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.
Owner at Orange Horse
Can understand varying document formats, provides efficient integration, and saves manual effort
Pros and Cons
- "The quality of the input documents is crucial because sometimes healthcare providers prefer automated processing rather than human review."
- "The results of classifying patient documents within UiPath Document Understanding need to be more accurate."
What is our primary use case?
How has it helped my organization?
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.
What is most valuable?
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.
What needs improvement?
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.
Buyer's Guide
UiPath Document Understanding
October 2024
Learn what your peers think about UiPath Document Understanding. Get advice and tips from experienced pros sharing their opinions. Updated: October 2024.
816,406 professionals have used our research since 2012.
For how long have I used the solution?
I have been using UiPath Document Understanding for three years.
How are customer service and support?
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.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
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.
What was our ROI?
We saw a return on investment within three months of deploying UiPath Document Understanding.
What's my experience with pricing, setup cost, and licensing?
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.
What other advice do I have?
I rate UiPath Document Understanding an eight out of ten.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Last updated: Feb 22, 2024
Flag as inappropriateVP Delivery at Bynet
Offers impressive ability to automate document processing while providing seamless integration, efficient training models, and significant time and cost savings
Pros and Cons
- "The scalability it offers is truly exceptional, making it arguably the best in the market."
- "Making the design of Forms AI more flexible and accommodating to companies' branding preferences would be beneficial."
What is our primary use case?
The primary use case revolves around processing invoices. In Israel, where the solution is region-oriented, the invoices typically involve multiple languages within a single document and may also include various currencies. The capability of handling such diverse linguistic and currency elements is a notable strength of UiPath Document Understanding in this context. Through its implementation, our goal was to minimize manual tasks significantly and reduce the time required for invoice processing.
How has it helped my organization?
Up to this point, Document Understanding has been applied primarily to automate invoice processing in our implementations. For the customers for whom we have implemented it, the emphasis has predominantly been on invoice processing. This is because, within the customer's value chain, these processes are perceived to deliver the most significant value.
In terms of the types and volumes of documents processed with Document Understanding, the volumes are measured per page rather than per invoice. We typically handle a range of 50,000 to 100,000 pages. It's important to note that invoices, which occasionally consist of more than two or three pages, are encompassed within these volume metrics.
Typically, the document format comprises a header, a table, and often a summary, along with occasional total figures. This basic structure is effectively handled by Document Understanding, excelling in processing both headers and tables seamlessly.
Approximately seventy to eighty percent of our customers' organizational documents undergo complete and automatic processing.
The benefits are straightforward– it eliminates the need for physical forms on the table. This simplicity instills a high level of confidence in the model, and I foresee a promising future for it. It stands out as an excellent solution for companies, particularly those dealing with a substantial volume of invoices and vendors from diverse sources.
It has liberated time for other projects. Previously, we needed three to four people for validating invoices. Now, we have scaled down to one part-time person, who, for the most part, is engaged in other responsibilities. Invoicing tasks occupy only around five percent of their work time, handled intermittently.
What is most valuable?
The most valuable aspect is the AI training model, which distinguishes itself by offering a more transparent and controllable approach compared to other products on the market. Unlike some alternatives, this model allows precise retraining of machine learning instances. It provides visibility into the training process, enabling control and the option to retrain multiple times as necessary. In contrast to comparable products, this transparency and control contribute to enhancing the precision of the training model.
Forms AI performs admirably, posing as a strong competitor to Microsoft's PowerApps and other similar products in the market. It is straightforward and versatile, yet there is room for enhancement in certain design features that could improve user experience.
Document Understanding seamlessly integrates with other systems and applications within the environment it operates. Its integration capabilities extend beyond RPA modules, ensuring smooth and trouble-free connections with various components.
Human validation is required for Document Understanding at the beginning of Document automation journey, constituting around thirty percent of the overall process, while the tool handles the remaining seventy percent and document straight through processing improver further with model retraining. Notably, the retraining feature is a crucial and valuable aspect of the platform. This feature allows for retraining based on the validation actions performed by human validators. This is particularly significant because it enables refinement of the model in cases where documents are validated with low confidence. Some of the platforms lack the capability to provide confidence levels for field and data recognition, making this retraining feature a valuable asset for businesses seeking precision and efficiency in document processing. The human validation process for each document typically takes only a couple of seconds. The validation requirements are easily identifiable, allowing you to point to the specific area. Typically, pointing to it triggers a quick refocus of recognition to a different part, making the validation process efficient and straightforward.
The average handle time before implementing Document Understanding was approximately between three to five minutes, but after automation, it has significantly reduced to less than a minute, possibly even just a couple of seconds. This improvement covers the entire process, including validation, data exchange, mailing approvals, and more, all seamlessly happening in the background. Beyond the time savings, the automation also substantially reduces rework caused by human errors, enhancing the overall efficiency and accuracy of the process. As per the customer, errors do occur at times, and the associated risk is considerably high. However, the implementation of Document Understanding effectively mitigates this risk, eliminating the potential for errors.
What needs improvement?
I wish to have more pre-trained modules available in various languages. For instance, while Document Understanding currently supports Hebrew for Israel, I would appreciate the addition of pre-trained modules specifically tailored for different Hebrew-related forms. This enhancement could prove to be quite beneficial.
For how long have I used the solution?
I have been working with it for three months.
What do I think about the stability of the solution?
The system is highly stable, especially since it operates on the cloud. We haven't encountered any disruptions or issues.
What do I think about the scalability of the solution?
When discussing Document Understanding and RPA processes, it's essential to highlight that it's a scalable solution on the cloud. The scalability it offers is truly exceptional, making it arguably the best in the market.
How are customer service and support?
The technical support is outstanding. In Israel, we have a local UiPath office, and they are incredibly helpful. Their responsiveness is remarkable, and if there's ever a need for assistance, they promptly provide valuable support. I would rate it nine out of ten.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial setup falls in the middle ground – not overly complex but not entirely straightforward either. It requires an understanding of how to retrain the model and fine-tune both the OCR and the application.
What about the implementation team?
Deployment time is a matter of minutes. The deployment process is straightforward as it involves a cloud solution. You order the environment, set up both the robotic and Document Understanding environments, and start working. It's a simple and quick process. Typically, the deployment involves one representative from our team and relevant subject matter experts from the customer's side. These experts are individuals directly engaged in the process, and often a reinsurance manager, functioning as a project manager, is crucial from the customer's side. It is imperative to have a subject matter expert from the customer's side because our team usually lacks visibility into their business processes and requirements.
Maintenance typically involves one person responsible for document validation. The specifics may vary based on the document type; for instance, if it's invoices, it's generally handled by a single person specializing in invoice processing. While I would assume similar patterns for other platforms, variations might occur with different document types, requiring different subject matter experts for each form. However, from the technical side, it usually entails the responsibility of one person.
What was our ROI?
In terms of Return on Investment, while we haven't quantified it precisely, the notable reduction in personnel from three or four full-time roles to one person handling the task part-time signifies a significant cost avoidance. Instead of letting people go, the approach involves reallocating them to other tasks, essentially avoiding around ninety-five percent of the previous budget dedicated to this particular process. The benefits in terms of cost-effectiveness and time efficiency are substantial. In the context of time to value, I'd estimate around two months to establish a production process, yielding impressive results ranging from seventy to eighty percent.
I think this timeframe needs to be considered with the multitude of invoices and vendors involved. We're dealing with processing invoices from over two thousand different vendors, spanning two different languages, including instances where both languages are mixed within a single invoice. The complexity is heightened by the inclusion of both right-to-left and left-to-right languages. Despite these intricate challenges, achieving the high complexity production process within two months is not only sufficient but also a commendable outcome.
What other advice do I have?
For those interested, I would recommend undergoing a POC to truly experience and be pleasantly surprised by the outcomes within a couple of days. In an overall comparison with other solutions in the local market, I would confidently rate this as a robust nine out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner Reseller, Integrator
Buyer's Guide
UiPath Document Understanding
October 2024
Learn what your peers think about UiPath Document Understanding. Get advice and tips from experienced pros sharing their opinions. Updated: October 2024.
816,406 professionals have used our research since 2012.
Team Lead at Phenologix
Helps reduce human error, and saves us time, but is expensive
Pros and Cons
- "UiPath provides a useful feature that allows us to classify documents as invoices or not."
- "UiPath Document Understanding's ability to read handwritten files has room for improvement."
What is our primary use case?
We implemented UiPath Document Understanding for our first project with a pharmaceutical insurance company. They were receiving invoices from over 2,000 different vendors in a variety of formats on a daily basis, and they wanted to automate the process. We are receiving invoices in their email, and we are automating the download and processing of these invoices. If the confidence level of the automated data extraction is low, a user or client can correct the data according to the invoice and then submit it. The data will then be improved. We will be automating this project in two parts: first, reading specific emails and downloading the attachments; and second, checking if the attachments are normal documents or invoices.
We have implemented UiPath Document Understanding for two companies: one in the insurance industry and the other in the financial industry. We have completed the document creation process, which includes OCR and automatic signature imposition by different lawyers on the finalized documentation. We also use Document Understanding to read the document after analyzing it, and we then update the PDF with a front page signature and other components. This is a small process, but the first project was very large and we gained a lot of business from it. It was a very good project overall.
We process between 100 to 200 documents per day using Document Understanding.
The documents include checkboxes and barcodes. Some of our vendors only provide handwritten invoices, which Document Understanding could not read. These invoices had to be processed manually by the user.
How has it helped my organization?
UiPath Document Understanding can handle varying document formats including handwritten documents.
We have implemented a machine learning model to sort vendor names and important information related to those vendors into our system. When the model encounters a vendor that it has already seen, it automatically grabs the important information from the invoice. The model is continuously training on the new data that it receives, so it can become more accurate over time.
Machine learning was very good. We don't think we can implement without any ML model.
We integrated Document Understanding with Dynamic CRM so that the information extracted by Document Understanding is automatically input into CRM.
The amount of human validation required is based on the confidence level of the ML model. Each time human validation is required, the ML model learns and the need for human validation decreases. At the start, the ratio of documents requiring human validation was 50/50, but this ratio decreased with each iteration.
Document understanding helps reduce human errors. For example, if we receive 150 emails daily, we must analyze and process each email accordingly, such as sending invoices, checking invoice values, and investigating all relevant information. We must then read each invoice and enter the data into the system. This is a very active task that requires around 15 people to perform daily. Document understanding has reduced the need for human interaction by allowing us to automate this process. Now, only one person needs to analyze the email invoices. Once the invoices have been checked and analyzed, they are passed to a UiPath bot, which handles all the subsequent steps, such as reading the invoices and entering the data into the system.
Document understanding has helped free up staff time.
What is most valuable?
UiPath provides a useful feature that allows us to classify documents as invoices or not.
If the confidence level is low, we can check it and provide the product value to move forward. In this step, the user can sometimes skip or delete pages, especially if we receive a large PDF with the first two pages being invoices, followed by some relevant documents, and then more invoices in the same period. This is a very good feature of UiPath Document Understanding, as it allows the user to skip pages within the PDF document to move forward. For example, the user can specify that the first two pages and pages nine and ten are invoices.
What needs improvement?
UiPath Document Understanding's ability to read handwritten files has room for improvement.
The price of Document understanding is high, and we are constantly struggling to get our clients to use it because they find it to be expensive.
For how long have I used the solution?
I have been using UiPath Document Understanding for one and a half years.
What do I think about the stability of the solution?
UiPath Document Understanding is stable. We have not encountered any downtime.
What do I think about the scalability of the solution?
UiPath Document Understanding is scalable.
How are customer service and support?
The technical support was helpful.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial deployment was straightforward.
Two people were required for deployment.
What about the implementation team?
The implementation was completed in-house. We have a large team that includes technical consultants, architects, and developers.
What's my experience with pricing, setup cost, and licensing?
The last time we implemented UiPath Document Understanding the price was high.
What other advice do I have?
I would rate UiPath Document Understanding six out of ten.
Which deployment model are you using for this solution?
On-premises
Disclosure: PeerSpot contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
RPA Consultant at Aubay Italia S.p.A.
Provides valuable machine learning, reduces human error, and speeds up processes
Pros and Cons
- "Machine learning is the most valuable feature of UiPath Document Understanding."
- "I encountered difficulties with UiPath Document Understanding in determining the appropriate OCR to use for certain files."
What is our primary use case?
Our primary use cases for UiPath Document Understanding are processing invoices for five different clients and importing/exporting documents to extract vital information, mainly from unstructured documents. These five clients are from various industries, including transportation, scientific research, food services, and clothing.
How has it helped my organization?
I processed 400 documents per day for one client and 20 documents per day for the second client.
The documents processed were in PDF format.
90 percent of the 400 documents processed per day for a single client were fully automated. However, only 50 percent of the 20 documents per day were automated due to their greater level of unstructured nature. As a result, the remaining 50 percent had to be sent to the action center.
AI and machine learning for Document Understanding are game changers. Machine learning was helpful in identifying the various areas of the documents from which I needed to extract different types of information, making the process quicker.
The default model didn't work for me because I needed to extract information from documents written in French. Thus, I had to create my own model using AI, which proved to be exceptionally beneficial for handling the French text and its accents.
Integrating UiPath Document Understanding with other systems and applications in our environment works well. The solution was able to retrieve the PDF document from an email, extract the details using the command, and apply those details to an application, saving a substantial amount of time.
UiPath Document Understanding serves as a safeguard in relation to cost and time savings, as it diminishes the manual workload for employees and minimizes errors. For a job that took a human eight hours to complete, the bot was able to do it in three hours.
The extent of human validation needed for Document Understanding varies for each client. For one client, no validation was necessary as the solution effectively extracted all required information from the documents. However, for another client dealing with diverse document types, errors occasionally occurred due to character placement. This was particularly evident when email addresses were positioned differently, some at the top and others at the bottom of the documents, posing challenges to the robot's detection capabilities. In such instances, a validation process was implemented. Every seven days, ten percent of the batch would be sent to the Action Center for validation.
The time saved with UiPath Document Understanding is exemplified by an organization that previously had to spend three days manually extracting information from 400 documents every month. However, with UiPath Document Understanding, this task now only takes two hours.
What is most valuable?
Machine learning is the most valuable feature of UiPath Document Understanding.
What needs improvement?
I encountered difficulties with UiPath Document Understanding in determining the appropriate OCR to use for certain files. These files required extracting both the company logo from the page and the digitized text, posing a challenge. The OCR engine faces difficulties when processing signatures and scanned documents with unclear handwritten text.
The robot faces difficulties in recognizing when there are multiple documents on a single page. This necessitates manual intervention by first splitting the document and then re-digitizing each part separately.
I would like a split feature in a future release of UiPath Document Understanding.
For how long have I used the solution?
I have been using UiPath Document Understanding for one month.
What do I think about the stability of the solution?
UiPath Document Understanding is extremely stable.
What do I think about the scalability of the solution?
UiPath Document Understanding is scalable.
How are customer service and support?
The technical support responds promptly and strives to resolve our issues quickly. However, there is room for improvement. For instance, we encountered an issue with the Action Center, and the support team was unable to determine the cause for three days. Eventually, someone from my team resolved the issue.
How would you rate customer service and support?
Neutral
How was the initial setup?
The initial setup was a bit complex.
Which other solutions did I evaluate?
I also assessed FlexiCapture, but I discovered that UiPath Document Understanding was more user-friendly. Coming from a scientific background, I found that UiPath Document Understanding offered a more logical and less complex solution.
What other advice do I have?
I would rate UiPath Document Understanding nine out of ten.
It took me one week to study UiPath Document Understanding and to present it to my organization.
I realized the benefits of UiPath Document Understanding once I completed my first project.
The quantity of personnel needed to maintain the solution relies on each project. In the most recent project I participated in, we needed a total of two individuals, one of whom was an administrator from our team.
When using UiPath Document Understanding, always ensure that the number of structures is the same each time to prevent errors.
I believe that utilizing communication mining would be more effective with the AI Center.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Google
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
Product Manager at a hospitality company with 51-200 employees
Good documentation understanding and helpful technical support with the capability to free up staff time
Pros and Cons
- "We can integrate document understanding with other systems and applications."
- "If there were more integrations with Veracode or the AWS server, so we don't have to completely transfer our data and keep data on our servers, that might increase security."
What is our primary use case?
We use the solution in pharmacy health care, and our role is to enable doctors so that they can set up a personalized clinic - everything a patient requires. We get information in the form of a document and we can break it down into sheets and JSON files, for example. We use a UiPath documentation tool.
How has it helped my organization?
Document understanding has helped us increase our efficiency and accuracy. We don't have to manually check data again and again.
After the first month, we discussed how the solution was benefiting us, and we decided to continue with it.
What is most valuable?
It helps with data and consistency. It helps us receive information and convert it so the systems we have in place can understand a problem and generate responses accordingly.
We've used it in one process where we received a patient's pharmaceutical documents from other sources that come in different formats. We receive the formats, convert the information into a standard format, and then process the information to provide information for insurance forms.
The average document size is not very large, likely 80-100 MBs. However, the total count of the patients is somewhere around 10,000.
We have 50% to 60% of clients directly onboarded via an insurance form. Therefore, we are provided with the exact form we need and can run a complete automation on that. There's no type of manual involvement there.
The format for setup is a great thing. Earlier, the tool that we used was pretty manual. In this case, it's a bit easier for our developers.
The solution can detect signatures to let us know that there's a signature there. You can construct tables or any other format of data based on pure text information.
They are employing an ML model for detection conversations. They are also trying to deploy a written-to-text conversion. They are convinced AMR systems will replace other manual work.
The main value of AI for us is to convert data formats from one type to another. We receive data stating two or more complex data points mixed later, for example, the license number and the serial date of operation for the doctors or the patient code; sometimes these things are mixed together. We want all those to be arranged. Their AI does the job very well.
We can integrate document understanding with other systems and applications. With it, we can simply write down a code to communicate with the ML model, for example, how to convert the data and which datasets to look for precisely in the documentation. We were able to communicate easily what would be the format of the PDF documents that we would be providing. The integration part and communication was the best aspect of the entire application.
We have Veracode integrated with it. We will do a manual check if we get a security flag where the data may be inconsistent. We usually get an alert like this once or twice a week. The human validation process usually takes an hour since we have to manually check the parameters. Before implementing the solution, the handling time before automating the process was pretty much the same. With this, we may have reduced it by half an hour. Also, previously, we'd have more manual interventions happening, maybe three or four times a day; however, now, with everything automated, that only happens one or two times a week. It's reduced the frequency by about half an hour on average.
Using the solution has freed up staff time. We've reduced our team size in regards to quality checking. We've reduced the amount of work by 40 to 50 hours a week.
What needs improvement?
UiPath's documentation tool is not great with converting handwriting to text, so we only used it for the conversion of insurance documents into other formats.
They could modulate the ML model in the future. When it comes to working with data and processing reports, we have to target the datasets we had earlier targeted and redefine the parameters, which takes a lot of time. If the ML model, at the time it is analyzing the data, could in itself provide the insights we will need for future reporting, that would be great. There needs to be better real-time analytics since we aren't getting the data for reporting until we go and seek it out.
If there were more integrations with Veracode or the AWS server, so we don't have to completely transfer our data and keep data on our servers, that might increase security.
For how long have I used the solution?
I've used the solution for a year or so.
What do I think about the stability of the solution?
The solution is good. It's very stable.
What do I think about the scalability of the solution?
It's not deployed across multiple departments. We have this deployed across one department. We have two developers working with the stream of data.
For small to medium firms, the solution scales well. However, if you are going for a global scale, you should develop your own models and not rely on outside models.
How are customer service and support?
Support is good. That said, sometimes they have problems understanding what we want to do with the data since we cannot provide the data in its raw format. We have to decrypt it. This makes it a bit harder. That's why we would like integration on our servers instead of theirs.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
We did use a different solution previously. We switched since the number of tags we were getting was pretty high. We had to do more manual interventions a lot more often. The parameters we used to communicate were also manual. It required setting up a decision tree in the whole of the document. A lot of the time, we would not know what the document type would look like. It required the developers to look at the documents, create a decision tree, and go from there. With UiPath, we don't need to do all that manual upfront work.
How was the initial setup?
I was a project manager, not a developer, deploying the solution. My understanding is the process was moderate. It was eight too easy or too complex.
The implementation involved discussing the work with the insurance firm. We explained we were moving from one system to another. Once we had that conversation, we received the documentation in the format we wanted.
Then, we looked at how we encrypted our data before sending it to UiPath servers. We did have a lot of compliance issues and had to be careful.
Once we came to the physical implementation, that was easy. Managing other stakeholders and their clients was the hardest part.
We had three developers from our team working on the deployment. It took us about 10 to 11 days to deploy.
Twice a week, maintenance is needed whenever there's a flag raised when data points do not match. We can simply ignore the solution and change the data file, or we can go in and see what is wrong with the file type and adjust it so that it doesn't happen again.
What about the implementation team?
We did not use any outside assistance beyond the help of UiPath's support team.
What was our ROI?
The ROI is pretty good. We did not do any calculation for ROI. However, the accuracy percentage and time reduction which we noted, have made us happy.
We originally noticed a time to value for UiPath within 10 to 12 days.
What's my experience with pricing, setup cost, and licensing?
The pricing is pretty fair. It is quote-based. Overall, it's fair. If you are a small firm looking to scale up, it is good. Enterprises should create their own ML model instead of relying on some outside product.
Which other solutions did I evaluate?
We looked at a few other options and did a few POCs. UiPath is able to sense and analyze a document and create a hierarchy for you. You can also create a manual code if you want something done differently. The only issue is we have to upload the information to UiPath servers, which may be a security issue.
What other advice do I have?
We're end-users, not integrators.
It's a good idea to have a call with the support team and managers and do a review to understand the solution to see if the product would work with your type of data. It's important to test it out, ideally using your own data.
I'd rate the solution nine out of ten.
Which deployment model are you using for this solution?
Private Cloud
Disclosure: PeerSpot contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
Business Dedicated Consultant B2B at a comms service provider with 10,001+ employees
Simplifies the automation process, helps with complex documents, and saves time
Pros and Cons
- "The highly visual and user-friendly interface was a standout feature."
- "UiPath Document Understanding requires more database connectors."
What is our primary use case?
I used UiPath Document Understanding to create a report by reading invoices and V9 tax documents. I employed specific taxonomies to facilitate document analysis and populate my database with extracted information. The primary objective was to accurately identify and store relevant data from these documents within the database.
The idea arose from the observation that many companies lack a centralized repository for essential documents, such as invoices. In response, I created a website where a robot automatically uploads and interprets these invoices, presenting key details about each document on the website.
How has it helped my organization?
By using taxonomies, I could interpret the documents and make them easily accessible through a website database. This way, website visitors could find all the documents themselves, eliminating the need for them to repeatedly ask employees for specific documents like invoices or V9 tax forms. UiPath's visual processes further simplified this by allowing me to implement and manage the system effortlessly.
I used UiPath Document Understanding to process invoices and V9 tax documents.
All the documents processed were in PDF format.
The documents contain tables, boxes, check marks, and handwritten text.
All the documents were processed 100 percent automatically.
UiPath Document Understanding was able to handle the handwriting and signatures with no issues.
UiPath Document Understanding helped make the automation process easier for me.
The manual validation of each document took one second.
Using UiPath Document Understanding, all the documents were processed in just a minute. While I didn't have many documents, it still surprised me how quickly it worked. Manually, it would have taken me about five to ten minutes.
UiPath Document Understanding has saved me time to work on other projects in parallel.
What is most valuable?
The highly visual and user-friendly interface was a standout feature. Selecting taxonomies was as simple as clicking the corresponding areas on the invoices, enhancing the visual nature of the interaction.
What needs improvement?
UiPath Document Understanding requires more database connectors. I encountered difficulty connecting to Workbench from MySQL, necessitating a workaround.
For how long have I used the solution?
I have been using UiPath Document Understanding for three months.
What do I think about the stability of the solution?
I did not face any stability issues with UiPath Document Understanding.
What do I think about the scalability of the solution?
The scalability of UiPath Document Understanding is fine.
How was the initial setup?
The initial deployment was straightforward. The deployment took a few minutes to complete and I did it myself.
What was our ROI?
Originally, I spent some time building the automation robot. However, once I completed it, I realized the value of UiPath Document Understanding.
What's my experience with pricing, setup cost, and licensing?
I used the community version, so there was no fee.
What other advice do I have?
I would rate UiPath Document Understanding nine out of ten.
I was the only one using the solution in our organization.
I recommend evaluating both the free and paid versions of UiPath Document Understanding.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Last updated: Feb 22, 2024
Flag as inappropriateHead Automation at a manufacturing company with 51-200 employees
Offers user-friendly development, and a structured labeling process, but the AI causes stability issues
Pros and Cons
- "I like the clear and organized way in which UiPath has structured the labeling process, as well as the user-friendly development environment."
- "The licensing model poses a significant challenge due to the fee charged for posting a model, which impedes the development of productivity-enhancing models."
What is our primary use case?
We use UiPath Document Understanding to process purchase orders and order confirmations.
We implemented UiPath Document Understanding because we wanted a more efficient way to process the documents we were receiving.
How has it helped my organization?
We process purchase orders and order confirmations in PDF format. The documents we process are tables and standard data.
We process anywhere from 150 to 200 documents per day with UiPath Document Understanding. We process between 60 to 70 percent of the documents completely with UiPath Document Understanding.
We do not use Document Understanding for signatures or handwriting, but we do use it for various document formats, which it handles moderately well.
The AI and machine learning with UiPath do the job moderately well.
We leveraged API calls to seamlessly integrate UiPath Document Understanding with other systems and applications within our environment.
UiPath Document Understanding has helped save us a lot of time.
We required human validation between 30 to 40 percent of the time.
Prior to implementing UiPath Document Understanding, it took us one hour to process each document. With the implementation, processing time has been reduced to one minute, saving us 59 minutes per document.
UiPath Document Understanding has helped reduce human error.
UiPath Document Understanding has helped save our people time to focus on other projects. This time savings is equivalent to the productive output of a full-time employee.
What is most valuable?
I like the clear and organized way in which UiPath has structured the labeling process, as well as the user-friendly development environment.
What needs improvement?
Several areas require improvement in UiPath. The licensing model poses a significant challenge due to the fee charged for posting a model, which impedes the development of productivity-enhancing models. Additionally, UiPath's pricing is substantially higher than that of its competitors, approximately three to four times higher.
UiPath's AI quality needs substantial improvement. Several issues have persisted for at least two years, such as the inability to handle breaks in tables. When data in a table extends from the first page to the second, UiPath fails to process it effectively. Additionally, UiPath struggles to handle multiple items.
For how long have I used the solution?
I have been using UiPath Document Understanding for over two years.
What do I think about the stability of the solution?
I would rate the stability of UiPath Document Understandings six out of ten, because of the AI issues.
What do I think about the scalability of the solution?
I would rate the scalability of UiPath Document Understanding eight out of ten.
How are customer service and support?
The technical support is good.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial deployment is straightforward. We implemented a hybrid model of on-premises and cloud deployment throughout the organization. The robots are physically located on-premises, but their operations are managed and controlled through a cloud-based platform. One person was required for the deployment.
What was our ROI?
We have seen a return on investment with UiPath Document Understanding.
What's my experience with pricing, setup cost, and licensing?
UiPath Document Understanding compared to the competitors is high. I would rate the price nine out of ten with ten being the most expensive.
What other advice do I have?
I would rate UiPath Document Understanding seven out of ten.
We have seen time to value with UiPath Document Understanding.
UiPath Document Understanding requires constant maintenance because of the AI issues. One person can handle the maintenance.
I recommend thoroughly testing UiPath Document Understanding to verify that the organization is genuinely deriving value from the tool and to assess whether the AI model can effectively handle the capabilities advertised by UiPath.
Which deployment model are you using for this solution?
Public Cloud
Disclosure: My company has a business relationship with this vendor other than being a customer:
RPA Developer at Arkon Group LLC
Reduces human validation, offers good machine learning and has excellent document understanding
Pros and Cons
- "It's great for document understanding for invoices and installments."
- "It would be ideal if they could include more packages for more use cases."
What is our primary use case?
I've done multiple projects. A couple of them included invoice processing. It has a machine learning package that works out of the box. For invoices. I use that. It does a very good job.
I also use document understanding, which doesn't have any training. I trained it for the extraction of data for some forms like car loan installments. It did a pretty good job.
In addition, I used it for a medical department. I use document understanding.
How has it helped my organization?
We wanted to have a way to do data extraction from PDF documents. It helped us automate the process. For example, if you purchase a car, the loan installment paper includes items like the vehicle number, purchase information, buyer and seller information, et cetera. It can pull that out. We can also use it similarly in the healthcare industry, to get client details.
What is most valuable?
It's great for document understanding for invoices and installments.
When it comes to document understanding for handwriting, it does a decent job sometimes with handwriting, however, some people have weird handwriting and the OCR can struggle to pick up the information. In those cases, you have to read it yourself. However, overall, it does a decent job. I haven't used it to read checkboxes or bar codes. It works well with tables, however.
There are thousands of documents that are completely, automatically processed. It can process close to a few thousand invoices per day.
I also integrated it with the Action Center for some projects; It's pretty neat.
I like the machine learning skills and the fact that they come out of the box. They are packages that you can just deploy. The training of the ML is great; there is this tool that comes with it called Data Manager. That's very handy when you are labeling data and then using it.
The AI center is excellent. AI does a pretty good job covering all the needs that are needed for automating the process for semi-structured documents. The structured documents with the form extracted, overall, are pretty good. It's doing a very impressive job. I was surprised the first time I was exposed to it. Now, I actually enjoyed doing it. It allows me to automate items that are mundane. For example, if an employee is given a task to scrape data from invoices, which are PDFs, they can get the robot to do it. Due to the fact that the documents most of the time are semi-structured, machine learning can handle the task, and machine learning is doing a pretty good job of handling that instead of the employee.
I've used Forms AI. So far, my experience has been pretty good. That said, it only works for structured documents.
In terms of the documented understanding of integrating with other systems or applications, everything is good. You can integrate it with the action center, and it does a very good job. Everything is handy and easy to use. Integration overall is good.
Human validation is not always required for the outputs. It depends on the document. For invoices, you might need human validation 5% to 10% of the time. If it processes ten documents, I would expect one document at least to need human intervention. If you are building some custom ML skills for some documents, if the document itself is scanned well and positioned well, it does a pretty good job of extracting the needed fields. If it's slightly less quality then the robot will struggle with both the OCR or extracting and digitizing data. Overall, we might need 10% to 20% human validation. The validation process itself now takes about a minute with the help of automation. It's reduced everything by a minute or two to up to five or six minutes.
Document understanding has helped us to reduce human error by at least half.
What needs improvement?
The only problem that I can see with integration is some of the features cannot be used inside the loop. At least that was the case before. I don't know if they fixed it or not. You can't put some of the activities that are de-related inside the loop. It's going to throw an error if you do.
It would be ideal if they could include more packages for more use cases.
For how long have I used the solution?
I've used the solution for about a year.
How are customer service and support?
I've contacted technical support and they have been helpful.
How would you rate customer service and support?
Positive
What other advice do I have?
I'm a customer and end user. I work as a developer.
I'd rate the solution nine out of ten overall.
Disclosure: PeerSpot contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
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Updated: October 2024
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