UiPath Document Understanding Reviews

We use the solution for purchase accounting, where we need a lot of invoices from various vendors.

The solution allows us to continue with vendors whose information comes in correctly and to stop the automation for vendors with many items that are not clearly defined. The precision is not very high when there is a lot of text in the item table.

Sometimes, when the number of items is very large, the solution doesn't properly identify them. It's not 100% accurate, but it gives an 85% to 90% output. The solution doesn't work properly when too much data is on the table. Using 10 pages of data only for tables makes it difficult to collate the data.

We would like to have a better conversion of data, which is in tables and available on multiple pages. The same table is repeated with different items across multiple pages, which is challenging. We have to give similar keywords when indicating the second or third page. We should be able to use the same keyword for one document to identify the first page. From the second page onwards, the keyword should be done away with.

The repeated keywords on the second and third pages create problems because not many keywords are available on the second and third pages. It is a simple table-to-table repetition. The keywords, including the vendor's registration number, address, phone number, or email, are unavailable on the table pages.

Pages 2 to 10 are all just simple tables. The keywords are not available on those pages, and that's where the system gets more complicated.

I have been using UiPath Document Understanding for 3 years.

A person who knows the system can deploy it very easily and quickly without any difficulty or complication.

We take about 2 to 3 days to set up UiPath and install the UiPath Document Understanding programs we have developed. It takes time to create the vendor data and train the bot for each vendor. We spend about 40 minutes training the bot for each vendor.

The solution is a good investment. The investment of one year's license fees and development costs could be recovered in four to five months' time.

The product is not very costly in itself. It is part of the normal license. The solution’s cost increases for machine learning or artificial intelligence because we have to go for UiPath Orchestrator. UiPath Orchestrator's cost is very high, around $10,000 per year.

If somebody has a lot of vendors and about 100 invoices per day, we have to train the bot for various formats. We convert data from scanned PDF into a standard Excel table, including invoice numbers, purchasing order numbers, dates, and vendor names. The solution uses typed or printed data, not handwritten. It's difficult to capture handwritten data because different types of handwriting increase the errors in capturing the data.

About 80% of our organization’s documents are completely processed automatically. Sometimes, we don't have good data capture when the PDF is not of good quality. You need to have good-quality scans. Using the artificial intelligence or machine learning capabilities of UiPath Document Understanding is costly.

Integrating the solution with other ERP systems and applications is very easy. There are a lot of commands specifically for SAP. Initially, almost every document was verified by human beings. After gaining confidence and segregating the vendors who were giving out correct results, we stopped human intervention and started running on the bot completely.

When there were about 200 invoices per day, we employed about 5people to process the invoices manually in the ERP. When we introduced the UiPath Document Understanding bot, it could process faster and reduce the number of people required from 5 to 2. The solution helped reduce human errors and made processing in time possible, thereby avoiding delays.

Users should take the solution as an opportunity to reduce costs and not expect 100% results. Expecting 100% results would make it a failure from day 1 because it will not give 100% results. It will give you 80% accurate and 20% jumbled details because of the various complications arising from lengthy or unclear documents. If your expectation is reasonable, your success rate will be high.

Overall, I rate the solution an 8 out of 10.

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.

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.

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.

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.

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

Document Understanding can complete each document within one second.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I have been using UiPath Document Understanding for three years.

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

Positive

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

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

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

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

I rate UiPath Document Understanding an eight out of ten.