Jiffy.ai Automate Reviews

Our initial use cases are mainly for finance. We are doing account payable, accounts receivable, reconciliation, and those types of things with the automation. In terms of accounts payable, we automate the invoice processing since it is an end-to-end. This means that the vendor will send an invoice to email, which will be picked up by the bot automatically. Then, it will extract the information from the invoices and post it to our SAP.

It is a web-based solution but hosted on our server.

The biggest driver was the cost savings. We wanted to improve productivity and save costs. Therefore, we gave most of the mundane tasks currently being done by a human to a bot. Some of the mundane tasks were reading invoices and keying in the data. We are talking about 15,000 documents every day. That is a huge volume that needs a lot of people. With the bot, it is just a fraction of the cost, because there is a huge savings in terms of manpower.

More for regulatory and audit purposes, we still require a human to approve it. Previously, we had the human to do it, then we had people cross-check it. Then, you have another layer of the approval. With the bot, we don't require two people. We only have the approval because we still have a person who does the approval, which we have to maintain. 

We are primarily using it for processing documents, particularly for procurement and payroll. We have a case for HR to deliver a document for attestations for people who are leaving the organization. They are provided with an attestation letter. We are primarily using it for payroll and procurement, so far.

We have our own virtual servers where we host on our own servers, but they are virtual and not physical. The machines running the bots are virtual machines as well.

We have not been using it for anything other than the standard automation process, which performs well.

In terms of the accuracy, it has done particularly well. For staff, it is common when doing repetitive work that there tends to be mistakes, even if you are familiar with it. There will be a missing digit or letter here and there, but Jiffy.ai Automate never does that. It is accurate to a "t". It encompasses errors only if the input is poorly done, so it does not commit errors when it does this process. 

Jiffy.ai Automate has reduced manual processing for the 25 percent of the processes that it has automated. We have many processes, not just one, that Jiffy.ai Automate processes. Each process takes between five to 10 minutes, depending on the network. It could be as fast as two minutes if you did it manually, but then it also depends on how the network performs, as it may be inaccurate as well. Jiffy.ai Automate may take slightly longer, because of the way it works (usually five minutes). If the network is a bit slow, then maybe it takes at most 10 minutes and you need to times that by 2000 transactions minimum. In addition, there were certain months when the numbers of transactions that Jiffy.ai Automate processed spiked, so we hit as high as 5,000 transactions.

The solution was proven to be especially essential during the pandemic. For example, the work that Jiffy.ai Automate does is around 25 percent of the data process. If Jiffy.ai Automate had not been operating during the pandemic, that 25 percent would probably have taken a much longer time than it normally does. Though, it depends on working from home conditions and some other factors. If it hadn't been for Jiffy.ai Automate, then the staff would probably have only got 30 percent done. The turnaround time would have been much longer; it could have been at least two to three times longer. 

It is important that Jiffy.ai Automate does its work, so payments will be initiated and processed on time to our vendors, partners, and suppliers.

We engage Jiffy.ai to do reusing, modifying, and building process automation work for us. We only inform them what we want them to do, then their staff will follow up closely and do the coding, then the actual automating process. We explain the process to them, they understand it, and then they capture it in their system. They run it, test it, and then confirm that it works with us. We are practically hands-off with it. 

We are using it in production for driving our Windows application, extracting content from it, and using it for our desktop automation. 

In our test environment, we are using the OCR features of the Jiffy.ai application. Essentially, we submit the images into the application, then we use the training model for the data. The actual images are being extracted and returned back to us as a document.

We automated a fairly complicated process, but it was revolving around a single application. We are not doing content switching among multiple applications. It was mainly driving a single application and extracting information out of it.

We are using version 3 in production, and we are using version 4 in our test environment.

We have worked on developing our workflow for driving a Windows application with individuals being either scripted or specified through the user interface. 

It reduces the amount of human intervention, but you still need a human in the loop in some cases. Accuracy is not 100 percent. Someone has to go through the results. but it does reduce the amount of work that needs to be done.

The system in production completely eliminated the need for human intervention. From time to time, we need to check the user interface and results, but that is very rare. It can be done once a week, or even less frequently. Regarding the OCR, we are still at very early stages. We have evaluated the results, giving feedback and comments to the Jiffy.ai team, so there is a bit of human intervention there.

We have a number of use cases. We've intentionally started with a focus in our finance controller organization as well as our supply chain organization. The bulk of our bots that have been deployed are in those two areas, doing a number of different business processes across those areas.

We've seen really good productivity gains. For the areas where we've chosen to automate, it's not that we have freed up 30 heads in one area with one project. It's more an aggregation of 20 heads across all of the different bots that have been developed. But we've seen tremendous value, especially in the pandemic. As an airline, we have had to cut costs and we were able to go through some pretty strong voluntary separation efforts and redistribute resources and cover things because of the automation work that we've been doing.

It has definitely reduced manual processing efforts in our company. We've automated about 10 percent of the work within particular areas that we're working on. I'm sure, across the company, there's a lot more opportunity. We have chosen to start small and get our bearing before we go too far. It has also reduced errors in our organization although I can't put a specific number on that right now. We haven't had as much focus on that front as we have around how much we've been able to automate.

It has also saved us time with the amount of manual work that has been addressed. The time savings are around 5 to 10 percent in the areas where we've been focused. And the automations have helped to save us money, in the $2 million to $3 million range, in annual savings.

We mostly use Jiffy.ai in finance, for accounts payable, accounts receivable, for scanning invoices, and fetching invoices from the suppliers' websites. 

The new division I'm in, which is a non-finance division, requires some automation as well. We are an engineering division, and the RPA team is starting to look into the non-finance processes. In our division, the processes are along the lines of document scanning and organization of those documents. We need to scan documents and identify the document types and put them in the right folder. It's a very manual, labor-intensive job that we are doing now.

But so far, it is mostly used in finance processes.

We have definitely seen savings in terms of manpower, especially in the finance processes, which are very labor-intensive and repetitive. Since the automation was implemented, they have been able to reduce the workforce by quite a number.

On a normal basis, a person has to process, say, 100 pages of invoices, but with automation, all that he or she needs to do is manage the exceptions and the errors. He can use the remaining time to do something else that is more valuable.

It also helps to reduce errors. By taking the human out of the equation, we can definitely increase accuracy. This was one of our goals with automation.

We have two different use cases:

1. Bank conflation. 
2. Wage Protection System (WPS), which is a new way to do salaries per person. 

We are trying to migrate to the latest version. We are planning to migrate onto their SaaS model.

Our most complex use case has been for reconciliation purposes. Apart from the core system, there is a third-party system, Western Union Money Transfer. We are using their platform. All countries are not integrated with their API. So, in certain countries, it still works on a standalone system. Then, they have another reconciliation system, Voyager, as well as our core system. These three systems need to be connected and consigned. This is quite complex because each one is a legacy system. Their reports have to be extracted and run on certain things that aren't on Windows, i.e., the old pre-XP kind of machines. Therefore, we had to build in a solution that could connect all reports from them, match, and talk to each other. Jiffy.ai has really helped us due to its flexibility. We were even able to go back 20 years and connect these machines to modern systems that we run on. It connects both ends of the technology. This is complex because of the heavy maintenance required to do all these things.

As a remittance company, we do close to 1.2 million transactions a month, and we do due diligence for all those 1.2 million transactions. Many of the customers expect their money to be credited in real-time. So, we have around 15 minutes for background actions to happen, so everything has to happen in real-time: enhancements, complaints, cleaning, etc. For certain countries, we need to add a number of compliance rules, e.g., for one country, we had to add 10 compliance officers. Two at a time would come in during rotating shifts. This adds a huge cost to operations. So, in the Jiffy.ai system, we put in machine learning. Very quickly, we didn't have anyone manually looking at it except for the chief compliance officer who gets a dashboard. We eliminated the need for 10 people because it is completely automated and worked out. Therefore, it improves efficiency. Everything is done in a couple of days, not even a week's time. 

We use the solution to upload and download the documents.

The solution's support services need improvement.

It is for invoice data extraction.

I am using the latest version. We have it on-premises, but my understanding is that they have cloud-based and on-premises solutions.

Previously, it used to be a completely manual process where we used to have around 16 people looking at the invoices and typing in the data. Now that we have put in a bot, this process is completely centralized. We now have just two people who look at the exceptions.