templates that we process now. The data is in semi-structured format and we mostly process things like signatures and tables. Out of the 16 templates, about 12 are completely processed automatically.

It has helped us automate finance statements and invoice billings.

Another benefit is that it has mostly helped reduce human error. We have a criteria of 75 percent matching. Out of 10 PDFs we have been getting eight PDFs with at least 75 percent matches. It has also helped free up staff time.

The taxonomy and Validation Station are among the most helpful features for us. If anything is extracted incorrectly, we can manually extract it there.

And we have included the AI Center for our customers to interact with PDFs to be extracted. Based on the approval or rejection feature, our customer can determine which kinds of PDFs they can automate.

I also like the table extraction feature. UiPath is very good with structured data.

Handwriting is more complex. We have not been able to get handwritten signatures correctly extracted in different languages. Our customer is in Dubai, and the solution cannot accurately process signatures in the local language. But it is a great tool for handling structured and semi-structured formats.

Another of the disadvantages is that we cannot include another tool. For example, with ABBYY extraction, we can integrate the process with any other product. We can integrate Document Understanding using JSON templates, but it is a bit of a complex model to extract the data from the JSON.

There is also room for improvement in long-running table extraction. If a table continues for more than 10 pages, in some cases, we have observed that it only extracts six or seven pages and skips the last pages.

I have been using UiPath for about 10 years.

Overall, the product is stable.

In our case, the use of Document Understanding is restricted to a particular group of users, around six or seven people.

The technical support from UiPath has been pretty good in the last year. It has been a very good experience. 

We used Azure DevOps for the deployment and we faced some issues regarding the deployment with UiPath and Orchestrator. We had a very good response from the UiPath technical team.

There is some room for them to improve the speed of the response because we often used to get late responses. But the resolutions are good.

Positive

We were using ABBYY, but it is more like a developer's tool with everything a developer needs for extracting fields. But we can train and retrain Document Understanding. In that way, I feel it's a better tool.

The pricing is reasonable.

As for additional costs, the solution is based on OCR, and sometimes the OCR cap is exceeded. It's not a major cost. Per month, we will have two or three scenarios like that. With ABBYY, once the cap was reached, we had to wait until the next day to use it again.

We did not evaluate other solutions. Using Document Understanding was a requirement from the client's side.

In terms of human validation for Document Understanding output, we have a limit of 75 percent correct scenarios. If it is below 75 percent, the user will be notified.

The solution doesn't require any maintenance unless the client requires more fields to be extracted. Only then are there changes that I need to make.

My advice is that if you are starting to learn about Document Understanding, you need to learn more about the taxonomy and what fields you are extracting. You need to have clarity on which position you are extracting, as it mostly depends on the position.

We primarily use UiPath Document Understanding for finance processes, covering both transactional procedures and reviews. One recent example involved streamlining the onboarding process, including pre-boarding, onboarding itself, and post-onboarding follow-up. The company typically requests various documents from applicants, which are then processed manually. However, due to variations in country-specific standards and requirements, HR personnel often spend significant time handling these documents.

Our solution involves creating a seamless online portal where applicants can upload their documents. These documents are automatically screened by the system and directly uploaded into the company's EFP system. This significantly reduces manual work for HR and finance teams. Similar automation applies to processing invoices from various suppliers in different formats. We leverage machine learning tools to train the system to read documents with varying complexity levels.

Essentially, the system mimics how an HR professional would process documents, capturing their knowledge and integrating it into the automated workflow. This reduces processing time and workload for both the company and its clients. Our focus lies on automating tasks within well-defined contexts, making us less involved in product development activities at this stage.

Initially, our clients were primarily interested in UiPath Document Understanding out of curiosity about its potential. Their main focus was on automation, but we also engaged in discussions about the broader benefits, such as time savings. We highlighted that a 30 percent time reduction allows them to focus on tasks with higher value. However, what I found even more crucial was the impact on lead times. Manual processes often lead to work stoppages, delays, and roadblocks. Automation, even partial, can significantly reduce lead times. For example, a task that previously took five weeks can now be completed in just a few days. While security concerns may necessitate some manual intervention, such as allowing the head of HR to retain some oversight, the overall process becomes more streamlined over time.

Most document processing is automated, improving efficiency and ease, especially in back-office transactions. However, areas like marketing, where business plans require creativity and flexibility, remain manual for now. Where documents are stored, and manipulated, and data needs to be extracted and distributed across various systems, the process is often cumbersome. Traditionally, someone would manually open each system, which is time-consuming, especially considering most companies have hundreds of them. This is where tools and systems come in, able to connect across platforms, read data from various sources, and make interpretations. The level of automation depends on the company's maturity. Sometimes we leverage their existing data, while other times we implement techniques to extract more insights. Ideally, we'd be able to predict and anticipate future needs, but for now, with clients, we're primarily focused on analyzing data and helping them automate their processes. This is the first step.

The volume and types of documents we process with UiPath Document Understanding vary depending on the client. For smaller companies with a few hundred employees, the needs are different than for large international corporations with thousands. These international clients often have diverse locations with varying processes and systems, making automation more challenging. In HR departments, for example, the sheer number of applicants and their associated documents can be immense. Ensuring accuracy is crucial, as mistakes can have significant consequences. Finance departments also present unique challenges, as data might be hidden or incomplete. This requires them to be at a certain level of maturity to benefit from automation effectively. The complexity of documents is another key factor. While machine learning can handle many documents, it has limitations. Some documents might be too time-consuming to train on, making the investment in automation impractical. This can leave a portion of documents requiring manual processing. Overall, UiPath Document Understanding automates the processing of the majority of documents we handle, around 80 percent. However, for the remaining 20 percent, manual intervention is still necessary due to document complexity, data limitations, or training time constraints.

UiPath Document Understanding helps us extract data from various document formats, including tables, handwritten content, checkboxes, and barcodes. However, poorly legible documents present a challenge. Automating 100 percent of documents is currently impossible due to diverse languages and handwritten sections. Our current approach categorizes documents into easy, medium, and complex based on difficulty. We prioritize easy documents as complex ones require significant time investment with uncertain results. Unfortunately, machine learning for document processing can be time-consuming. We prioritize documents based on return on investment. For example, if we have 10,000 documents, we might skip two unique ones, even if theoretically similar to others. If only two or three data points are needed, but the structure drastically varies, processing might not be worthwhile. Imagine a 10-page phone bill invoice with a minimal value of €10. Investing time in such documents offers a minimal return. Therefore, we focus on documents offering greater value.

Around 70 percent of the documents are processed automatically using UiPath Document Understanding.

UiPath excels at connecting with various systems compared to some competitors. This is crucial when promoting it to clients, as in our case with our UiPath partnership. All our developers have UiPath training, and we strongly believe in its capabilities. However, internal legacy systems within companies can pose challenges. For example, a client with an EFP system they plan to replace might hesitate to automate now. Integrating UiPath with basic IT infrastructure is essential, and frequent system changes demand flexible solutions. While UiPath is adaptable, we need to demonstrate its compatibility with various systems to gain client buy-in. This will make them more open to automation. It's important to remember that company maturity levels influence their automation openness. While UiPath has no control over that, adapting to ever-changing environments requires flexible systems. By showcasing UiPath's ability to work with different systems, we can overcome client hesitation and secure their trust in our proposed automation solutions.

It typically takes clients about a month to see the benefits of UiPath Document Understanding. We start by showing a demo. We often use the UiPath website itself for inspiration, and we also consult with UiPath staff to see if they have any pre-built demos for specific areas, such as onboarding. We create short, simple videos tailored to their needs and showcase them to both HR and IT personnel, giving them a glimpse of the solution before implementation. While deployment ultimately requires its timeline, we can typically craft a process description within a couple of weeks, allowing for a swift rollout. The tools themselves are relatively quick to use. In my experience, the main bottleneck usually lies within the client organization itself. Functional teams are often busy, have competing priorities, and sometimes change their decisions. Navigating these internal dynamics can be time-consuming. The actual development time for tasks like process mapping, decision-making, and technical implementation is relatively short, typically measured between 10 to 20 days. However, building consensus, convincing stakeholders, and developing a compelling business case can take considerably longer. Internally, clients often encounter both promoters and detractors – individuals who welcome or resist change. These internal dynamics are often the biggest hurdle. However, once the decision is made, we can quickly create a targeted demo showcasing the added value UiPath Document Understanding can bring.

On average, human validation takes just a few minutes. Additionally, the number of full-time equivalents was reduced by 30 percent - that's a significant achievement. Lead time has also decreased dramatically, much more than the FTE reduction. A small department of three people can now do the same work with two, freeing up one person for other tasks. It's important to note that lead time reduction depends on the specific case. Theoretically, in a perfect scenario with seamless workflow, automation tools operating 24/7, and no disruptions, a five-fold decrease in lead time is possible. However, real-world scenarios often involve unforeseen issues requiring manual intervention, limiting the maximum achievable reduction. Still, significant lead time reductions are attainable through consistent improvement efforts.

When it is done well we can reduce and improve the accuracy through automation helping to reduce human error.  

For me, the most valuable aspects of UiPath Document Understanding are its time efficiency and minimal human intervention.

UiPath Document Understanding's ability to handle diverse document formats, including scans and signatures, needs improvement. While it can be learned from various examples, the accuracy suffers when presented with poorly scanned, multi-generation photocopies. Companies often struggle with repeated scanning and photocopying, leading to documents illegible even for humans. While the software can be trained on various signatures and handwriting styles, it requires a significant number of high-quality samples for optimal performance. This training process necessitates time and effort, and human verification often remains necessary. Initial excitement about the automation potential can be dampened by the reality of data quality limitations. Collaboration is key. While the tool has limitations, companies must also invest in providing high-quality training data to optimize results. Simply expecting the software to adapt without proper resources is unrealistic. Improvements in both tool capabilities and data quality are needed for truly reliable document understanding.

I have been using UiPath Document Understanding for one year.

UiPath Document Understanding is stable.

Up to this point, we have not encountered any scalability issues for UiPath Document Understanding.

Both technical support and the commercial team need to actively listen to clients. Simply pushing products onto them is ineffective and often unwelcome. We frequently find ourselves caught in the middle, mediating between UiPath and clients with differing priorities. This lack of unified communication creates the impression that neither side is truly listening to the other.

It's crucial to pay close attention to clients' specific concerns, as their needs often extend beyond a single product. They may have broader goals and considerations that we are unaware of. By actively listening, we can gain valuable insights and build stronger relationships.

Neutral

We implement the solution for our clients.

One of the biggest challenges we face with UiPath is the pricing structure. It's often opaque and difficult to understand the true cost involved. This makes it hard to have transparent conversations with clients, as any lack of clarity can raise concerns about hidden fees or manipulation. Our goal is simply to understand the pricing ourselves, but the complex structure creates an unnecessary obstacle.

Thankfully, the UiPath team recognizes this issue and is actively working with partners to improve communication and transparency. We've seen initiatives from their Chief Marketing Officer aimed at strengthening partner relationships, specifically addressing the pricing concerns. While they often propose pre-defined packages designed to sell bundled functionalities, these aren't always appropriate for every client's needs.

We've experienced situations where clients express interest in a specific solution but decline the complete package. When we relay this feedback to UiPath, they sometimes counter with larger, multi-year contracts that significantly exceed the client's budget and desire for a trial period. This makes it challenging to demonstrate the value of UiPath in a way that aligns with the client's initial request.

Ultimately, what we need is a more flexible and transparent pricing structure that allows clients to start small, experiment with specific solutions, and scale up as needed. This would significantly improve our ability to have open and honest conversations with clients and build trust in the UiPath platform.

We should pay closer attention to listening to our clients. In my experience, I've observed conversations between UiPath and clients where they clearly explain their needs. While UiPath naturally wants to sell larger deals, they should prioritize active listening. The client may not always be 100 percent accurate, but pushing big deals is counterproductive.

UiPath, of course, wants to secure larger deals with longer contracts. This is understandable, as automating for only 3-6 months wouldn't be ideal. However, clients often want to pilot tools first. They need to justify the investment to internal stakeholders and prove the added value. Selling them pre-packaged solutions designed for other clients, particularly those in different regions or industries, often proves ineffective.

Clients seek adaptable solutions that fit their specific context. Large companies with thousands of employees have access to numerous competitors. We can't assume they won't explore other options. While polite on the surface, they're actively seeking the best solution for their needs.

While UiPath offers excellent solutions, they sometimes fall on the higher-priced end compared to alternatives like Microsoft, which might appear more affordable on the surface. Clients who already have established contracts with Microsoft might be more inclined to choose their products unless we can effectively demonstrate the unique value proposition UiPath offers. This goes beyond mere cost and includes aspects like security, which is paramount in Switzerland. Clients often require data control and prefer on-premise or regulated cloud storage options.

Data security is a major concern for many companies. Cloud solutions, while attractive, aren't always universally accepted. Factors like industry regulations and legal requirements often dictate data storage options. Defense, oil and gas, and other sensitive sectors have stricter constraints imposed by their legal departments.

In conclusion, while larger deals are desirable, focusing on active listening and adapting solutions to each client's specific needs is crucial. Highlighting unique value propositions beyond cost, such as robust security and data control options, will differentiate UiPath from competitors and win over clients.

I rate UiPath Document Understanding eight out of ten. In my experience, UiPath Document Understanding stands out as a superior solution compared to other document processing tools I've encountered.

The future lies in leveraging artificial intelligence or machine learning to accelerate progress across various landscapes. Recently, we encountered a situation where technicians presented a series of documents with a medium-high level of complexity. They proposed running a machine for a month to process them, but this was unrealistic for management. The lead time for new document processing needs to be appropriate. While processing in a day is acceptable, dedicating a team for a month to a single document type is impractical. Scaling up operations requires flexibility and adaptability. For example, testing tools in one country and then scaling to another presents challenges due to different environments and document types. This necessitates a more powerful machine with faster processing and the ability to handle diverse document formats. Ultimately, such advancements will significantly improve the system's efficiency.

The amount of human validation required for UiPath Document Understanding outputs varies based on the client. While some clients may hesitate to trust complete automation, others recognize its potential. However, for sensitive tasks like contract reviews, they wouldn't send documents to external candidates without human verification. Therefore, the initial steps involve clarifying expectations with the client. During implementation, adjustments might be needed, and even after the tool is operational, some human involvement is typically built into the process for added confidence. Over time, as trust in the system grows, these checks can be gradually reduced. However, eliminating all checks could be risky.

Most of our clients prefer on-premise deployments and for any Cloud deployments, the servers must be located in Switzerland.

Many organizations fall into the trap of automation neglect. They implement new tools or processes, only to abandon them later due to lack of maintenance. While initial implementation may bring a sense of accomplishment, this approach ultimately fails to deliver business value. Beyond simply implementing technology, user adoption, and ongoing maintenance are crucial. IT systems should be seen as part of a continuous improvement journey, not one-time solutions. Analyzing processes, strategy, and people allows for ongoing optimization, where digital tools empower improvement instead of creating isolated interventions. To avoid the common pitfall of neglected automation, consider establishing a Center of Excellence. This central team can provide support, guidance, and expertise to local users, ensuring the system functions effectively and delivers lasting value.

Before organizations implement UiPath Document Understanding, they need to clearly define their desired outcomes and understand that successful implementation requires both adapting their documents and refining their processes. While it's tempting to see automation as a magic bullet for fixing dysfunctional processes, it's crucial to address underlying issues beforehand. This involves simultaneous work on process improvement and document optimization. For example, when I consider the HR department I worked with. The key was to first understand their existing workflow through process mapping. Then, we identified bottlenecks and potential improvement areas based on their feedback. While developing the automation, we also reviewed their document structure and eliminated unnecessary documents. This combined approach ensured that the implemented process and tools were efficient and streamlined. Simply speeding up a flawed process with automation often proves ineffective, leading to user dissatisfaction and a perception of failure. The problem doesn't lie with the tool itself, but rather with the lack of skilled staff who understand the processes they manage, their purpose, and the specific complexities of the company and its unique environment.

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

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

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

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

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

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

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

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

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

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

Document understanding has helped free up staff time.

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

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

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

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

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

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

UiPath Document Understanding is scalable.

The technical support was helpful.

Positive

The initial deployment was straightforward.

Two people were required for deployment.

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

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

I would rate UiPath Document Understanding six out of ten.

We use UiPath Document Understanding for two purposes: extracting information from medical certificates issued by a prominent university in Singapore and processing invoices for a client in the logistics industry within their ERP systems.

We implemented UiPath Document Understanding to significantly reduce the substantial mailout effort. Approximately 20 full-time employees were previously dedicated to these processes, but after implementation, we were able to halve the number of full-time employees required.

We are capturing the header line items, which include the account number, invoice number, invoice date, and the line items: quantity, line item description, unit price, taxes, item number, and ZIP codes. This is a sales sector document. The medical certificate is an untested document, and we need to capture specific dates, the doctor's medical certificate number, and the student's name. We also need to check whether a checkbox is checked. There are no handwritten documents to extract.

Around 80 percent of our documents are processed 100 percent automatically.

Before implementing Document Understanding, the average time per invoice for manual processing, including invoice scanning and data extraction, was 15 minutes. Following automation, the processing time has been reduced to six minutes, with the specific duration varying based on the number of features on each invoice.

Document Understanding has helped reduce human error by 70 to 80 percent.

Document Understanding has reduced staff time by nearly 50 percent.

The best feature is pre-labeling, as it eliminates the need to manually label each data point. This saves a significant amount of time and effort. Additionally, the generated text is integrated seamlessly into the tool, making it easy to use. The documentation is also very clear and concise, making it easy to get started with the tool.

Over the past few years, I have observed that the invoice model consistently improves with each new UiPath release. There is still room for enhancement in capturing line items from invoices. This is one of the areas where I believe we can achieve near-perfect data capture. Unfortunately, the current accuracy rate for capturing line items is between 50 and 60 percent. This necessitates manual two-way matching, which is time-consuming and inefficient. I believe UiPath Document Understanding can still improve in this area, but overall, it is moving in the right direction.

Despite advancements in artificial intelligence and machine learning, there are lingering concerns about data privacy and security. These concerns can have a significant impact on users, particularly in terms of geographic restrictions and data policies.

The accuracy level we receive does not justify the price, as many competitors are offering much lower prices.

I have been using UiPath Document Understanding for three years.

While the stability is improving, it still needs to be enhanced in terms of model learning.

UiPath Document Understanding is scalable, but there is an aspect of training that requires attention. The model should be trained with a specific type of invoice to ensure optimal accuracy. For instance, if the invoices are in multiple languages and formats, the post-model training results may not be as effective as compared to training the model with invoices in a single language or two languages at most.

The technical support is good.

Positive

In the past, we used IQ Bot from Automation Anywhere; however, its output fell short of UiPath Document Understanding's capabilities. This discrepancy stems from the sheer size of the model we currently employ in the collection. UiPath Document Understanding's effectiveness is attributable to this factor. Additionally, UiPath offers superior analytical reporting capabilities, whereas Automation Anywhere falters in this regard. With Automation Anywhere, we were required to create multiple models, whereas UiPath allows us to utilize a single model for a collection of invoices with similar structures.

The deployment itself was straightforward. However, the deployment of the automation may have been more complex. In terms of the Document Understanding skills required for deployment, the process is straightforward. It doesn't require a lot of effort and can be completed in a day or two. For an experienced or certified individual, the deployment can likely be completed within a few hours.

To complete the deployment, a team of three people is required to work together.

The return on investment is seen within the first year of using the solution. 

UiPath Document Understanding is priced high compared to its competition.

I would rate UiPath Document Understanding nine out of ten.

Our clients experience time to value after approximately four months of usage because, initially, it takes some time to become familiar with the models and begin to see results.

The number of people we have using the solution is specific to the AP team or data finance team. Currently, we have two teams working on the solution.

Document Understanding requires ongoing maintenance in the form of model retraining. In the event of any encryptions, we may need to provide validation to the user. Additionally, we need to ensure that our models are regularly retrained.

Organizations need to carefully evaluate the scope and requirements of their Document Understanding initiatives. While existing Document Understanding models have demonstrated capabilities in specific invoice formats, it is crucial to test their performance across a broader range of invoice types. I recommend conducting a pilot test using a sample of 20 diverse but similar invoices to assess the models' accuracy and applicability.

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 clients use UiPath Document Understanding for their purchase order creations.

We need to process invoices received from vendors. This involves posting the data to SAP and creating a virtual file. To extract data from the vendor's PDF documents, we utilize UiPath Document Understanding.

The documents we process using UiPath Document Understanding are invoices and purchase orders.

The documents are in PDF format. Some documents include handwriting and screenshots.

Around 80 percent of the documents processed are completely automated without any human intervention.

UiPath Document Understanding helps handle signatures.

The call center teams automated a process where they used to manually identify configuration items in service notifications submitted by users. This manual process required a team of more than three people to analyze over 70,000 records per month. To address this inefficiency, we implemented Forms AI to automate the process. This automation has directly benefited end users.

UiPath Document Understanding has streamlined invoice processing. Previously, processing invoices was a time-consuming manual process. Employees had to read each invoice, create corresponding entries in SAP and CRM systems, and then route them to accounts payable. This required multiple resources. UiPath Document Understanding automates these tasks, reducing processing time and errors.

In the past, a team of more than 10 people was required to manually process purchase orders. Now, thanks to UiPath Document Understanding, only a few people are needed to validate the complete information and resolve any issues.

Before UiPath Document Understanding, we used over eight resources to process documents. Each resource could only handle around 20 documents per day, limiting our total daily capacity to 160 documents. However, since implementing automation, we can now process over 600 invoices daily.

UiPath Document Understanding helps reduce human error by over 90 percent.

UiPath Document Understanding has freed up staff time to work on other projects.

Our clients are satisfied with the time to value.

The most valuable feature in UiPath Document Understanding is the identification of the fields column in the PDF documents.

UiPath Document Understanding has challenges with handwriting and screenshots.

I have been using UiPath Document Understanding for 2 years.

I would rate the stability of UiPath Document Understanding 8 out of 10.

I would rate the scalability of UiPath Document Understanding 8 out of 10.

We have a dedicated account manager as our primary point of contact for any support we require.

Positive

We faced some challenges with the initial deployment and had to get support from the product team.

Our clients saw a return on investment after the second year of use.

While Robotic Process Automation tools can be expensive, UiPath Document Understanding is no exception. However, the long-term benefits often outweigh the initial cost.

I would rate UiPath Document Understanding 8 out of 10.

The integration of AI in UiPath Document Understanding will enhance its ability to read screenshots and handwriting within PDFs in the future.

We're currently working with several internal clients across various industries, not just the financial sector. We're expanding our reach to assist them with both compliance and audit matters. By targeting a wider range of clients, we aim to help them implement effective tech ops practices.

Currently, we are using UiPath Document Understanding in our client's finance department.

We have a 4 person support team that monitors and maintains UiPath Document Understanding.