Imagine if you were to receive 500 loan applications one day and they just they showed up at your doorstep. That may be one, two, three boxes of paper, depending on all the supporting documentation that goes with that. You can imagine what this looks like. A loan package, on average, is around 600 pages. That’s what Charlie Weidman aims to solve with his company, Buddha Logic, an enterprise content management (ECM) solutions provider that helps companies streamline digital document capture and management. Folks get intimidated by the word robot or automated process and there’s some level of concern about them replacing people, but Charlie says it’s an education and an awareness that helps you eliminate unnecessary work when you start automating more.
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Buddha Logic: Simplifying Life with Charlie Weidman
We have Charlie Weidman as our guest. He’s the CTO and President of Buddha Logic. We’re going to do a deep dive in robots, digital transformation, and financial process automation. If that doesn’t take in and make your toes curl, we’ll dig into it. Charlie, thanks so much for taking the time.
Bob, it’s a pleasure to be here.
You and I chatted in a previous podcast and we talked about what you do at Buddha Logic. I was sufficiently interested in what I thought the possibilities were that I wanted to come back and do a deep dive because I think folks don’t quite get what it is that you do.
I’m happy to share what I can and give you some insight into what robots are, what digital transformation is, and what we can do with financial processes.
We were talking before we started, you’ve been working with a Housing Authority here in Denver.
It takes weeks to process a paper application.
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That’s correct. We’ve seen them through quite a transition. If I were to look back, we were processing truckloads of paper that would show up and be captured in one way or feature or fashion or another. Scanning, sorting, separating, and batching. It would take a week to process a paper application.
I think about that from the end-user, from the customer standpoint, “What’s going on with my app?” It’s somewhere between the loading dock and the fourth floor or whatever. What I thought would be useful is to go back and go “This is where we were years ago.” They didn’t just jump off the cliff and started doing robots. I don’t even know that “robots” were available at that time. Let’s go sequentially what were they doing.
If you really want to start from scratch, you’re an entity that has not even thought about moving away from paper, getting into the digital transformation of paper. This Housing Authority was pretty much right at that space back then. They made a decision to say, “We can’t process paper anymore and the paper we get in, we need to digitize and move it into repository and make it available for our loan processors to take care of our customers.”
Phase one, let’s define a backbone, put together something that allows us to capture that information, and understand what we have and get it into the processors hands within a week’s time. Let’s say seven business days, that would be a huge goal. At that time, it was all over the map, how quickly something got processed. Sneakernet, paper from one desk to another in inboxes and outboxes. You can imagine what a mess that that was.
I had this vision what people’s desks look like.
Just think of the storage space you need to handle that paper because you’ve got to keep these documents for a certain period of time to make sure that you captured everything and then you got to go find it if there’s an issue. Not only do you have paper coming in and you’re capturing it, you have the storage of the paper.
Whose desk is it on? If line two is wrong, who does it go back to?
It was quite a challenge. Phase one was how do we streamline and centralize where the paper shows up. We have a mailroom. We have this concept of a mailroom and we’re going to automate the mailroom. It’s a robot, if you want to look at it. I’m going to start automating how I capture my paper, that was phase one.
In phase one, when the document came in, what automated it? What did you capture?
You’d have a handful of people that would look at the document and they would start sorting the document types. This is beginning the capture, beginning scanning. We’re going to segregate these, then I’m going to scan in batch applications. Here’s a scan and a batch of titles and some insurance documents. That was phase one. Let’s understand the documents that we’re ingesting. They created a job aid for all these people. I have this huge board that says, “When you get this document, this is the pile that it goes into and that’s what we scan it into.”
That automation was really an understanding of content and how the content needs to be separated and then how you feed it into this digital process. Once that was accomplished and they felt good about that, I think that took them about a year, year and a half to get comfortable with. They’ve got this beautiful job aid that represents 160 plus document types. People who they bring into train, start understanding what they’ve got and how to process it.
For our audience, they may not visualize the document mass.
A good way to look at that would be, imagine if you were to receive 500 loan applications in one day. You can imagine that may be one, two, three boxes of paper, depending on all the supporting documentation that goes with that. You’ve got everything from your insurance information, bank information, and appraisal reports. You can imagine what this looks like. I would say an average loan package is around 600 pages. That’s a lot per document. The volume is important to think about because then they have to store it for a period of time.
You have to have a room or a floor in your building. If you’ve captured them, wonderful. They are electronic, but you still have to hang on the source for 30 days or 60 days, depending on what your requirements are for your particular business. That’s the volume. Phase one was, we understand what we’re getting, we have a good idea how to separate it and put it into a process that allows the people who need to review and decide, “This is a good risk. Let’s go ahead and follow through and make this loan.”
We’re at about five to seven days from who knows how long it took before, maybe it was a couple, three weeks. Like you said, the customer is going, “Where’s my app?” I was like, “Let me go find it.” That takes even more people, “Who’s got this loan?” They have to go to the file room and check to see if it came in. You can imagine all the steps that are going on to track where something is at in any particular point in time in a paper process. Even when it’s digital, that first phase, unless it was in front of the processors. What they were learning is now we have to have a way to say, “Once we’ve digitized it, how do we retrieve that information quickly when we have somebody who’s interested in what the status of their loan is?”
By doing phase one, you start identifying those things that you need to think of in phase two. How do we improve our automation process? Part two is, “Can we stop sorting all the paper? If we have to keep doing paper, can I put it in a scanner in a stack and let the software take care of it?” Phase two was to bring in different toolsets that said, “I understand what a mortgage document is. We’re going to create a model that this software does and says, ‘I just scanned 500 pages and I know that there’s a loan app in there. I know there’s an appraisal report. I know there are titles and deeds. I know there’s a verification of employment, all sorts of certifications.” The robot or the machine has to decide, “Here’s what I found, now I’m going to present it to the same mailroom people who have learned what these document types are from phase one and say, ‘Did I get it right?”
Now, the robot is starting to take that approach or the machine, the software saying, “I’ve done my best to understand what you sent me. I’m presenting it to you to tell me if I’m right or wrong. When you tell me I’m right, I’m going to remember that information. When you tell me I’m wrong, I’m also going to remember that, and you tell me what it should’ve been.” We’re talking about machine learning. How does the machine understand when it makes mistakes? The human has to tell it. The robot has to understand, “Next time I see this, I know it should be this document type, not the one I thought it was.” There’s some auto correction that goes on.
The automation process takes some of the work out of the human and lets the machine or the robot do it.
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Phase two took about a year and a half. It was to not only continue the automation process, but now take less of the work out of the human and let the machine or the robot do it. I’m going to let the robot tell me what I’ve got and I’m just going to review. I’ve changed how I do my job. I’m hired to review documents, not sort them and batch them and go look for them. With that piece was how do we grab information about that document so when we store it into a repository that can be searched and retrieved by the processors? What’s the information around it so it makes it easy for that loan operator to go, “Here’s the application to co-borrower and borrower name. Put that in, show me all the documents associated with those two. Maybe they have a loan number, let’s grab the loan number.” Every document that’s in the repository comes back.
What was the reaction from the customer, the people that were submitting the apps as this was going through?
First of all, to say five to seven days, it was a big win. It could have been a couple of weeks before they heard anything back, maybe three. The service level agreement or SLA of people coming into the Housing Authority says, “Within five to seven days, we’ll get you a response back.” They were very pleased about that. It was a big win for them.
How did the folks that started at the beginning with handling the paperwork, what was their response to this change?
There was a lot of relief. That’s how I would describe it. What used to be when they find out there’s a problem, somebody’s complaining, we have a customer that’s not happy, everybody’s in scramble mode to go find all the documents associated with it. With the transition into digitizing that information, there’s one place to look. Occasionally, they still had to go reference to paper because maybe we missed something. Maybe we didn’t get everything that we should have scanned in. In general it’s like, “At least we have one place to go look for a data now.”
For the housing agency, did they start to do notice the cost reduction at that point?
What happened was they started to get more customers. Since they could offer the five to seven-day turnaround, now more vendors were interested in using them. The volume of paper went up. It’s a wonderful problem to have. I’m getting twice as much paper as I used to, which is okay because they set up phase one to handle that. Storage grew, so now they have to ship some of this stuff off site, so you’ve got the paper moving off site. Customer participation, you got more customers. They are thrilled that they have a five to seven-day SLA versus the two to three weeks. For the second phase which is how do we do our automation and make it smarter, continue to let the machine do as much work as possible and let the humans review.
Phase three was how do we reduce paper? How do we grow our customer base and reduce the amount of paper we’re gathering? Paper costs money to store. It takes a lot of time to process even if you have fast scanners. This Housing Authority had four scanners running all the time. That’s a big investment. That’s quite a crew to run those and keep things flowing. How do we shrink the paper? What the Housing Authority did was offer incentives to say, “If you email us or drop this into an FTP site as a PDF or an image, you don’t have to send us the paper anymore.”
These vendors who were shipping boxes of paper daily just saved anywhere from $10,000 a month depending on the vendor to even more for some of these larger vendors. They’ve just saved themselves shipping. A lot of times they want it done as quickly as possible so they FedEx stuff. Imagine the cost savings for their customers. The software has to take the next step, “How do I ingest these electronic files?”
You have to have bandwidth. There were some investments that the Housing Authority had to make. We’re going to set up an FTP site that allows people to put documents there. We have to take another tool set that says, “We have this wonderful model that we’re building that recognizes mortgage documents, understands what they do, and we’re going to now feed it without paper. We need to grab it electronically, do the same thing, split it up, and present it to somebody who understands what the document is.” They say, “You got it right,” off to the processor. That was really the last piece of this automation process to get them to the point where paper started dropping. It took about six months to go from 98% paper and 2%electronic to the opposite, which is 98% electronic to 2% paper.
As soon as the larger vendors understood they could do this, they switched almost immediately. What happened is like, “Let them do the scanning.” Now, I’ve taken a part of what I used to be as a customer service, I’m providing you the service, I’m letting you do that for me. What I’ve offered is you don’t have to send me the paper and we’re going to shrink our SLA down to four days. It’s a huge win for everybody. The customers are actually happy about it. You’ve shifted the burden of the scanning to the customer. There are some little stars you put that because quality of image drives everything with machine learning and understanding what you have. If the image is poor, I’m not going to do a very good job as a robot or a machine and say, “I understand what this is. I’m going to give it my best guess.” Encouraging the vendors and the customers to do a good job, that gets a gray area. It’s hard to enforce that.
The opposite side of that is, “It was really poor. Yours instead of being two days, if the document had been really clean, we’d probably could have gotten back to you in two days, it took four.” SLAs, it basically represents, especially when you’re doing digital transformation, what it takes at the extreme end. If you send me really poor stuff, it’s going to take me my maximum SLA. If you send me really good stuff, I might turn that around in hours depending on how clean it is. What we’ve done is we’ve refined the model. The model continues to grow and get better to understand the documents coming in.
Now, we’re extracting data off of the actual application. We started with classifying and separating, and putting some minimal data around that so it could be retrieved quickly. What we’re going to do is say, “Let’s extract the information off of the document so nobody has to enter it.” Processors have to enter a number of the data fields into their systems. They were happy to do it because before they were working off a paper or whatever. That wasn’t an issue.
The next step is let’s let the machine do the next step. Extract the data, try to determine through the databases that are available if the data that I extracted matches. I know this is good data, I don’t even need to present it to somebody. I can take that data and put it into the system. If it’s not very confident, now I can present it to an operator. It’s that same concept. The machine thinks it’s this, “I need a human to review it, yes or no.” It starts learning.
What happens in the next phase is our model not only understands what it’s looking at, it understands where the information is on it. It extracts it and puts it into this system that helps them make the decision whether or not to make the loan to this customer. It took us about six years to get to that point where we’re extracting data, we’re very comfortable with how the model works and we’re starting to look at how do we expose the workflow portion of it? How do we move this information in such a way where the customer has visibility into the process? No longer is it, “I fed you my documents, I’m waiting for a call or I’m calling you.” I need to show. How do we make it visible?
There’s been a couple of ways that they’ve accomplished that. They set up a portal. The customer has a place to go to put their documents in and because they used that portal, and there’s information that we enforced. The robot doesn’t have to be as smart in many ways because now we’re controlling what the data looks like when it comes in. It has to be a PDF, it has to have certain attributes associated with it. You’ve got to tell me the documents that you’re sending. We’re shifting some of that work into the customer’s hands. It becomes more incentives because now we’re dropping down our SLA to less than a day. Here’s how you incent the customer to start using the tools that allow you to do a better job.
We’ve gone from seven to a day?
When I received that document in my world, in a day, I’m going to get a response back to you.
That’s in year six?
The robotic processes do the manual repetitive tasks that can complement what we're doing.
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Six to seven and now, we have a day turnaround time. We’ve got a way to do a better job of understanding the data coming in. That speeds up our process, it helps with separation, classification, and extracting data. The customer knows within a day I’m going to get a response. Transparency starts. They can’t dial in and look, but when they feed the portal, the document set that get fed has these were pending and within a day, we update that portal and say, “We received these documents. There is still some pending. We never received these or maybe there’s something that we haven’t processed yet.”
They have at least a way to check in without making the calls and sweating if they’re going to get the loan or not. That was year seven. The next step for us is to, “How do we get that SLA within hours?” We were a day at year seven and year eight to nine, we’re now under 45 minutes. “How do we do that? What’s that next step?” We’re looking at robotic processes and what those are. They are manual repetitive tasks that can complement what we’re doing when we’re bringing in that data from the port.
What would be an example of a manual or repetitive task?
Let’s say when we receive information and there is a missing document. You sent me this set, I’m missing a title. Somebody would understand, “The title is missing. I need to generate an email, put some information in it, send it back to the customer,” and say, “I need this back.” There are templates you can use, there are things you can do, but somebody is manually going to do that.
What do we do? We create a robotic process automation that when a certain event happens and a document’s missing, now the robot goes, “I know all the information here, another document that’s missing. I know the borrower and the customer. I’m going to send them an email. I’m going to watch for a response back from that customer. I’m going to watch my inbox and when I see that, I’m going to grab that information, put it back into the process, check the box that says I was missing that, and now it’s not missing. I’m going to update the processor to say, “You’re ready to go.” That was the only missing document. We have it in now. You can move on.
When the document comes in and it’s missing something, what’s the timeframe between when the machine recognizes the missing document and the email goes out to the sender?
Seconds. We are talking as soon as it knows that an event has happened, the robot works until it’s done. It seconds.
Then there’s a queue and at some point, the missing document arrives, then the computer recognizes it.
The robot does the same thing. Often, what we do is we’ll have a robot the watching inbox. This is the stuff coming back from customers. This is where they send it. My favorite thing is you’ve seen all those, “Do not reply at such and such,” we say, “Please reply.” We want the customer to reply. “Send this information right back to me. I’ll take it and make sure the processor gets it.” It’s a friendly way to do things.
What’s been the reaction from the vendors?
They’re embracing it completely. In fact, more vendors are starting to send us better electronic data to begin with. They want to use the portal because now with an SLA of under 45 minutes, that makes this Housing Authority very popular.
The agent can say it’s either gone through or not in a very short period.
Exactly. Visibility, “Where am I at?” “You submitted it 45 minutes ago.” Now, I can see how many documents the processor has already touched and looked at, because it’s on the portal. We update that portal with that information after the processor has done their job.
The potential borrower could be in the seat with the agent and then they’d get an answer.
Yes, 45 minutes is the average, and they’ve actually been lower at times, depending on the quality. If they’ve taken their time with their documents, the process goes very quick. The processors can do more loans because they’re not correcting data. They’re not entering data, they’re reviewing the data. They have the image up and they can go, “The robot missed this. I better correct it and let them know.”
From the Housing Authority side, with this data capture, what’s the second or third level of intel that they’re picking up from all of this?
The embracing automating manual repetitive tasks, that is really the intel. They see error rates dropping significantly. We’ve talked about digital transformation and the robot activities there. As we go into year nine, we’re looking at all of the process that’s surrounding that capture, that transformation of information. There are a lot of departments that still move data from a spreadsheet into a database or distribute spreadsheets.
They’re getting billed for some of the services that the Housing Authority doesn’t do, but they hire out. They get these bills in that are huge spreadsheets that somebody sits down and goes through as many columns as they can. The Housing Authority recognized that they had currently one FTE assigned to handle the monthly bill from one of their providers. That’s one fulltime employee for the whole year. The bill arrives every month and it takes four to five weeks to reconcile the bill.
They only did parts of it because you can’t break even. If I find $10,000 of overcharge, it was probably worth having that FTE do it. If I only find $2,000, you may go, “Do we even really need to go reconcile that bill?” With that second level of intel, what you were talking about is the recognition of, “Let’s get a robot to gather the information, get all that stuff off the spreadsheet, and put it into a database.” Now, I’ve moved the stuff out of a spreadsheet and put it into a database.
The robot learns when there’s an error.
What the robot does is move that data, then it can take the information that’s in the billing system, your ERP system, pull another set of data that represents your accounts payable. I have the accounts payable data. This is what I was told I was going to be built for and here’s the cost. Here’s the actual bill and I’m going to make sure they all match. I’m going to go through every line item I can and say, “You guys overcharged this or it all matches, we’re good.”
I have two sets of data. I have the set that came in from this huge spreadsheet. One of them had over 100,000 rows of data. Imagine sitting down and your job is to go through the spreadsheet. You’re not going to go through every row. You’re not going to go through every column. I would have a hard time doing that.
The robot grabs the information and puts it into tables. One robot does that and then the other robot grabs the information out of the accounting system. You have the third robot that does reconciliation. It’s looking at every line item and it’s doing a comparison and saying, “Here’s what they sent me. Let’s go find it in our accounting tables and let’s see if it matches. If it doesn’t, let’s write the third table, which is all of the things that are either higher or lower charges. If it matched, we’re done. We move onto the next one.”
We took that process that one FTE for the whole year and now the robot takes the spreadsheet that comes in for that monthly bill and processes in less than twelve minutes. This is all on a table. The other robot extracts the data from the accounting system and that takes less than two minutes because that’s data we already know and we understand. We don’t have to do special checks when the other robot is processing the other bill. The third robot goes through it in about five minutes.
We’ve taken something that was about a month’s worth of work for each bill and we reduced it to a day’s worth of process time. People are reviewing it and they have some analytics tools that help the different departments look at these charges. It says, “Here’s the set that was overcharged. Why?” You have employees instead of data entry and are actually doing a review. They’re doing what they were hired to do.
That is this movement towards what are all the ancillary activities that go around in your departments or in this Housing Authority that are manual tasks. I’m going to have a robot do it for you. You’re going to review the results and you’ll have time to do other things. Staffing can stay flat and you can handle more volume. That digital transformation growth that we did over the past years, the staff has stayed the same, in fact, it shrunk and we’re handling more volume. Those are some of the benefits you get out of investing into that process.
For the audience that are out there and they go, “We’re somewhere along this scale. Maybe we’re not eight years ago. Maybe we’re scanning docs. Maybe we’re there.” That would sound intimidating. How should they think about the evolution of their process from the scanned docs and they’ve got some data retrieval that’s okay, but they’re trying to go to the next level, how should they characterize that thought process?
There are two ways to look at this. One of our mantras is start small. If you’ve got documents that are being captured or you’ve got scanned documents now, you’re in a good space because now you have it at least digitally. How do you take that and put it in a location where it can be retrieved? Little baby steps. Literally scan it, have somebody put some attributes on it, put it in repository. That’s a great start and that costs money. What the Housing Authority learned is that capturing your information should be part of the business. It’s no longer a project. You don’t do it once. This is one of the legs of the table that your business sits on. It’s got to be a program. It’s got to be part of the budget. You can’t be skimpy on it.
It’s like employees.
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Has the Housing Authority noticed a difference in their underwriting success?
Absolutely. Performance, first of all and visibility, SLA. I can turn this around quickly so they’d get more customers. Their decisions are better because now I’m not quickly going, “I entered all this data. I’m tired and now I’m going to make a decision.” Now, they’re saying, “Let me really look at this data and see what it says.” They have more time now. Part of the analytics phase which is always behind the scenes, we try to push analytics sooner than later. Get the tools that’ll let you see what your data actually means.
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What’s an anomaly? What’s a standard deviation? What’s out of the band?
Where do I put my min and max? That’s what it’s all about. Where am I willing to take a risk?
Where am I taking the human in making a key decision?
The analytics piece, which is always in the background, that’s the other piece that I would recommend. Whoever is in this position of, “We are starting here.” Start collecting those analytics. Spend the money on the analytics tools because that’ll keep you from gathering data that doesn’t mean anything to you.
When you talk to an organization that’s data intensive, what’s the typical pushback why people won’t do this? I have a hard time figuring out why they wouldn’t.
Usually, it’s cost. It all comes down to, “Am I willing to put that money into my company? Do I have that kind of investment?” That’s usually the biggest pushback. “Let’s start with some simple process automation. Let’s go look at some places in your departments that you were moving data from this place to this place and distributing spreadsheets, whatever. Let’s nibble away at what you’re doing here.”
The various people that are doing that, can they measure when they get to the crossover point where the automation is now saving in money instead of seeing it as an investment, not a cost?
Yes, by doing those smaller projects, those robotic process automations, RPA, is basically, “I’m going to replace that repetitive stuff with a process that’ll do it for me to be accurate.” I’m going to start by saying, “This took me two hours a day to do this process.” I do it every day. I get in at [8:00] AM, two hours later, I’m done with my grunt work. I can go about my day. Some people, maybe that’s four hours, if I can replace that two or four hours for you every day, now I have a metric.
Here’s what I used to do and I shaved two hours off my day every day. You add that up over a year. This is just one process. I can write robots to take over all sorts of those manual repetitive tasks. That’s the way we help gain confidence in automation. Educate people because part of it is education. The other pushback is robots are coming in, and we’re now obsolete.
What we haven’t talked about is when we say, robot. Do you teach them how to build their own?
The tool sets that we really embrace and we show up with are our sets that are designed for a business owner or a business process user. You don’t want a handful of consultants sitting next to your people building robots. That’s very expensive. If I can teach you how to build the robot, you know your processes. I’m there to show you how to build the robot. The tool sets that we employ are very friendly.
Within a month, somebody gets very comfortable building their own robots. They’ve removed the consultant part of it, out of it. The benefit becomes, now I can have my business analysts, perhaps a manager who runs the department who knows all the processes. Let’s give them the keys, let’s let them build the robots, and then they can start using them internally within their own.
In my mind, I’m thinking, “I want a simple process. I’m going to build a robot. I’m going to take a look at the output and then I’ll go calculate it myself manually to make sure that I didn’t blow it up.” For you, where you come in is they say, “We’ve got a problem with what we did in our robot. It’s kicking out a different number.” What happens then? Do you guys step in?
Oftentimes, what happens is and the tools set again really are geared towards you have an environment as a robot author. I can sit down in my environment and I can make sure my robot runs end-to-end. I can grab information. I can go to the web and get information. I can go to spreadsheets and databases. I can take that information and I can process it and put it into a table. I can run reports off of it and I can do checks on it, all before it ever gets pushed in any production environment. That it itself, most people correct their bugs or errors right away. If they’re having struggles, we’d come in and help guide them around those. We’ll show them, “This is why this is broken here.”
If you have a big data center, if they want you to do something, you can do that from here?
We can do it remotely. These days you can do a Zoom session or a WebEx, whatever vehicle you use and we can take over the keyboard. Show them where the issue is, or we can reproduce the issue. Send me the robot. That’s the other thing. These things are powerful. I can package them up, you can send it to me, I can play with it a little bit and say, “I see where your issue is. Here’s what you need to do to fix it.” It’s a very interactive tool. There are a number of them out there too.
We’ve talked on purpose about this one entity because I wanted to take in and drill down and really talk about the evolution of the process. There are other industries that are data heavy. What are the ones that come to your mind that you’ve impacted as well as the Housing Authority?
Medical claims processing. There are huge opportunities there. Interactions with not only the primary care providers, doctors, and nurses because there’s a lot of activity that happens that are all manual. “I had to generate a letter because this particular patient doesn’t have an attorney because it was a claim. Now, I’ve got to send data to them while I’m going to write a letter.” There’s all this ancillary activity that robotic processes can handle for you. Claims processing is huge. We’re finding a lot of government and public entities that don’t have the budget. It’s always a struggle with budget. If I can put together a package that lets you build robots at a very easy cost to get in.
For an easy cost, what does that represent?
Under $30,000, you can build as many robots as you want. Typically the way they work is they only can run one robot at a time. If you need to run five or six at a time, that’s when the cost starts increasing.
If you’re doing one at a time, it is sequential?
Yes. What it does is it queues up. Some of these robots take seconds to run. You can imagine that it should take awhile before you need that second concurrent robot to run. When I say reasonable, I look at $30,000. It includes that robot set. You’ve got a development set, you’ve got training, this is how I build the robots, and now you have this toolset that can start in any department.
When you do the toolset for someone that has a very unique and special process, can they protect that intellectual property?
I would think so. The tool itself allows you to publish and give access to running it. If you don’t want anybody to run your robot, you don’t give them access to it. In a sense it is protected. One of the things I’ve seen lately with the medical industry is there’s data out there in different sources all over the world that they don’t know what they have. If I could get permission to write robots to bring data to help diagnostics, if I’m a doctor or a nurse practitioner and I have a patient with a certain issue, where do you go to look at all the data that’s been collected?
This is predominantly machine learning. At what point in time do you go from the data fetch on machine learning to artificial intelligence where you’re looking at second and third generation thoughts?
Robots are pretty mindless. It can learn from some things. Right now, robots are data collectors and data pushers, and they can be data checkers. It clean things, it can migrate things, it can move data. Now the robot says, “Let me build that repository for you that now my artificial intelligence algorithms can go absorb and now I can go send queries out to my AI to get information back and start doing predictive analysis.”
The robot is a vehicle. I’m a big believer in the wonderful digital workforce. I can have thousands of them working overtime, 24/7. I can go interact with these repositories that maybe I helped create, but I’m actually not smart. If you think about it that way, the robotic process automation is just another worker. I’m accurate and I can move things quickly. If the AI piece is going to continue to grow and evolve, then let me go and talk to it. If you need information out there, I’ll write a robot that grabs that information based on the information you have. I’ll send it over there and then I’ll send you the results back.
I think about inventory control. Let’s say yours had two on hand and every time I look at it, you’re out. In January or February we should have four on hand. If you see this event then you should have eight on him, if you don’t see this event, then I think that’s for me where you look at the intel that comes into your data?
That’s the analytics piece. I’m going to step back from the data I’m collecting, which is what the robots do great. I’m going to let my AI tools and my intelligence tools and my analytics tools start showing me projections, doing the predictions. “Here’s the trend. Let’s make sure we’ve ordered,” and maybe you have the robots interact with that and say, “Let’s get the orders in now. Anytime we hit this peak, let’s send the robots off to go do this information.” Events trigger robots, the analysis can trigger robots. They’re great distributors of data, that’s really where I see that movement.
How do people find you?
We have a pretty good presence, on the web, LinkedIn. You can find us at BuddhaLogic.com. That’ll get you to our website. We’re introducing our Buddha Botz page, which highlights some of the things that we’ve been doing in the robotic process automation world and give you some samples and some ideas. We love doing what we call orientations. I’ve found that that word changes how people look at what they’re about to see. Within a couple hours, I can usually sit down with somebody and build a robot with them.
That’s the power of some of these tools. I’m going to show you how to do this, teach you how to fish and then if you have more complex problems or you want to take that next step, like the Housing Authority did. “We’re going to do this and we’re going to make it a backbone.” Now, we’re finding, “I can start you with some of that robotic process automation, get you comfortable with it. You’re educated, you understand what you can do. Now, let’s look at your backbone.”
For the audience out there, that’s going like, “What skill set do I currently have to have in order to have an intelligent conversation to create a bot within two hours?”
It’s very simple, you know your process.
For me, I’ve got a spreadsheet and go like, “I’m going to do data fetch in my spreadsheet. I’ve got my data aligned and I always take this data and I do the following things with it and it dumps to this other spreadsheet all the time. This is what I do.”
The way you described that to me, I can show you how to build that robot. You can do that for any of those processes. In fact, what you’ll find is you migrate away from spreadsheets and all of a sudden you become a database fan because now everybody can access it instead of me handing you a spreadsheet to go look at and review.
For the social media crowd, let’s say that you wanted to go out on Twitter and anytime anybody says share. You could create a bot that would go do data fetch out of Twitter?
I can have it watch Twitter. A robot can be scheduled to run every second or it can be scheduled to run every day or however you want to do it. It can be run ad hoc. I use to call them little robot apps. If I have access to it and I open up that app, it prompts me for some information and goes off and does a search for me. Maybe it brings back information I’m interested in. Authors can author for other people in the department who really don’t need to know how to build a robot, but they need to be able to use it, because they’re doing searches. HR is a great example.
We need to go find Ruby on Rails programmers, because we have a big project coming up. I’m going to send them a link to a little application that prompts for what’s the programming language and where do you want them. I’m going to search GitHub, I’m going to search LinkedIn. I’m going to get some information back and present it to you. “Here’s what I found.” This takes minutes instead of sitting down and going, “Go out to Google, go out to LinkedIn. Start searching.” You can imagine how much that would take.
I think the challenge is that folks get intimidated by the word robot or automated process. There’s some level of concern about replacing people. My sense is that I’m more interested in the intel massage than the data massage. We came back on purpose to do a deep dive and this is atypical of what we typically do on the podcast. I thought it was important for business owners out there, the worst thing you can do is not call.
I think you’re leaving a lot of money on the table. It’s an education and then it’s an awareness. It helps you eliminate unnecessary work when you start automating more of your world. It uncovers those things right away. “This is data we’re not interested in. Let’s leave it alone. Here are the processes that we improve. In fact, we re-engineered them. We’ve made them simpler because we have a tool that can do it for us now.”
I think about the old 80/20 rule. We’re focused on 80% of the world and 20% of really where we need to focus. We could focus our tools on that 20% and automate the 80% so it pops up every now and again.
Robots don’t do everything. AI is out there but it’s not a robot. A robot, the way I look at it is, if I can tell you I can write, I can take your process, that’s 100%process and I can do 80% of that for you right off the bat, I’m going to leave the other 20% as is because it’s a human. They need to touch it. They need to approve it. I can do 80% of that for you with a robot. Now, that employee or employees have more time to actually do that review and now I’m doing a much better job at it.
Do you find larger physician practices adapting these tools?
There’s not a comfort level there yet. It’s an education. That’s why this toolset is so important because you can do it slowly. I’m not coming in there saying, we’re going to replace your file system. There’s no switch. The robot is as close to a switch as you can get. I can build something within a week to help you with the process. One of the things that we’ve found with the robots when the adaption is good is that the IT department is usually slammed. Nobody has time to build an application for you and you have to get it on the books. You have to get everybody lined up and you have to have the budget for it.
If I’m a business owner or a department head and I can write my robots and then when I’m ready, IT gets involved because we have to put it into production. That changes the IT’s view too. They say, “Those guys have to do the work. If it breaks, it notifies the admin and the person who wrote it, they’re responsible for fixing it. That’s another little thing that showed up with robots that IT gets to take a little bit of break from all the development that they typically have to go and pursue.
This is really what I wanted to do. I really wanted to do a deep dive. For our audience, I hope this is helpful for them to understand what’s going on in this space. I would encourage folks that are listening to reach out to see if it applies and if not now, when. One way or another, it’s going to show up and if they’re not doing it, their competitor’s doing it.
It’s not hard to get started. You don’t have to have a crew. You can come in and you can start a Robotic Center of Excellence in your environment, and you can start growing it. It’s internal growth.
Charlie, I appreciate you taking time.
Bob, thank you. I really enjoy talking with you and I think we’ve covered some nice, adventurous exploration for businesses.
The fact that you’ve been doing this for years for this one company and they continue to expand what they’re doing in an ongoing basis, they wouldn’t keep doing this if they don’t see the value.
They embrace it. It’s becoming more and more part of their culture. People are talking about it. That’s how it works.
Why can’t we do this this way?
Right and when do we get a robot? What’s nice is let me train you and you can build your own. That changes how people look at them. They’re not afraid of them anymore. They see it as a helper, not a replacer.
It’s so much nicer to analyze the data than load it. Thanks so much.
About Charlie Weidman
Buddha Logic founder and president Charles Weidman is “Charlie” to his friends. Spend just a few minutes talking with this warm, friendly, easy-going guy and you’ll be included in that large circle. A skilled and accomplished Enterprise Content Management (ECM) system architect and successful businessman, he is both highly respected and genuinely liked by his clients, his business partners and his team.
And that team is a reflection of his own character. “I founded Buddha Logic to provide clients with business process automation solutions that make their lives easier. But that can be challenging work, so I knew that I’d only want to do it with a team that is a joy to work with,” he says. “I only hire people who are excited about what they do, who take pride in the results they produce, and who are kind, likeable and trustworthy. It’s a strategy that has produced outstanding ECM solutions for our clients and a really positive work environment for us.”
Charlie also places great emphasis on collaboration. “My goal as a participant in this crazy human collective is to share information and provide tools that make the daily grind of a job easier. If I can use what I’ve learned in two decades in this industry to inform and educate others, I feel like I’ve succeeded.”
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