UiPath IXP Reviews

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Neutral

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Positive

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

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

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

The cost is considerably high for UiPath Document Understanding.

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

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

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

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

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.

The use case is related to invoice processing. We extract details from the invoices, and after those details are extracted, we use the UiPath RPA bot to process those invoices.

We have installed it on the client's machine and integrated it with the UiPath RPA bot. Document Understanding extracts the details from the document, and the UiPath RPA bot picks up this data and puts it in the system to process the invoice.

We are processing 2,00,000 to 3,00,000 invoices received from the vendors. They have structured data. There is no barcode on the invoice. There is structured data with date, invoice number, fax code number, amount, etc. It is a printed invoice.

The artificial intelligence or machine learning (AI or ML) capabilities of Document Understanding are very good. It reduces the development time. We can extract the required details quickly and with far more accuracy.

Document Understanding works very well with structured documents in different formats. I have not tried it with unstructured data.

About 70% of the invoices are completely (100%) processed automatically. The human validation required depends on the logic that we write. If the match is more than 85% to 90%, we do not require any human validation. If it is less than 85%, a few things are required from a human. The human validation process does not take more than a minute per document.

The average processing time used to be 6 to 7 minutes per document, but with Document Understanding, it has come down to 2 minutes, which also includes any human validation that is required.

Document Understanding has helped to reduce human errors, but I do not have the metrics.

Document Understanding has helped free up staff’s time for other projects. Approximately 50% to 60% of the time is freed up.

The entity-level extraction is very good. The workflow is also very good.

Its pricing can be improved. 

I have been working with this solution for three to five months. 

It is scalable. There is no doubt about it.

I would rate them an eight out of ten. They can have slightly better performance.

Positive

We use another solution. It is a local solution that we have. It is a lot cheaper, and the pricing model is also a little different. They do not charge you on a per-page basis. We saw an ROI with this solution because of its cost and charging model.

It is mostly deployed on the cloud. The cloud type depends on the organization, but mostly it is on a private cloud. AWS and Azure are the most popular ones currently.

I was involved in its deployment on a couple of projects. Its deployment is a little bit complex because you have to set up a private cloud, and then you have to install this entire product from the cloud. With a public cloud, it is relatively easy because the cloud services are provided by the product company itself, whereas with a private cloud, you have to take more measures.

In terms of the implementation strategy, we have to identify the type of document that we want to process. We have to determine the volume. We have to determine the variations. We have to classify them into structured data and unstructured data. Once all of those things are done, we start training based on the sample format. After the training is complete, we put it into the UAT mode, and then it will go to production.

Usually, we do the deployment as implementers. We take help from the product company's technical support in case we get stuck somewhere.

It requires one or three people for a maximum of three days. The scope of deployment depends on the use case. If you have use cases across departments, then it will be deployed across departments. The deployment would be dependent on the number of departments or countries. If additional countries are to be added, we have to deploy in that environment. We have done multi-country deployments as well. Multi-function deployments are not very common because, usually, all the applications work in the same environment.

Any maintenance is taken care of by the product company. There are upgrades, and then there are bugs that are found in the product. They need to update the product on a time-to-time basis.

We have seen time to value with Document Understanding. Outside India, it would be somewhere around 18 months, and in India, it would be somewhere around 2 to 2.5 years or 24 to 30 months.

Its pricing can be looked into because it is on the higher side for developing economies, such as India, where the cost of labor is a little cheaper compared to advanced technologies.

I would rate Document Understanding an eight out of ten.

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

Document Understanding can complete each document within one second.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I have been using UiPath Document Understanding for three years.

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

Positive

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

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

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

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

I rate UiPath Document Understanding an eight out of ten.

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

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

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

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

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

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

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

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

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

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

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

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

Positive

The setup process for UiPath Document Understanding is simple.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The human validation takes three to four hours to complete.

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

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

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

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

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

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

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

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

I have been using UiPath Document Understanding for two years.

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

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

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

Positive

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

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

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

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

I would rate UiPath Document Understanding nine out of ten.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The UiPath APIs lack reliable table parsing.

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

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

The cost of UiPath Document Understanding has room for improvement.

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

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

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

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

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

I would rate UiPath Document Understanding five out of ten.

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

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