NOEL:  Our guest today is Orlando Saez. Orlando is the co-founder and CEO of Aker, a precision crop diagnostic data and service company. We're going to talk about what that means and more generally, about how technology and agriculture intersect. We'll talk about how Orlando got into the agriculture and technology space and who his customers are and what they learn from using specialized drones to monitor their crops. It's an interesting deep dive into a part of the technology world that I, for one, did not know very much about and I hope you enjoy it.

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Orlando, would you like to tell everybody who you are and what you do?

ORLANDO:  Thank you for inviting me. Yes, Orlando Saez, CEO and co-founder of a company called Aker Technologies. I'm based in the Chicago area. I'm a computer engineer. I did masters of computer science and MBA here in Chicago. I worked in software systems in the telecommunications space for many years and then was lucky enough to be in the C-suite of a handful of startup companies. We founded Aker in 2016 with my co-founder, Todd Golly, who's a farmer in Southern Minnesota and Ag is fascinating and beautiful.

NOEL:  Tell everybody what Aker does. Aker is in the tech agricultural sector and I think that a lot of people listening to this will not be aware that there is much of a tech agricultural sector, although if you listen back to the podcast that we did a couple of months ago with Jamey Hampton, Jamey's company is also involved in agricultural technology.

ORLANDO:  The division that we have at Aker is that we believe in finding the cause of crop stress and so, we created that very unique and interesting technology to find pests and pathogens in the field.

NOEL:  So you're trying to help farmers find things that are stressing their crops and then help them deal with them?

ORLANDO:  Yeah, let me explain the problem that we saw because that way, you can relate better about our technology. I started farming as I was invited with my co-founder and I knew nothing about farming five years ago and I landed, as I met him and the first task that he gives me to do is he gives me a clipboard and he said, "Orlando, walk my fields and find how nature craps out of my fields," and it's like, "Okay, how do you do that?" He's like, "Well, you got to walk."

If you don't know much about agriculture and I'm sure that if you travel the savanna of the Midwest, you don't have a lot of people walking corn and soybeans. That doesn't happen. There's not a lot of people walking fields but that happens to be the only way that growers can confirm what disease, pests, and pathogens are afflicting their yields or impacting their yields.

The way the growers deal with this today is very simple. Because they have limited resources, they go to a corner of a field, they lift a leaf and they see bugs underneath and they either spray the whole thing as a precaution or they skip the application altogether of fungicide and insecticide just to save money. Neither one of these actions is financially wise or environmentally sustainable. That's a problem that we saw.

NOEL:  How do you solve it? What technology did you bring to bear on this?

ORLANDO:  We have some pretty cool stuff. Our tech is essentially, for people that are more familiar with the broad category in the industries, we operate in three sectors. We operate in the remote sensing or this IoT, if you will. We have drones, that we have as part of our solution and we have machine learning. All those three things are combined. I'm happy to take a minute and describe how we apply all those three things to solve the problem that we have.

NOEL:  Yeah, start with drones, I think. Drones seemed interesting.

ORLANDO:  As you know, it's an interesting technology. A lot of the agricultural modality or the way that technologies are coming into agriculture is because of this need for automation. We don't have enough people, therefore, we need to automate and we need to do more with less. Drones is an interesting way to fly fields and collect data. If you attach a number of sensors into a drone and you collect biomass index or chlorophyll content through something that is called NDVI, which is essentially tells you that the degree of reflectance of the green, and gives you a stress signature. Is this plant stressed or not? This is like an x-ray.

We use a drone much the same way that there is many drones but instead of flying high, we have our own set of what we call payload: our own cameras and sensors. We created our own part, we call it 'True Cause.' That has cameras, computer vision, CO2 sensor, humidity, barometric pressure and a biometric film and then we dip it. Think of this as a selfie stick hanging off a drone and then you fly the drone and you dip the stick underneath the leaves and so for the first time, we're capturing information at the earliest manifestation of disease and pests, just like a person walking the fields.

NOEL:  So the drone takes the place of a person walking the fields because with that extended camera, you can still get in under the leaves and see what's actually going on more efficiently.

ORLANDO:  Yup. That's exactly it and that's pretty unprecedented. It hasn't been done the way that the technology is used today for drones is you fly high and you come up with a stress signature, but it doesn't give you the cause of the stress. It gives you a stress indicator, which you still need to walk and verify what the heck is going on, in order to know what to apply. We take one step further by not going to the symptoms but identifying the cause.

NOEL:  Is that where some of the machine learning comes in?

ORLANDO:  Yeah. The machine learning comes in in the next stage, which is what happens when you capture pictures? We have this camera sensor that goes under the leaves and takes hundreds of pictures and it's all automated and the drone flies every two and a half acres and we collect data. It's more of a sampling every two and a half acres and at the end of the day, we end up with like 5000 images -- a ton of images. Now, the question becomes, "Now, so what? What do we do with this?" All these images are naturally raster and metadata. Somebody has to sit down and look at that.

The first time out and this is what part of our workflow is, we have a staff of professional agronomists looking at every single image and then, marking what the growers in the industry, chemical suppliers, and retailers care about and we classify things basically, in four types. We're finding an image whether it has a fertility issue, a disease issue, an insect issue, or some other kind of environmental damage and then that becomes kind of our training data set for what we anticipate in the future. We will reduce the reliance on this agronomist desk that we have to have and increase the ability to anticipate and predict and interpret the image as it comes in an automatic way.

NOEL:  You said that you use the drone every two and a half acres. How big typically are the farms the you are deploying this technology on?

ORLANDO:  That's a very good question. Farms in the Midwest are large. You cannot go to Best Buy and buy a DJI drone and expect to fly a thousand acres. Those drones don't work for more than a half hour. The first thing is that you have to work with next generation drones. We work with several. The one that we're starting, it's a company in California, Skyfront. They build the first drone that is a multi-rotor drone that flies for five hours straight and the reason is in the automobile industry, we went from combustion engine to electric and the drones were going the opposite way. We're going from electric to combustion.

This is a two-stroke engine that can fly. Very interesting. It has a lot of very cool technology but it’s a leaf blower and it can fly for five hours straight. We use that technology, first of all. Not cheap, you cannot buy in a Best Buy. That's the frame or the host that we used for our technology. We fly sample fields and our company acts much like a weather company. Back when you had weather stations, you put them very spread apart because the technology was expensive. As the technology for weather stations became more commoditized, you deploy weather stations in every single farm. We anticipate the same phenomena.

What we do today is we sample -- we go and we take a 40-acre field here and then we drive five miles. We do another 40 acres, we fly five miles and then we create a pest map, much like a weather map, across an entire county. We're doing this county by county across all the states in the United States. There's 450 counties that are responsible for 75% of all the production of corn and soybeans. That is our oyster.

NOEL:  Are you then able to aggregate the information across different farms to warn places that might be threatened if there's insects here that might be at the neighboring property soon? Are you able to do that level of shared data?

ORLANDO:  That's part of what we do that is pretty cool. A lot of people are geeking out about the way that this practice exists today is that people have to walk fields and not until this technology is coming in to enable this automation that it has been possible to aggregate. What's cool is as we aggregate this information, you have a number of stakeholders that are very curious about what this means and what it can do to improve productivity in farms and sustainability.

For growers, it's very immediate. If I know what's going on and I can see it, I can act on it right there on the spot. It's basically to support their crop management practices. They have 120 days from the moment that they plant the seed to the moment that they harvest. They have a number of shots to get it right and they need the most information to take the right action. We help them there.

For chemical suppliers, it's a little bit different. They have to mobilize a lot of fertilizer and crop protection chemistry across the country and they don't know what are the indications that are going to be more prevalent this year compared to last year and it changes because you have one element that is chaotic and that's called nature. You can't fool nature. Part of what the chemical suppliers want is more of a projection of where these particular pests and pathogens are moving and so, they intersect our data with other data sets: weather, air flows, etcetera, to know the incidence of rust for instance. Weed rust is pretty bad and moves from the warmer climates to the colder climates. We're in spring now. When we have an outbreak of these diseases, it starts to move north and so, they use our technology and our data to be able to understand the movement of these pests and pathogens so that they can manage logistics.

The commodities guys love what we're doing because they're all about predicting yields, so that they can hedge in commodities and trade. They use our data to improve the alpha to be able to predict the yields for different crops that they trade. It's a pretty broad market out there that we are enabling and that's exciting for us.

NOEL:  What is it like working with that sector, first of all as customers and second of all as users? Are they more skeptical than other sectors? What was different about what you expected when you started dealing with existing agriculture stakeholders from what you actually found when you got there?

ORLANDO:  At first, especially this is a tendency of people who are not in agriculture coming into a new field, we tend to see the world very analytically. We see what can we do right, how can we lever this technology and we have all the answers, potentially related to ROI. What we fail to understand is that agriculture is an industry that has been optimized and has been for thousands of years, so growers are very smart, very entrepreneurial and they're very practical.

A lot of the what we've learned early on is the previous generation of companies that started came with a prediction ball. It's like, "We don't know what's actually out there but let me give you a "conditions are right" model." "Conditions are right" is we don't know exactly what's out there and we don't have people walking the fields but you know what, we know the heat degree days, we know the weather, we have all these other technologies that we can intercept data to tell you what's the potential for a pest and therefore, you need to take action.

The first thing the grower said is like, "No way. I'm not going to rely on a prediction ball." They're very practical, so from that perspective, that has been a wakeup call for many companies, including us in terms of realizing that the way forward was to find pest and pathogens under their leaves and come back with a photo like you walk into their fields with an iPhone and take a picture.

NOEL:  Did you find skepticism because of things that have been done by previous generations of technology companies that were unable to deliver on what they said they were going to do?

ORLANDO:  Absolutely. There is early adopters in every industry and they are willing to take a risk and farmers are no exception. They like to take risk. The problem is in the agricultural space, especially in the last three years, the margins are so small that farmers are challenged to pick technologies that are proven before they can adopt it. Everybody is curious and everybody wants something new to improve yield, whether it's through technology, biotechnology, better seed, all of that but it's very tough when you don't have an economy that is producing high margins.

NOEL:  You know, as I was preparing for this, I was remembering a presentation that I saw more than 20 years ago. More than 20 years ago, I was working as an intern at Apple and they were... I think I'm getting the dates on this right. They were interested in applications of what was the current mobile technology at the time, Newtons, and they were talking to people who are you using Newton's in agriculture, not in America but, I think in India where they were having people use the hot new mobile devices to take crop readings and be able to aggregate personal observation in a way that they hadn't been able to. I think I'm remembering that right.

I definitely remember they're trying to use the current mobile technology to do data gathering that had not been able to be done before. Are you finding anything like that? Have we moved beyond just handing people iPhones and having them take observations? Does that still have a value?

ORLANDO:  There's still a lot of Newtons out there that are attempting to put stuff and I'll give you one headliner here. If you want to show up to a farmer and be more credible, let me tell you something not to do. Every presentation that I've seen and Apple is no exception and some of these big companies start like this, "We're going to have to feed 9.5 billion people by the year 2050 and we need to grow double what we do today and oh, by the way, my tech will feed the world". That is such a stupid response to how to deal with agriculture because it undermines the complexity of the industry.

Like I said before, growers are good at what they do, so part of what I see that needs to happen more is not the technology itself but the technology and function of the use case that drives value. There's not a lot of that. What you see is all these efforts that are disjointed. You know, Microsoft is doing a better effort at it this time around. They have a department or a group called 'FarmBeats,' where they're taking farms in India and applying their technology and they have some serious investments to understand how the technology translates into value for the growers but it has to be translated for the grower, not for what the technology company thinks that it can be used. I think that's a positive trend in the industry but there's a fair amount of things that are still throwing stuff on the wall and see what sticks.

NOEL:  Yeah. I think there's a long history in technology of technologists assuming they know everything about an industry and blithely assuming that the technology was going to fix problems when they wildly underestimated the complexity. Is there another example of some complexity in the way growers work that you had to take into account that you weren't aware of when you started?

ORLANDO:  Well, it's the intersection of data. The biggest complexity here is all these technology and infrastructure in farming is very advanced.

NOEL:  Do you see a difference between the people who are actually purchasing your products and the people who are actually using it? Do the customers and the actual users have different needs or different desires? What kind of research did you do on the ways that these products are actually used, literally in the field?

ORLANDO:  I have only one example and that relates to our own service. When we started the company, we started a drone fleet service or mapping service, where we kind of think of us as an Uber of drones as far as the backend and then we would dispatch drone operators, which we do throughout the country. We have an infrastructure of over 30 pilots in 21 states and we fly high. The first wave of companies that did this were analyzing the obvious. There's a lot of drone companies that will collect data and would go to the grower and say, "Hey, grower. Pay me $250 an acre and I will fly your fields and then tell you something that you may not know," and the first wave of companies came in and they were analyzing the obvious.

NOEL:  What kinds of things are they saying that are obvious? They're telling the farmers things that they already knew?

ORLANDO:  NDVI gave you stress or thermal. Let’s pick thermal. I'm going to do a thermal because it gives you, obviously the number one component that is needed to grow healthy  plants is water. A thermal signature gives you the degree of water that I can see, so I'm going to find where there is deposits of moisture or water. Well, guess what? Growers know exactly where they have drowned out areas, etcetera so you're coming back, paying for money to tell me something that I know? Thank you. That's the first thing.

The second wave, going back to the question that you have, which I think is very good in terms of who's the user and who's the buyer, is recognizing that that is different. Let me give you an example. Flying drones in agriculture is like you having a broken arm and walking into a radiology to get your x-rays done. What can the radiology do for you? Nothing, so why would you pay a radiology to do that? It's a referral service, so the first wave of drone companies was going to growers with the presumption that there would be an ROI in imagery.

The imagery itself does not have an ROI. The ROI is leveraged by the action from the image or informed by the image. In other words, here's an image, by the way, you need to put less chemistry, so the value is on the chemical supplier or the crop consultant, not the grower. We learned that early on and that's why our buyer is not the grower. The buyer is the chemical supplier, so we have national relationships with Bayer, BSF and they send us, similar to a radiology or a nurse and a doctor, where the nurse, they send us, do the vitals on the farms, the grower doesn't have to pay and then they make and leverage that data for the purpose of selling the right chemistry so that you can apply the right product for the right problem.

NOEL:  I think I didn't realize this early on. Your actual customers are not the growers themselves. It's the supply companies and you're providing analysis, both for them and to the growers about various interventions that they would need to make based on your data. Am I getting that right?

ORLANDO:  Exactly. It's a little bit of both. We do have to have the grower so that we don't have too much power by the suppliers with us and we appear more independent. We do have both stakeholders as part of our platform.

NOEL:  And you provide the drone pilots too?

ORLANDO:  Yes. Again, there's an Apple versus Microsoft argument in the drone space which is the do-it-yourself or the service model. As a grower, would you buy your own drone and would you want to do this yourself or would you want to call Aker as a service provider to do it. I think there's merit for both. The challenge is as these technologies and if you fly any drone, you probably know this, every year DJI comes with a new drone, so the capital and costs and the obsolescence rate of this technology, it's annual. Just like your cellphone. The growers, they don't have a lot of infrastructure capital to subsidize for this compared to what they recover on buying, say a plant or a sprayer or a combine.

I think that there is space for both. There is a market for selling the drones themselves and selling a package that they do it. We are not that. We are more of a service provider and so, all of our fleet, all of our people, it's all turnkey. We dispatch people, we have all the software infrastructure to be able to facilitate the logistics and all the workflow to do all the processing, the interpretation, and the delivery as an API into our customers.

NOEL:  This is a question I genuinely don't know the answer to at all. How hard is it to train drone pilots for something like this?

ORLANDO:  It's not very hard and the reason is drone technology has come quite a bit in terms of automation. The only thing that you have to do is essentially, load into the embedded software of the autopilot in the drone, just a boundary file. You upload a KML or a shape file of the GIS boundaries and then the drone basically works by itself similar to your iRobot vacuum cleaner in your house. It goes up and down in this particular case there's no obstructions. It goes in and out and it collects pictures every so many, depending on the speed and the programming of the drone and the characteristics and then you have to create some overlap. You take pictures of the air as you're looking down with some overlap, so that you can stitch them together.

The first process when it comes back is you download this enormously rich, very high-resolution folder with a ton of images. You throw it into a GIS process that stitches this and creates an orthomosaic composition of the entire piece and then you start to do some metadata extraction from there and then you need to do some interpretation but that's how the mapping service works.

NOEL:  Just to do a couple of definitions. GIS is the actual geographic data, right? And a shape file is a map that has specific boundary data embedded in it, so you're actually giving the drone a map of the area and telling it the boundaries of the area that it's supposed to fly in and then it just goes off like a Roomba or whatever?

ORLANDO:  Exactly. GIS is a map of the vectors. These are the fences of the field that you should fly and it has GPS specifications that gets reconciled as the drone is flying. It's pretty autonomous. When our pilots go to the field, we have the work orders, the software is already built into the drone or uploaded and then they go there. The only thing that they typically have to rely on is having a tub full of batteries because they don't charge batteries on the go. They have to do it at home. For battery, fly it, it's a half hour. Let's say, it hovers, we fly over about 40 minutes, move onto the next one and keep going. That's for the mapping service.

NOEL:  What in the software space is the most complicated or the most industry-specific piece? I guess I'm kind of wondering to what extent you can use in general, for instance machine learning techniques or to what extent you need to develop new techniques that are more focused on the kinds of data that you're using?

ORLANDO:  In our probe, which is kind of our next generation, that is the exciting part. The machine learning is very necessary and one of the things that we're learning, as we're doing some skunkwork with machine learning is that 80% of the effort is having a well-defined training data set. It's all about the training set and there's no way to go about doing this other than brute forcing, so you have to go through the work of doing the tagging manually and capturing enough indications -- thousands of indications -- of northern corn leaf blight or aphids or any one of these diseases or insects and having a pattern matching, so that then you can rely on our machine learning engine to be able to do the rest.

I believe that the industry has enough stacks of engines. There's enough service providers coming up emerging every day that we may not need to build our own stack. We have the training side, which is the heavy lifting and what we're doing is evaluating different options for who can do better prediction. We're in a very fortunate position to be able to do that because we have such a massive data set to be able to do that. We're shopping for vendors every day to be able to know which one plays better, nicer as we are trying to improve the score prediction and the probability of success and doing the matching and eliminating or reducing our false positives and were in the middle of that right now. That's where we are.

NOEL:  As you look forward, what sort of capabilities do you see agriculture technology being able to deliver in the next few years? What are growers going to be able to do that they weren't able to do or what are they going to be able to do more efficiently than they can do now.

ORLANDO:  That's a loaded question. There's a lot there. From the perspective of our technology, I think that our next generation drone, which is the probes that flies into the canopy, completing the cycle for us to really do a good job in flying our probe into the canopy across different crop types, not just corn and soybeans but specialty crops, tree crops, nuts where we're getting huge demand, how do you fly a drone and you fly it in a smart way and the z-axis under the leaf, it’s a frontier that we are in the breakthrough category now.

For the growers themselves, they're looking for better evidence, especially in season. Five years ago, the number one, more popular thing to work is optimizing nitrogen use. That was very expensive input. Let's make sure that we don't spend much. Let's optimize that. I think the industry has done a fairly good job in coming up with models to optimize nitrogen. This time around, what growers are more concerned with is diseases. What's happening and some of this is borne out of climate change and just nature in general, when you create a super chemistry to be able to kill a bug, the bug evolves by Darwin, to be able to generate a mutation that is a stronger bug. The super chemistry is being met with super killers and so part of what is interesting in this industry is having the opportunity to engage in technologies that are relevant to provide the growers with the level of resolution and timeliness on a very small window of time that they have between the moment that they plan to the moment that they collect or harvest, which is 120 days roughly and that's pretty tough to do. I think that's where growers are trying to optimize the use of inputs, what seed matters, so there is a lot that you see in terms of the biologicals and the seed that are genetically modified or molecular biology work that is going on. I think those are the two main things. If we have better technology for the biotech and also for crop management, I think that's what growers are really, really hoping for more to improve their yield and their profitability.

NOEL:  What got you into the sector in the first place? What made you interested in farming?

ORLANDO:  I'm a foodie. I'm the first in my family to go to college. My hometown is Dominican Republic and Puerto Rico. I just got super inspired to be able to connect and do work in something that was completely different and I found agriculture to be fascinating, that alongside the fact that I met an amazing farmer. You know, if there's something that I invite anybody to do is we have lost the connection in this country, especially in urban areas, with food and what is the source of food. We think that food comes from Whole Foods. You know, you hear all these stories. If there is a recommendation that I have for people, read a good book or if you can, at least watch two movies, I recommend anybody to watch, check Michael Pollan, In Defense of Food, a very good movie. Check also the Food Evolution. It's narrated by Neil deGrasse Tyson and it has Robert Fraley, which is the CTO of Monsanto. It's a fantastic movie or if you can hang out with a real farmer, like I did, that's what you need to do.

NOEL:  How did you go from 'I'm hanging out with a real farmer' to 'I'm going to put my time and effort into a company that's going to help this sector?'

ORLANDO:  Yeah, it's interesting. I try to promote university students and high schools to get involved in STEM education much because of my projection of the way that I have become successful and I'm lucky so, I'm in Dominican Republic five years ago, I'm doing a talk related to technology and I brought a drone -- Phantom 2 Plus of DJI, one of the early versions. What do you think every kid is looking at this new thing, a plastic that can fly? Everybody's going, "Oh, this is cool," so I need to figure out a way to make this relevant, not just say technology piece but what's the use case.

In emerging markets, there's really two industries that kill: tourism and agriculture. Agriculture seems interesting and I started making calls to find out somebody who can answer the question, "So what? Who has used drones in agriculture for something useful?" and that's how I learn and I found Todd Golly, a farmer in Southern Minnesota who is now my co-founder. I invited him to Dominican Republic four years ago, a balmy February in Minnesota. I said, "Todd, would you come back with me and hang out in Dominican Republic?" and he said, "Hell, yeah."

We traveled the world. We went to Brazil, Guatemala, Dominican Republic, Malaysia and I went with a farmer and came back with a partner. For me, I've been lucky to be in companies that have been successful and a lot of it has to do nothing with the technology but it has to do with the practitioners approach -- what is the use case. For the last four years, I've gotten in character. I travel just about every week to Minnesota, to the farm and then you have to get into the industry to understand how it works, to understand how to drive and bring value.

NOEL:  Excellent. Normally, I ask people just where to find you to continue the conversation and I want to people to know that too but also, you mentioned a couple of movies and things like that. Are there some specific resources for people who are interested in agriculture tech, places that they can go to learn more about technology and software as it applies to agriculture?

ORLANDO:  Yeah. There are a handful of very good resources. I would say, similar to what happened in digital tech, where in the early days, there were a number of accelerators and trades, conferences that I would say get involved. There is a good group that deals with more of the specialty crop category in the West Coast. It's called 'THRIVE', it's an accelerator. They are an adjunct to a group that is part of Forbes, that puts the largest agriculture show where a lot of the senior leadership hangs out in Salinas, California. It's called the 'AgTech Summit.' It's a pretty good group and throughout the country, there is in St Louis. There's 'The Yield Lab,' which is an incubator-accelerator dealing with this as well.

I would say, you dial in and you type 'agtech' and you'll get a number of very good resources, Pro Farmer, PrecisionAg, the trade, the publishers of magazines. They put phenomenal shows related to this. If you really want to get deeper into it, you also want to do where farmers go to get loaded with technology and knowledge. I just came a couple of weeks ago, the largest show of this kind. It moves every year but it's called the 'Commodity Classic.' It happened in Orlando a couple of weeks ago. Very, very good. Farm Progress is the largest agriculture show that features a lot of the equipment: big John Deere tractors, etcetera. It's in Iowa. It's massive. It's the largest of its kind.

I would say, if you want to get involved, get in character, make sure that you find a good mentor farmer that is progressive, that is technology savvy and if you're in the technology side, get involved with one of the agtech accelerators and there's quite a few and emerging and there's new every day, not just in the US but around the world.

NOEL:  Thank you very much. Where can people find more about you and about Aker, to continue this conversation directly with you?

ORLANDO:  Check out the information on my bio, on the page that you have and Aker's website is Aker.ag and I love to continue the conversation.

NOEL:  Yeah, that .ag is important. There are a lot of Akers out there.

ORLANDO:  Yes, I know.

NOEL:  Well, Orlando, this has been really interesting. Thank you very much for being on the show. I really appreciate it.

ORLANDO:  Thank you.

NOEL:  Tech Done Right is a production of Table XI. You can find us on the web at TableXI.com and TechDoneRight.io and you can find us on Twitter at @TableXI and @Tech_Done_Right. You can listen to us wherever you get podcasts. The show is hosted by me, Noel Rappin. I'm at @NoelRap on Twitter. The show is edited by Mandy Moore. She's at @TheRubyRep on Twitter and of course, if you like the show, please tell a friend, a colleague, a pet, your social media network or tell me. That would all be very, very helpful and review on Apple Podcast helps people find the show.

Table XI is a UX design and software development company in Chicago, with a 15-year history of building websites, mobile applications and custom digital experiences for everyone from startups to story brands. Find us at TableXI.com where you can learn more about working with us or working for us and we'll be back in a couple of weeks with the next episode of Tech Done Right.