Jiffy.ai Automate Reviews

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.

The most valuable feature is the computer vision or OCR. That has a lot of use cases in real life. A lot of man hours can be saved, as we've seen in the finance processes and also in my future use cases. The feedback I have gotten from the team is that the OCR is quite powerful. I'm really looking forward to that.

For the finance processes, from what I know, Jiffy integrated quite well with the Oracle system. Most of the finance requests have been taken care of. It handled the integration pretty well.

The UI or the UX has room for improvement. The approach for designing the workflow is not that straightforward. It's quite difficult.

Also, when it comes to a Knowledge Base or training, there aren't many resources online that developers can refer to, unlike the competitors. There's no forum and there aren't too many YouTube videos or that sort of thing. There is also no free trial.

I was involved in the RFP and the PoC stages of looking for the right solution for RPA. The process started more than a year ago. It took us quite a while, more than a year, actually, to make a decision. There were a few factors involved. One of them was because of our budget. We didn't really have a budget at the time, but we do have a requirement to source for a suitable RPA solution.

I was involved a little bit during the initial stage, and then I changed my role and joined another department. Since then, I haven't used Jiffy. When I was involved with Jiffy, I was more a project manager. I wasn't really involved hands-on, but I did go through training that they did during the setup and the onboarding process.

Ultimately, we implemented it about six months ago.

There's a trade-off. One of the reasons we ended up with Jiffy is that the cost is really low compared to the rest, but in exchange you get what you pay for. To be fair, the people involved with it are not really familiar with the system, because it's quite new. But I've heard quite a few complaints about the usability and stability of Jiffy. Sometimes the bot will just not run for some unknown reason. In those cases, there's no documentation or forum where we can discuss issues. I wouldn't say it's running smoothly all the time. There have been a few issues along the way.

If they manage to resolve all the nitty-gritty small issues, then the scalability is not an issue. 

Our finance processes are not small, they are quite huge in terms of volume, and Jiffy has handled that quite well.

One thing that they are very good at is customer support. I still have a good working relationship with the top management of Jiffy and their sales team, even though I'm no longer part of their project. We still keep in contact every once in a while, just to check on the progress and catch up. In that sense, they are really good.

Jiffy is our first RPA solution. That's what we took a while to make a decision, because we are not really familiar with all the concepts.

One of the reasons we chose Jiffy is that they are quite new, so they're easier to work with. That is what we are experiencing now. The customer support is really good. They are willing to adapt to whatever we need. 

Product-wise, I think there are superior products on the market, but that comes with a price as well. We wanted to start small. Also, the promise of it running on Chromebook, which turns out not to be true, was a factor in our choice, so we feel quite disappointed about that.

We deployed Jiffy on a Windows Server, running on Google cloud, because the bots, in some processes, have to be running on a 24-hour basis. That's why we needed it to be installed on a server. But in some cases we are running it locally on the laptop, for some basic automations.

We are still very much depending on Jiffy. The agreement that we have is that they will help us to automate 10 initial processes while we are building up our in-house capabilities among our developers. It's quite difficult to find Jiffy-specific talent out there, because it's new. To hire a new developer, we need to train them.

We have two power users of Jiffy in our company, and we have one or two business analysts, while the rest are normal business users. Our team is very small and that is why we are very much dependent on Jiffy at the moment.

We ran a few use cases for the common RPA vendors, like UiPath, Automation Anywhere, and Blue Prism. Jiffy came in at the very last-minute. We did run a trial using the system, but one of the issues that we had was that Jiffy didn't really have a free trial account that we could use. They did manage to give us a few trials in a sandbox environment, but it was not straightforward like the other RPA vendors where you can download a trial version and run it on your PC. After we requested quite a bit that we needed some sort of sandbox environment for us to try it on, the Jiffy team allowed us to use it in a limited way. It was not as straightforward as the other players.

One of the reasons we picked Jiffy was that they promised us that it is completely a SaaS solution. Our company is running 100 percent on Chromebooks. We are a Google G-Suite customer and we don't have any Windows machines running. Most of the top RPA software didn't run on Chromebooks at that time. When we met Jiffy, this was the first thing that we were promised, but during the onboarding process we found that that was not the case and that it still runs on a remote desktop Windows machine. To me, it wasn't really a full SaaS solution. It still had to be installed on a Windows Server, accessible from the remote desktop application.

I haven't seen fully deployed, end-to-end processes, because I wasn't really involved in the automation of the finance processes, but from what I've heard, it can be done. The development part takes a while because of the complexity. The creation of a bot or a process is not that straightforward. The workflow setup is quite complex. The methodology, compared to the other RPAs, is more like a flow chart kind of thing. They have used a different methodology. It's not so much a question of whether Jiffy can do it, but more of a question of whether the developers can do it.

In theory, because Jiffy combines RPA, machine learning, workflow, intelligent document processing, analytics, and human intervention features, you can automate without having to integrate with third-party platforms, but we still have to test that. We still need to test whether Jiffy can solidly integrate. RPA is more for front-end automation. In theory, it can work with any system, as long as the UI is clear. As long as a human can click the buttons it can be done.

As an agent of change, because I'm doing digital transformation now in my role, I'm always looking for end-to-end transformation, not just some of the processes within the larger process. The Jiffy approach is good, although it's not unique to Jiffy. The system is very much capable. It's just a matter of how people design and implement it.

Overall, it's a good application to start your RPA journey with. If cost is of concern, then start with Jiffy. If cost is not your main concern there are a few premium products out there, but they come with a huge price tag. They're easier to learn and to use. There are a lot of resources out there. You can easily find developers out there who have experience with UiPath or Automation Anywhere. But if you want to start small, and you have a very small budget, and you are expecting very good customer support, then Jiffy is the one. Their future is bright. They just need to improve a few things along the way.

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. 

The most important part is how easy it is to pair the automation. So, it is a canvas that is just drag and drop. You don't have to code, so it is a no-code to low-code solution.

It is good for simple tasks that we have done in the past, e.g., reading the invoices. A valuable feature is the document processing. Usually when we talk about document processing in the market, you just have OCR. Where once you extract the information, you need to program or do some type of data wrangling to actually get the value of it or process it. For Jiffy.ai, they have the machine learning behind it, so we didn't need to code one by one. For example, if you have 5,000 vendors who are sending you different types of invoices, then we are not talking about 5,000 invoices. We are talking about one vendor who has three types of templates, so that is about 15,000 documents to process. Even if you do OCR, you want to extract the information and code it to read this and that. So, Jiffy.ai has machine learning where we don't have to teach all the documents, instead we just need to teach it a few. Then, the machine will already know if it finds this type of information, then that is what it is. For example, the easiest way is the invoice number. Most vendors usually have similar wording: invoice number, invoice NO, and INV. However, in all 15,000 documents, you see that the vendors just play around with this wording. It won't differ much. Therefore, the machine learning knows because of this, you don't need to teach it all 15,000 documents. After about 10 documents, the bot can pick it up themselves and learn about it.

There are not a lot of vendors in the market who provide built-in machine learning. In the invoice, you have multiple things that you want to extract: invoice number, PO, and some other line items. With machine learning, we expect it to know what to extract from, by looking at different templates of invoices. It should know that this is similar. Even though you use the different wording across multiple templates, the machine should know that it is an invoice number. We expect the machine learning should be able to do this, and the Jiffy.ai machine learning is able to do it with 80 to 90 percent accuracy. So far, we haven't had a big problem in whatever the machine learning reads, doing it correctly. If it didn't read correctly, we would have to correct it, then the bot will learn from that, "Okay, this is actually the better way," so it can do better next time.

They are still new in the market. Or, at least, they are still a small player. They require a lot of improvement in terms of learning material as well as the community developers. If you compare Jiffy.ai to an established solution, like UiPath, you can go to YouTube and find a lot of learning material posted by UiPath, partners, and other people in the community. However, for Jiffy.ai, you won't find that available in the market. Because of this it is very hard for us to find talent in the market. Most of the developers in the market are used to the bigger players. For Jiffy.ai, if you search a resume because you are trying to find someone who has used Jiffy.ai, you won't be able to find it. So, when we onboard a new person, we want them to learn this new system, but it is a bit hard for them to pick up because there are no external learning materials on the Internet.

For training, they provide the foundation and advanced training. If you have other issues, they have a support portal, which shows a brief summary of the features. It's not very extensive, like Google Cloud Platform. Sometimes there are things that may not be available in the portal. While other products will also not have available the information in their portal, other people know it. So, you don't have in the community discussions about solutions to a problem that would not be available in the portal. 

We started this project last year in May.

In terms of the portal’s stability, the system is quite stable. We almost never have downtime, and if so, it is very minimal. However, in terms of bot stability, it depends on the server. The bot sometimes gets stuck, then you have to restart the bot, which is something for them to improve.

I do not see any issues in terms of scalability. We can automate a process for a certain department and that process can be very similar to a process of another department. We might need to just change it a little, so we can use the existing solution that we have created. For example, if we create a reconciliation, then the same engine can be used for any big reconciliation tasks in other departments not related to finance. It could be done for engineering, operations, etc. It is very scalable in terms of reusing the existing solution.

They are still quite a small player. Because of that, they can focus on the customer a lot more. If I am comparing them to a bigger player or other players that we have worked with in the past as well, they are a lot more responsive, passionate, and focused on us. They help the customer. 

We can create almost any type of solution in a very inexpensive way. In the past, we bought software to do certain processes. However, with Jiffy.ai, we can build the same software at a fraction of the cost. We no longer had to buy this other vendor's software anymore, which we licensed every year. With Jiffy.ai, we just have to pay the setup costs in the beginning and have them do it for us. We wouldn't have to pay them if we are doing it by ourselves. If you just use their service and do the setup ourselves, then we don't have to pay for the service, we would just need to pay for the service to use the Jiffy.ai platform to build our software. So, in this example, we are actually saving 97 percent of the costs.

The deployment process is quite fast. Because they are small, they could focus on us. With the development, there are not a lot of processes to do it. We just have to set up the server. We can use their cloud, as cloud hosting or hosted in our on-premises. Even if it is hosted on-premises, the setup is quite fast. Training our staff was also quite fast. I didn't have any issues. I was quite happy with the setup.

The initial setup could take about a month or less, but we also had the incremental setup for our sister company. So, we have multiple entities in our company. The first time that we set up, we set up from scratch so there were a lot of other things that we needed to set up, but setting up another tenant for our sister company took a few days.

The reduction of work on a manually basis by project is between 50 to 90 percent. There are some processes where we almost automate the whole thing, and we just need manual handling by a person in certain rare situations. In that case, the reduction could be 90 to 99 percent. However, for certain processes, we can only automate 30 to 50 percent because the rest of the process still needs to be done by a human because of regulatory purposes, etc. So, it's a huge range: 30 percent to 99 percent.

The pricing is quite competitive. As a small player in the market, they are quite aggressive in their pricing. With the features that they offer, it is quite worth the value.

Before we chose Jiffy.ai, we looked into other solutions, especially bigger, more established solution providers, like UiPath, Blue Prism, and Automation Anywhere. In terms of simplicity of usage, Jiffy.ai is easier to use since they are on a webpage. We put a portal on it and everything is available there. The UI is a bit more user-friendly and intuitive. 

In terms of trying to do end-to-end process automation and how easy it is to do it, these are big pros and cons when compared to UiPath. In some ways, they are easier, and in some ways, they are not. I like with Jiffy.ai that we can use Python, but with UiPath, we can't use Python and need to use .NET. I'm unsure if they have enabled Python now. We also have a lot more flexibility with Jiffy.ai, e.g., we can connect to Google or any kind of system without having to do integration. We can just go from the front-end and record it. UiPath has this as well. You need to install Orchestrator on your PC. Then, you can install the design anywhere, because it is web-based, which is an advantage.

In other solutions, you have to install and set it up. If I have a new developer come in, then I have to install the system on their laptop before they are able to do their work. With Jiffy.ai, you can do it anywhere, on any laptop, as long as the laptop has access to the webpage. You just need access to the webpage, then you are able to do it. We control it from the portal as well. So, if I want to shut down or restart the bot, then I just have to go to the portal. I don't have to go to somewhere else, log into the server, or remote desktop to several laptops to do it. Everything is centralized on one laptop in one portal: the user access, the bot management, the task management, and the user interface for the human to manually handle certain stuff. Everything is on one page. This is an advantage over other solutions.

You have to be open to trying new things. There are certain things that if you are already used to other bigger players in the market, then there are things that you like and things that you don't like. However, even the things that you don't like, it is mostly because you are already used to the way the service player is doing it. Therefore, if someone is doing it differently, it could be actually better, though it may not feel like it. I think you will find it exceeds your expectations.

Even with using humans, we have multiple redundancies to ensure there are no errors. The end results are not a lot of errors, though using the bot reduces the redundancy in having people check each other's work.

We are still reducing the full-time employees doing the work, but not up to 100 percent. We still need to maintain certain people for handling tasks that can't be handled by the bot, like manual exception and manual handling. Therefore, we cannot 100 percent automate everything. There are certain scenarios that require human judgment, preventing us from using the bot to do them.

I would rate this solution as an eight (out of 10).

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. 

With the customization option, we can write custom expressions using its compatibility with Python or other programming languages. 

Their web automation is good. It makes the developer's work easy.

Jiffy.ai integrates into existing infrastructure with a very straightforward, simple API. This was not a concern for us at all.

In the latest version, they have a solution called Docube that comes with machine learning. We have used this for the WPS processing, manually adding the keywords over the matching algorithm or things. The system automatically learns new things, and we even have an option to train the bot. This streamlines our automation process, making it easier. Otherwise, we would need one person to identify the new keywords, adding them manually.

In Jiffy.ai, they should add more customization, depending on the plugins available in the system.

We have been using it for the last three years.

I would rate the overall stability as a seven out of 10. When we do customizations, we have to do proper testing before we can deploy. In the initial days, there were a couple of issues, but now things are streamlined.

We have been using WPS for more than one year now. It has been running without any issues, and we are happy with the solution.

Adding in a new client machine is not a big deal. It is a maximum of one hour because setting up a new machine is just taking a clone of the existing client machine. So, I haven't faced any problems with the scalability part. Even when we added two more client machines, it didn't take much time because they have setup procedures.

They have good support. There is a team who is ready to build whatever we ask. That is why we are still using this solution. When we are stuck on issues, there should be a team to back us up.

We had at one point of time Automation Anywhere and UiPath. They were good platforms, but their platforms were quite large. So, they became very rigid to change. Customization, especially our industry, is unlike retail or banking. We are not a very customized or standardized industry. Every business process has to be customized because we change every day. 

We needed somebody, like Jiffy.ai, because we found we could go into the code level and make changes. Their team was there for support. They never complained whenever we threw non-standard practices at them; they never tried to correct us. In fact, they were trying to help us or make things work. 

Their code level changes and scripts have been quite flexible. I remember the first time Jiffy.ai came out. They showed us a demo with two different versions. That is just the power that they had.

I would rate Jiffy.ai as an eight out of 10 versus other solutions that I have used. 

We started with the installation, which is a collaborative model, then we moved to development. From our side, it took two resources from the engineering team to get it implemented in around one and a half months.

We were able to do it within a couple of months. We received great support from the Jiffy.ai team.

We are waiting for the latest updates. They handle the entire migration process.

We still receive ongoing support for implementation issues.

With compliance, we had eight people sitting in different shifts. Now, they have been completely moved into other departments. Reconciliation used to take four hours per person for one country every day, and we have 10 countries. So, if we added three countries that would be a huge increase in size. These were the pending logs that we used to have. They don't even touch the system today because everything runs automated at four in the morning. When the team is back in their office in the morning, everything is ready so then they only work on exceptions. I feel like that huge process was eliminated. 

For the WPS file processing, around 80 percent of its workload is handled by Jiffy.ai. 

For all those departments that we have mentioned, we have not recruited any additional people nor resources. However, the business has been growing at 12 percent on business volume.

I like that it is very cost-efficient. We had licenses with UiPath for a year, and there was not much valuation at all. There is only a brand name, though it does have a beautiful foundation with all the needed plugins.

When using UiPath automation, we could just Google issues if we were stuck with something. In the initial days with Jiffy.ai, we could not get that type of information from Google because there wasn't much of a community. Now, they are trying to add all the things in their support forums. Whenever we write a support ticket, we get a link that was published in their portal. They are improving this too.

The biggest value that Jiffy.ai brings is efficiencies in the code and support from their team. They are there for whatever customizations you want.

It is quite flexible and powerful. I would recommend this solution because of their customization option and support.

This forced us to think about certain places where we automate using RPA. We went back and considered, "Why do you want to build up a system based on manual process, then go and build an RPA?" There are human interventions, which is required, and certain places you can't eliminate a third system. While RPA is in-built into those, we are using only the back-end of Jiffy.ai now where the entire user experience is built by us. So, a bit of innovation is happening. For example, we are doing eKYC approvals next, where it is mandated that everything has to go through the manual approvals. We are planning to automate this, but this time around instead of using Jiffy.ai as a standalone system, Jiffy.ai is on the back-end. Our system will be built on the front-end with Jiffy.ai as the back-end for all this automation.

A task is sent to an email inbox, which reads and processes information continuously. Nobody uses the client machines. There is a dashboard that we built for users to monitor things, like the WPS.

I would rate this solution as an eight (out of 10) because there are still things that need to be improved.

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.

The way Jiffy.ai integrates into existing infrastructure has been great for us. Our company is pretty stringent when it comes to cyber security and integrating with our apps. For every automation that we do, we have our technical architects involved especially since, when we implemented this a few years ago, it was really something of a new technology, knowing that you've got "robots" accessing systems and updating records and altering information. It's a little bit daunting if you're not familiar with it. We've definitely had very strong scrutiny over this platform and this work, and even within that, it's been really successful at being able to integrate.

You still have to integrate with, or at least access, the systems that you're automating within. For example, if you're doing something within SAP, you're still going to need to access data or screens or APIs or something to interact with that system. But the fact that the solution incorporates intelligent document processing, among other features, means we don't have to integrate with another document processing capability or technology. That's a big reason we chose Jiffy.

It has gotten better over time, but some of the training and product documentation could be better. 

I believe this is also being addressed, but a lot of the platform work, as we were putting in new versions or making some updates, was, ironically, very manual. It's improved greatly, but I would imagine that's an area that they're probably still working on, on the backend, to help when it comes to what we need to do for platform support.

We've been using Jiffy.ai for two years now. We are on version 3.2. We're in the process of working through the upgrade to the newest version of 4.2. So we're on the prior version to what is current right now.

Their support is fantastic. They're a startup, they're a young company, and the product has things that can be improved on and worked on, but they have been nothing but there for us, and ready to help solve, and ready to help fix.

Some of those things, early on, were with security. Our cybersecurity positioning and stance on what we expect and what we allow and don't allow, are pretty advanced. It was a pretty tall order to meet a lot of our cybersecurity constraints, so that's an area where we had to do quite a bit of work. The very first bot we put into production fell into the PCI realm. We actually have two environments, one that has to be PCI-compliant, and a regular environment, and Jiffy has been fantastic from a partnership perspective.

A lot of the challenges that we uncover are really internal to our environment, as opposed to the platform. There's a little bit of both, and that's where it really comes back to the partnership with Jiffy. They are always super-responsive in addressing any challenges with the product or the platform and supporting us as we work through how to integrate or automate a certain homegrown application of ours that is probably an outdated legacy application. We've run into challenges along the way, but we partner with our internal folks and then with Jiffy to work through the challenges.

Our deployment was over a period of about three to six months. That was because we were working through internal challenges in getting it set up.

I can't speak to the actual architecture, but organizationally we have a center of excellence which is my responsibility and under my leadership. We partner with, and act as more of a matrixed team with, our technology organization. They help with the platform and all of the configuration management, the change management, migration, and production through the different environments. 

With every area that we're developing a bot for, the business area that we're working with—whether that's finance or pricing, etc.—we have engagement and involvement and leadership involved from that side. It's really like a three-legged stool with our center of excellence making sure that we're working on the right things with a good RPA fit, making sure that we're developing in the right way, following the standards and all the things that we need to do from a good-hygiene perspective. Then, our technology organization makes sure we're security-compliant, and they oversee the change management and the architecture, and make sure we're not doing any harm to existing systems that we're trying to access. And, of course, the business is driving where the opportunities are and what are the business processes that we're trying to automate.

Jiffy helped us with the implementation. We leveraged their professional services for the implementation support as well as ongoing development and for RPA architecture-type support as well.

We looked at Blue Prism and Automation Anywhere. 

When we were looking at the other platforms and tools, Jiffy's willingness to partner and work with us and truly understand our needs stood out.

It is also more user-friendly. The understanding of the business process, and how that translates into what you need to develop and code within the platform, was more intuitive than what we saw in some of the other platforms.

We also liked something that's in the new version, the version we're not using yet, which is the AI, the artificial intelligence capabilities, including document processing and OCR. We knew those features were coming and didn't require getting individual licenses. The strategy of some of the other product platforms was, "Yeah, we can integrate with any other tools," but we liked the idea of having that capability built-in, and part of the actual platform itself.

Lean into Jiffy as a partner for their expertise and knowledge. Be respectful of the partnership and the relationships, and our experience has been that they will really jump in to help and be there for you. You have to be honest and you have to just make sure you've got good communication in place as you're working through things.

We've definitely automated end-to-end business processes, but you have to find the right level of granularity where you can do it all. We haven't explored an end-to-end process for a major value stream within the company. But within large-grain, end-to-end processes, we've definitely identified many sub-processes, and their entire end-to-end workflows, that we have been able to automate.

We haven't used Jiffy's Natural Language Processing yet. It is something that we would like to explore, among many of those capabilities in the next version. We've just got so much on our plate right now, trying to implement in the current version, that we're trying to figure out that time to cut over.

There is definitely some human intervention, but it's really not Human In The Loop. That's another of those things that will be in our next release and that we've got planned for the future. We wanted to crawl-before-we-walk-before-we-run, so we've started with some more basic automations. I'm sure the capability is there. We just haven't taken advantage of it yet.

A lot of the challenges that we uncover are really internal to our environment, as opposed to the platform. There's a little bit of both, and that's where it really comes back to the partnership with Jiffy. They are always super-responsive in addressing any challenges with the product or the platform and supporting us as we work through how to integrate or automate a certain homegrown application of ours that is probably an outdated legacy application. We've run into challenges along the way, but we partner with our internal folks and then with Jiffy to work through the challenges.

In the beginning, I'll admit, I had some concern about Jiffy's stability as a company. They were working through some additional rounds of funding. They were a small startup when we first selected them, but they've gone through some additional rounds of funding. They've done some hiring. They've done some solidifying. I feel really confident about it now. They're in a good spot, and they've made a lot of good progress this year.

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. 

Jiffy.ai is in our cloud and very efficient. It speeds up the very time consuming, repetitive work, which would have taken up a lot of our time, if it was not automated.

It is a one stop solution for automating processes. The modular way that is assigned and works together follows a certain logic, and it encompasses a wide range of processes in a very structured and logical manner.

We did undergo some basic training on how to operate Jiffy.ai Automate. It seems to be relatively easy to implement, if you know what to do.

Their Jiffy.ai Data Interface (JDI) contains something like an audit trail of all the transactions performed. It is easily accessible, then we have an oversight of all the transactions done and the time performed as well as details of the transaction. It is pretty transparent. it is secure and hosted on our system.

They are doing part of the automation process, but it is not entirely end-to-end. They are filling in the gaps for us, and from an end-to-end perspective, they fulfill part of it. They breakup about 25 percent, then 75 percent is actually work done by the staff. However, for the 25 percent that is required, this is actually very time-consuming, especially now during the pandemic. Forwarding documents from home is a pain, as it depends on individual staff and their Internet connection as well as the performance of their ISP. So, we have to take that into account. 

I have a background in IT and I have an understanding of how the RPA system works. If I am programming this, I know it will be a hassle. It is mostly graphical, then something that you just need to type. As long as you know where to press and what to do as well as have some basic programming skills, it is quite easy. The solution has just not closed the gap of being accessible to non-IT users. If you are a non-IT person, then this all looks like gobbledygook. Maybe that is something that can be improved upon.

We have been using it since the end of 2018, between November and December.

Thus far, it has been pretty stable. Its only bottlenecks are due to network performance, or even the issues that we face with IT sometimes. For example, the solution runs on virtual machines, and sometimes those machines don't start properly because there are some issues with logins, etc. Mainly, that is beyond their control. The base system itself is quite stable.

It fits our requirements well. There are no issues whatsoever that I can think of. Jiffy.ai Automate understands the way that our systems work. The process that they are doing involves several systems: the repository system, the ERP system, our SharePoint, and our single sign-on system. It works across all these various systems and their operations well, adapting well to the environment.

So far, it has been reliable, except for once or twice where there could have been some technical issues involved. However, the logic and processing of the system are quite solid.

Unfortunately, because of the pandemic, we have been stuck for a while. We had to adjust our upgrade to version 3.2. However, due to the pandemic, some infrastructural changes will not be able to be done. Therefore, we are still stuck with version 2.7. We intend to upgrade to version 4.0 this year.

In the beginning, we had three processes. Then, we gradually increased it to seven. There have been no issues whatsoever with scalability. So far, it has been working fine.

We have between 2000 to 5000 transactions processed by Jiffy.ai Automate every month.

Jiffy.ai assists 50 of our staff, who are part of the procurement and payroll teams, to do their work.

Thus far, their support has been great. They are quite responsive. If there is something that requires an action from our end, they understand our process flows very well.

It took us around two weeks to implement the first process. So, it was quite fast. Then, we did some verification and validation, then it was up and running quickly. They understood our needs very well and had us up and running within two weeks for the first process, which was quick. Subsequent processes took another additional two weeks.

There were only two staff from our company in the beginning, my colleague and myself. I did the procurement part and my colleagues did the payroll part.

Jiffy.ai did everything from the coding to the support of the system. We were the ones telling them the process. We were also the liaison between them and the business unit. Jiffy.ai codes the process, then they execute, run, maintain, and support it.

Our experience with their setup support was satisfactory. Out of 10 times, there were maybe one or two lapses. This was because of possible miscommunication or they could not answer in time due to some factors. However, most of the time, they were quite responsive.

In terms of the processing of the transaction, Jiffy.ai Automate scores monthly between a 95 to 98 percent success rate for all transactions. The other two to five percent are failures, mostly due to technical issues or an error caused by the staff, e.g., if they entered a wrong number. Then, it becomes an error because the number doesn't correspond to that transaction. It could also be a network outage or system issue. 

Jiffy.ai Automate does the work of around three of our normal assistant level staff doing data processing. Each of those staff probably makes $20,000 to $30,000 per year. 

The licensing is not expensive; it is quite cheap. The expensive part is to support and maintain it. Understandably, we engage them to do a lot of work that could have been done by our own staff, if we had an RPA team. I wouldn't hold that against them. It is just that the cost of support is on the higher side for us. 

If there is an upgrade, they will also charge us for the man-hours required to perform the upgrade. 

We interviewed a few suppliers and tested some of them. Then, we ended up with Jiffy.ai. In 2018, we did talk to the big companies, like UiPath, Blue Prism, and Automation Anywhere. The problem was our center is located in Malaysia, South East Asia, so there weren't many partners available locally at that time. The way that they were going to implement, they would have to look for a partner somewhere in Singapore to come over to our center and perform the implementation. That was a bit of a hassle because there weren't many RPA partners then. Now, before the pandemic, there were all these new partners popping up in the market, growing in 2019. However, back in 2018, Jiffy.ai was the only one whom we could use without going through a middleman. They were the only ones willing to come to our office, sit with us for a few weeks, and analyze the system as well as the process. Then, they got everything up and running. They were the only ones willing to do that, which is the primary reason why we selected them.

Jiffy.ai is not as mainstream as the major players: Automation Anywhere, Blue Prism, and UiPath. Those three are constantly on people's minds when they think of automation. Other companies, like Kryon RPA, have hit the nail on the head in terms of marketing and advertising. Jiffy.ai doesn't have that type of exposure and could stand to have more visibility. 

Other companies market, "Even a dummy can operate the bot by themselves," and Jiffy.ai doesn't. Maybe, they are also being honest. However, they are not marketing themselves like other companies, where even a guy without IT knowledge can operate that bot. However, all these companies which say, "Even dummies use it," neglect the fact that you will not be afforded some granular controls.

I learned that processes can be made more efficient with automation. That is a no-brainer. However, I feel that there are still a lot of misconceptions about the role of bots and people are afraid of them. They are afraid of losing their job due to a robot. RPA is something like a sneaker that allows you to move faster and do more. It is not a replacement for a person. A person is still needed to do the work for the direction you want to go and the target that you need to reach. A bot allows you to do things faster. 

Give them a try, then you can compare them to the other solutions. I think you will be surprised to find that they are quite responsive and understanding of your automation needs. They grasp things quite quickly. All the software automation in the market basically does the same function. What makes the difference from them is the human factor.

We have not used the advanced features. They have things like machine learning and some AI thrown in, but we have not used them. I find that Automate is basically just a descendant use of Jiffy.ai itself. Also, we are not directly programming on it.

Most of the time the process is hands-off and we don't interfere with what Jiffy.ai Automate is doing. When it encounters an error, due to some human error or a system issue, then we will have to go in and perform manual intervention.

We are upgrading our ERP and are unsure if Jiffy.Ai Automate is compatible. If it is compatible, then we will use their solution. We are open to new systems. So, if a better system comes along, then we might consider it.

I would rate the solution as an eight (out of 10).

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.

The workflow engine is definitely a very strong asset of Jiffy.ai, because it is easy to configure. It has a nice user interface. It is also scriptable. It doesn't have a steep learning curve. It is quite easy to learn, so you can become productive very quickly. Up until now, their automation tool combined with the workflow engine has been their strongest asset. It has helped us extract information out of an application which otherwise would have to be done manually. So, it gives us the opportunity to automate a lot of tasks related to extracting information, rather than delegating that to actual people. It has saved us hundreds of hours per month. It has covered the work of two or three full-time operators.

Jiffy.ai's app-based approach is suitable to automating entire complex business processes and to an approach that only automates specific tasks within a process or workflow. My impression is that the solution is so flexible that it can combine multiple applications into the workflow and interact with all of them. For example, in a Windows environment, it can launch one UI application, interact with it through the workflow, launch a process into a remote virtual machine (or interact with a remote service), fetch the result, and then feed this back into the local desktop application. My understanding is that it can deal equally well with tasks within a single process and tasks that span multiple applications in multiple environments.

It can definitely support integration with other third-parties. The combination of all these features can create very powerful applications. Our use of Jiffy.ai so far is a bit limited because we are using desktop workflows and the AI aspects of it. However, combining these can create a powerful set of features for creating more advanced applications. It can be an integral part of a bigger system. For example, you can have a front-end application that is delegating requests back into the Jiffy.ai, then Jiffy.ai will essentially act as the orchestrator for back-end services. So, it's quite powerful. The fact that it has a UI means it is accessible to non-technical people as well. So, you can get from the design phase to implementation phase very quickly.

Jiffy.ai has its own notations for specifying the theme navigation of individual nodes. That notation has the most common structures that you would expect from a programming language without some of the most complex features, like memory management or complex design. I feel it is accessible to junior developers. Now, you can be productive, even if you don't know any code despite designing the workflows. I see this being done in two ways: 

1. You can have someone who is non-technical design the workflow, essentially designing the control flow, specifying the input and output data, and treating this as a black box. 
2. You can have a junior developer who is familiar with the notation that Jiffy.ai is using for implementing individual execution nodes fill in the gaps. Of course, it needs some testing.

This is the development model that I see which is suitable for workflow entry. This means essentially that we don't need to engage expensive senior developers into managing the system. Also, it means that we can get from design to production faster. Essentially, this now provides an advantage, which means we use Jiffy.ai for more automation tasks as we become familiar with the UI and scripting notation.

Initially, in version 3, Jiffy.ai did not have support for containerization. In our environment, we are heavy users of containers and container illustrators. So, the initial deployment option was running based on individual hosts that we deployed in the cloud. That created a singularity in the way that we deployed services in our system. However, in the latest version release (4), they have full support for containerization. This has been a great improvement and one of the driving factors for switching from version 3 to version 4 very soon.

The containerization capability will make a huge difference in our deployment process, because it doesn't create exceptions in the way we would deploy services. All the rest of the system is containers, so if you now have a product from a vendor that doesn't support containerization that means you must have a different process of deploying services, then accompany it with corresponding policies, because we have policies on how systems need to be configured to be reliable, secure, etc. Also, there are the BCDR aspects of it: the disaster recovery and business continuity. So, if you're introducing a new way of deploying services, now you need to have a BCDR plan dedicated to that as well, as opposed to deploying everything using a single model of deployment (mainly containers). Therefore, this will simplify a lot of things, in terms of DevOps.

We have been using Jiffy.ai in production for six to seven months, but we have been working with the company behind Jiffy.ai for about a year.

It is very stable. We haven't had any issues.

We are using just a single node for the workflow engine. I haven't tested the scalability aspects of it.

In production, it is being used by two people. There is one person who monitors the health of the system, then a second person, who is more business-oriented, takes the results extracted from the system.

 Since we are happy with this, we probably use it in other projects. The number of users might increase, but I don't see it being massively used by everyone. It is pretty specialized. I don't expect the number of users to increase sharply.

They have been great. Emails have been replied to very quickly. We get on calls every time that there are issues. 

We are using a workflow engine as a library for some of the services that we have on the back-end, but nothing near Jiffy.ai.

The installation of version 3 was a bit complex. The installation of version 4 has been improved. 

Version 4 took about one hour to deploy, then the UI took about half an hour. We needed to do some configuration afterwards, so we had calls with Jiffy.ai to ensure everything was installed well, going through user creations and some demos. So, we spent another hour and a half to two hours on calls.

I feel some minor assistance from their team is still needed to verify the correctness of the installation, but they have improved this a lot since version 3. Version 4 installation is much simpler. Still, some help from their team might be needed in some cases, especially for the workflow engine piece. The UI piece that is running Windows is very easy to install.

We first deployed the solution in our testing environment, then we ran the demos. With the help of the Jiffy.ai team, we started implementing the desktop automation workflow. They helped us get the skeleton of the workflow, then we started filling in the details. At some point after three months, we moved version 3 of this solution into production.

A single DevOps resource performed the deployment. Maintenance is required a few hours per month, depending on updates from Jiffy.ai, such as installing security patches. Overall, it takes no more than 12 hours a month of DevOps time. It is just maintenance, and that cost is quite low.

It removes the burden of having to do some tasks manually. However, we are just using it in production for a single project. It saves us a lot of time in terms of extracting that information. So far, it has made a big impact, but perhaps we need to use it in a bigger project to get a more accurate estimate of its effect on team productivity.

The solution has definitely helped to reduce errors in our organization. That is one of the driving factors. In production, we have had flawless execution for several months now, without any issues. It has helped to resolve issues because we previously had to do this work manually. For example, in some cases, the operator was forgetting to retrieve some of the information or he was placing the content into a different location, the wrong file, or giving it the wrong file name. There were these little glitches here and there.

It has saved us money from:

• The cost of the resources, who had to do this manually.
• The cost of errors.

Once someone made a mistake during the extraction of the data, then this needed to be discovered and more time was needed to be spent identifying the issue and fixing it. I believe we gained a lot in terms of both productivity and cost.

The pricing and license make sense. They have a model based on the concurrency of the workforce, which is very suitable in our case. The savings are great on the licensing cost.

Technical support has been included so far, and we haven't needed much technical support. In case we did need some help developing new features, there might be some additional costs that we would have to incur. However, everything has been included in the licensing cost so far.

I believe others have looked into a Microsoft-based solution.

The OCR aspect of it is very interesting, although we haven't fully tested it.

In the testing environment, we do use some of the AI features, which are primarily being developed by the Jiffy.ai team, not internally. We see the results, which are pretty good. We provide a number of images. The Jiffy.ai team performs the training, then we use the training model to extract information. So far, the results are very promising. We get the OCR scan results with a very high accuracy. So, we are happy with the results.

From my little experience with our OCR project, it requires some training. I haven't seen any natural language processing features, but I have seen some AI-related aspects that relate to how the image is structured and the location of individual images allows Jiffy.ai to classify words. So, it can define the meaning of a particular attribute and associate that attribute with a tag based on its location.

We like Jiffy.ai because it is stable and easy to use. We don't have any concrete projects for the future yet. We have some ideas for exposing some web services to Jiffy.ai, but nothing concrete yet. Essentially, we want Jiffy.ai to extract information from desktop applications and provide these as web services to third-parties to consume. 

I would rate this solution as a nine (out of 10).