In this episode of the IoT For All Podcast, Interlogica Co-Founder and CEO Alessandro Fossato joins us to talk about the software side of IoT. Alessandro shares a little about his experience developing software for IoT, the role of agile software development – and why the agile methodology is so beneficial to IoT development, as well as how companies can decide whether an off-the-shelf or custom software solution is best for their Applications. To round out the episode, Alessandro speaks to the mountain of data IoT is creating and what’s next for companies looking to extract usable information – as well as how machine learning plays a role there.

Alessandro Fossato has always been a forerunner in the use of disruptive technologies. In 1995 he founded Interlogica, a software house specialized in engineering cutting-edge digital platforms, which includes numerous international groups among its customers. In the course of his entrepreneurial experience, in over 20 years of activity, he has set up several companies in Venice, Milan and Dubai, and so transforming Interlogica into an innovative ecosystem operating in multiple B2B sectors, both in Italy and abroad. 

Interested in connecting with Alessandro? Reach out to him on Linkedin!

About Interlogica: Interlogica is a dynamic ecosystem that operates in the B2B channel. We assist companies in their digital transformation process. With our software production, consultancy and training, we transform critical issues identified by our clients while optimizing their processes, thanks to solutions that are designed to simplify and bootstrap digital evolution in medium and large companies.

Key Questions and Topics from this Episode:

(1:02) Intro to Alessandro

(4:21) Intro to Interlogica

(12:06) Why is agile software development important to IoT?

(16:55) What are the differences between custom software and IoT solutions bought off-the-shelf? Who should choose which?

(23:17) How does machine learning play a role at the edge of these IoT solutions?

(27:28) What’s going on at Interlogica?


– [Ken] You are listening to the IoT For All Media Network.

– [Ryan] Hello everyone and welcome to another episode of the IoT For All Podcast on the IoT For All Media Network. I’m your host, Ryan Chacon, one of the co-creators of IoT For All. Now before we jump into this episode, please don’t forget to subscribe on your favorite podcast platform or join our newsletter at to catch all the newest episodes as soon as they come out. But before we get started does your business waste hours searching for assets like equipment or vehicles and pay full-time employees just to manually enter location and status data? You can get real-time location status updates for assets, indoors and outdoors at the lowest cost possible, with leverages end-to-end IoT solutions. To learn more go to that’s So without further ado please enjoy this episode of the IoT For All Podcast. Welcome Alessandro to the IoT For All show. Thanks for being here this week.

– [Alessandro] Thanks to you, Ryan. I’m happy to be here.

– [Ryan] Yeah, same here. We’re very excited to have you also, on this episode, looking forward to this conversation. Let’s start off by having you give a quick introduction about yourself, talk a little bit more about your background experience anything you think would be relevant for our audience just to get a better sense of who they’re listening to.

– [Alessandro] Hi, I’m a passionate about technology since I was very young and spent a part of my life in and in security of systems, then founded, I founded a Interlogica in 1995. So we are on the market since a lot of years. And I really enjoyed the devolution of the technology in the last 35, 40 years. It’s an amazing evolution. I’ve seen a technology becoming from something for just a few people to something that is a totally pervasive in humanity and in every kind of business. And my company is, you can call it a , but actually working on the software means a lot working on the business models of the companies, helping companies to evolve their business model and something that it’s a something we really love. We started working on the software, then we started to understand that working on the organizations and the business modeling is a very very exciting tool and the two technology and human organizations together are incredible mixture of interesting things to do.

– [Ryan] Absolutely.

– [Alessandro] We have a very wide, we are focused in a pretty wide spectrum of technologies, but we’ve seen in the last year or so that most of that technologies are converging in a big, in something big that is happening, that is the internet of things, the IoT and this is the reason why we are very interested and we are working a lot and creating experiences in that kind of market. In IoT, you can put most of the more interesting technologies available today converge in that topic. You can speak about big data, you can speak about security, you can speak about devices, obviously you can speak about the artificial intelligence, machine learning enterprise nervous systems and many things. And this is the subject where we love to work, yeah.

– [Ryan] Fantastic, yeah, I think that’s that connects really well to kind of my next question which is on that love and passion that you have, tell me a little bit more about the story behind Interlogica and how it was founded, why it was founded what was the opportunity or the problem you saw in the market to kind of form the company from the beginning?

– [Alessandro] Yeah, well, we were a small team of, let’s say hackers in the 80s, four guys, as I said in love with technology, and so we started to create a company because we were in love with the technology more than in the business itself. But sooner we found the, sort of need in the market and the need at that time was of another company, that was able to resolve big troubles for big companies. So for many years, the main activity of Interlogica was to to collaborate with big customers in resolving big troubles in the IT platforms. I mean, in their software platforms. And the mindset we always had, that is a mindset you can, we can define it, the mindset, the composed by hacking approach and Agile approach too all that mindset have been very useful in that critical situations. Then after many years working in that field and it’s a field where we still work today sometimes, we started to have the capability and the opportunity to collaborate in a more a wider projects in a different situation inside the same customers. And we started to work harder in the industrialization of the big processes and mainly for service companies, so not production companies, and then the evolution, why we still work also in that field, we have, we are leaders in Italy in the long-term rental industry. I mean, the companies that the rents for long-term fleets of vehicles. It’s a niche in automotive sector, it’s a very-

– For sure.

– [Alessandro] Very interesting niche, and the market is growing a lot in the last decade. And then as I said, we started to discover the possibility to improve in a different way, the processes and generate value for the customers. And generation of value in this case is related to the using of devices of sensors-

– Right.

– And collect data from that devices-

– Right.

– [Alessandro] And we started to see that it’s, even for example, we found the possibility to help them managing better a building. Let’s say, if you have several floors on the scraper you can optimize the usage of the space without for example changing the way you assign the space inside the office, avoiding to assign-

– Right.

– [Alessandro] Desks to the people, and allowing people-

– Right.

– [Ryan] To book the desks they need when they need, that’s an example. Then we discovered that in the production industry there is a huge need to connect the products with the capability of the products to be commanded by remote, to send data to a cloud platforms and to analyze these data. There is a huge movement in the servitization of the products. So adding abstract layers of software and the services to physical products. And this became another huge market to follow. It’s incredible. It looks like, to me, it looks like every company that is a building is producing something that is electronic, is looking around to the possibility is trying to understand how they can add services to their products.

– Yeah, totally.

– [Alessandro] And also. Yeah, and this is a part of the internet of thing evolution and the, and now this something that is still happening, and we are probably going to see this evolution for many, many years in the future. But on the other side, we see more companies even in the production, I mean industrial companies that produce products that need to put sensors inside their production facilities in order to better understand how the behaviors of their production sites and they need to collect the data from the sensors, and they need to connect the data they collect to data that are coming from their, for example, supply chain from the logistic, from third-party partners. And they need to analyze this data. And so the internet of thing is something that starts a process inside these companies. You start putting sensors, you collect the data around your own facilities and then you start to imagine to look at the data and imagine new optimizations of the processes and of your business and sometimes relating the data with external data and suppliers and all-

– Right.

– [Alessandro] The other data sets you can reach you discover sometimes how to build a new business from the data.

– [Ryan] Absolutely. So one of the questions I wanna ask kind of just about your experience in the space as it relates to IoT is ’cause it seems like you have a lot of background and understanding of kind of the Agile software development side of things, and I’d love it if you could, just at a high level explain what Agile software development is to our audience and how it works in the world of IoT and why it’s something that you recommend and a practice, and why it lends itself well to success on the IoT side of things.

– [Alessandro] Hey, I think, well, from a high point of view Agile is an approach that helps to create communities of knowledge workers that can express their full potential. When we speak about work, nowadays, most of the activities we face every day as Interlogica and most of the activities in our customers are knowledge activities. Agile is a way to create organizations that allows knowledge workers to express at their best. And is a way to manage the projects in order to follow the continuous changes that the market requires. Market is evolving at a huge speed and long-term projects are no more the best way to evolve your business. So, Agile provides a lot of tools that are cultural tools mainly that adds up to organize projects in order to be able to go to the market with solutions that can then retrieve feedbacks give you feedbacks and allows to change fast while you learn from the market. Where can I, where do I see Agile in the IoT? Well, you say, IoT if we speak about today things part of IoT, so the hardware, I’m not very expert about the Agile development of hardware, because hardware normally has, needs certain kind of design and a certain time for design and development. But I see Agile-

– Right.

– [Alessandro] In every activity that involves the software development. These, I mean from the part to the cloud platforms and everything around. Agile is also a way to organize the company itself is not only a way to manage projects is a way organize company around the people. It’s a very humanistic approach to the organization of the company.

– Right.

– [Alessandro] So, where do I see Agile? If you want to go to the market with a product you cannot spend months designing a solution. It’s in my opinion, it’s a much more better to start with a minimum viable product and go to the market as soon as possible, learn from the market and then evolve the product by fast interactions, and this is Agile, basically.

– [Ryan] Awesome, that’s great. I appreciate kind of sharing that, those insights with our audience and kind of giving a quick overview, it’s very helpful. One of the things I did wanna ask you is when it comes to the development of a IoT solution, there are lots of possible, I guess, companies to work with that could help develop a solution, and there are a lot of that can do them custom, and then there are options where you can kind of buy it off the shelf in a sense, I wanted to know your thoughts on kind of the difference between how somebody out there listening should view an off-the-shelf IoT solutions, as opposed to kind of building from scratch custom, and if who should, what should choose one or the other, and why?

– [Alessandro] Why? Wow, wow, what a question. I, from my point of view, you look for off-the-shelf solution when you need to acquire a process and the knowledge about the process from outside. I mean, you buying off a solution that is made for maybe hundreds or thousands of companies like yours and that solution is okay for your business model. I see the development of a customized of the tailored software as the answer for a different kind of need. When you need to create something that is not available in the market or that is cutting edge technology and, or that fits with some specific need, that is useful for let’s say a sort of Blue ocean strategy of your company, just, if I can spend one minute I can make you an example.

– [Ryan] Sure.

– [Alessandro] Imagine a production company that builds line machines that are used by huge companies to build products. And that machines produces thousands of small products per hour, thousands and thousands, I mean, very big numbers. And you need to create, to add a quality assurance layer for that products. So you have to monitor to check the quality, of thousands and thousands and products of products every hour or every minute. And this quality can be monitored through an AI lets say a machine learning system that is able to recognize from a photo, the quality of the product and recognize which product is effective. And-

– Sure.

– [Alessandro] But, and you need to train your specific AI to be able to recognize which product is defective and which one is perfect. This is a very particular process. The process of collection of images from different facilities, for example the process of bagging the images, the process of recognition of training, the specific AI, the quality assurance of that AI the data center needed inside the specific facilities to run the artificial intelligence, the machine learning software, to provide this kind of service, the interior let’s say approach of the AI on the edge, the way to implement it in a very complex wide structure. This is an example, this is a real example about something we did and we are still working on about, it’s already working. It’s a real application,

– Right.

– [Alessandro] This is something that is too very, very complex, and at a certain point, I can assure you that you need certain level of a capability to manage it, to handle technology and to build something that is not existing. So this is an example, maybe it’s not the best example, but in this case, it’s a real case. There is not any ready-made software solution that performs this specific task in the way the customer needs and so you need to build it.

– Absolutely.

– [Alessandro] We are speaking about internet of things. You need cameras around, you need a lot of staff on the production facility, then you need to transfer information in a secure way. Then you need to train AI centrally and then deploy the AI on the edges. So we are speaking about AI on the edge, and it’s like, so we are speaking about a lot of interesting and technologies that I can define, it’s a sort of frontier.

– [Ryan] Absolutely. Now I wanted to shift, before we finish up here and ask a quick ML question, so with IoT, obviously there’s tons of data that’s being collected by these sensors, obviously the scale of that depends solely on the solution, the Applications, how many sensors are deployed, but there are, I wanna kind of ask you how machine learning kind of plays into the management and recognition of those data sets that are coming in and how that kind of is playing a role more at the edge than probably ever before. So how are companies kind of handling that? And what’s that approach look like?

– [Alessandro] Okay, it to handle data is clearly one of the biggest issues, sometimes depending on the volumes clearly. Today, just speaking about the example, I did a few seconds ago, you can use specific devices for the optical recognition that are able not only to, for example, shoot a photo, but they are able internally to create some meta tags that can speed up a little bit, the process and they reduce of the quantity of computational power you need on the edge too. Then you, we normally perform a certain amount of calculations on the edge, not on the camera, but on, let’s say on specific devices or servers on the edge. So we reduce the amount of data we clear up, we refine, we reduce the amount of data that we need to elaborate in the central, in the place where we train the AI. But clearly it’s more, let’s say a game of distributing the management, the usage also of the data in different levels of the process. Machine learning can help. You can, it’s not always feasible to distribute the computational activity on the edge, sometimes we need to collect the pure data in the center of the star. It’s something you need to define case by case depends on what kind of operations you need to perform on the data. But as you said, the volume is sometimes is a big challenge, yes.

– [Ryan] Yeah, absolutely. It’s something and I think we’ve talked a lot about, in just this new world of being able to collect so much data with the capabilities that IoT has kind of enabled organizations to do which is something they weren’t able to do before. And then the question from there is, well, what do I do with the data? How do I manage the data? How do I interpret the data? How do I do it at a, in an effective way? How do I do it at an effective cost? So there’s, this discussion around what gets done at the edge? What gets done at the, up in the cloud? And as for the ML then comes into to being able to kind of have that brain in there to make decisions and just overall kind of lead to the success of these deployments across the board. So, but I think, we’re all on the same page when it comes to that, for sure. One thing I wanted to ask kind of at the end here is just from on the company side at Interlogica, what are some things that maybe you have going on now or have going on in the next couple of months that our audience should be interested in? And then on top of that, if they have more questions or want to learn a little bit more what’s the best way they can reach out or should kind of engaged to kind of get that information?

– [Alessandro] Hey, I, what we are doing and the the kind of challenge we are facing we see for our next future is all related to the, let’s say it’s a sort of a process that let’s call it, from IoT to the executive. So what is the process that goes from the sensor, the physical sensor to the executive or the, owner of a company in order to support in taking decisions and to train machine learning software in order to be able to take decisions or to support the executives in order to be able to take the best decisions, so all the process from the sensor to the executive. And this the huge value that we see in the IoT, I repeat something I said at the beginning of the interview, I really think that’s the point. IoT is a game-changer when you speak about and evolving and the business. And also for the creation of new business models to identify new possible business models, it pass from IoT to data to executive. This is our challenge for the next years, enable companies to transform themselves through the data. If you, if someone needs to connect to Interlogica for sure, the best way is, use our website that is inside that, inside there you can find a lot of information and all the contact details. And we’re very happy to interact with everyone, not only for business, sometimes even just for talk, for a chat.

– [Ryan] Absolutely. Well, I appreciate that. We’ll make sure we link all of that up in our description and all the material that we put out and obviously we’ll share it with you and so you can get it out to your audience, and then we can kind of just double up and get as many people to listen to this episode as possible. I really appreciate the time, it’s, it always means a lot when executives are willing to share a few minutes to kind of enlighten the industry on your knowledge experience what your company is doing all that fun and exciting stuff. So Alessandro, I really appreciate, and thank you for being on the show.

– [Alessandro] Thanks to you, Ryan. I’m proud to be here. Alright everyone, thanks again for joining us this week on the IoT For All Podcast, I hope you enjoyed this episode, and if you did, please leave us a rating or review and be sure to subscribe to our podcast on whichever platform you’re listening to us on. Also, if you have a guest you’d like to see on the show please drop us a note at and we’ll do everything we can to get them as a featured guest. Other than that, thanks again for listening, and we’ll see you next time.

Hosted By
IoT For All
IoT For All
IoT For All is creating resources to enable companies of all sizes to leverage IoT. From technical deep-dives, to IoT ecosystem overviews, to evergreen resources, IoT For All is the best place to keep up with what's going on in IoT.
IoT For All is creating resources to enable companies of all sizes to leverage IoT. From technical deep-dives, to IoT ecosystem overviews, to evergreen resources, IoT For All is the best place to keep up with what's going on in IoT.