 Welcome to this CUBE conversation that is a part of the AWS startup showcase. I'm Lisa Martin. I've got with me now the CEO of Aizon, John Vitale. John, welcome to the CUBE. Lisa, it's a pleasure to be here. Nice to see you. Likewise, so give your audience an overview of Aizon and what it is that you guys are specifically in pharma and life sciences. Well, you can find that in the name of the company, Aizon, we think of us as leading customers to the horizon of AI in pharmaceutical and biological manufacturing. And we're all about helping our customers take the step into pharma 4.0 and really realize the value of leveraging machine learning and artificial intelligence in the manufacturing process so they can get higher yields and predictability and ultimately better outcomes for their patients. Is your technology built on AWS? Absolutely, from the ground up. Talking about that. Yeah, we leverage as much as we can from AWS's innovation and a few years ago when our founders envisioned the future of manufacturing in this industry and where it needs to go, first thought was go with a leader to build the solutions. And of course, AWS is by far the largest provider of this type of technology. And we're happy to say that we're helping and partnering with AWS to advance the science of artificial intelligence in life sciences. And it's just a natural fit for us to continue to leverage the platform on behalf of our customers. I like that, the AI horizon, excellent. So talk to me a little bit about, the last year has been presented many challenges and also opportunities for people in every industry. I'm just wondering what are some of the changes that we've seen pharma and life sciences companies have become household names, for example, but talk to me about some of the key initiatives in smart manufacturing and what pharma companies require. We're sure, pharma companies and biotech companies alike are looking to the lessons from other industries where AI has been widely adopted. If you look at manufacturing and other industries, it's been widely adopted for a number of years. Tesla is a great example of how to use AI and robotics and data science to advance the efficiency of manufacturing globally. That's exactly what we're trying to achieve here in life sciences. So a lot of the leading innovators in this space have been working in their labs with data science teams to find new ways to collect data, to cleanse that data, make it data that's useful across the enterprise. But they haven't really tackled continuous processing in manufacturing yet. There are a number of leaders that are mapping out strategies and they've begun to go down this path. But most are really looking at how first to bring the data together in a way that it could be democratized and anonymized in some cases and used across the enterprise. There's a model that we've adopted in terms of our product strategy and how we engage customers. And that's the pharmaceutical maturity model which was developed by the bio forum. This maturity models is a great way for companies and vendors alike and innovators to look at how to help advance their capabilities from one level to the next. And so we help customers understand where they are in that journey. And we look for the areas where they can get traction more quickly, they can see value sooner and therefore the adoption would be accelerating across their sites and in different ways of use. Is that maturity model, that pharma maturity model, is it built on or based on digital transformation? Absolutely, it's all about digital transformation. And so the model really begins with pre-digital and you'd be amazed to find, I think, the amount of Excel spreadsheets they're still used in manufacturing today. And that would be what we would consider to be pretty much pre-digital because that data is not accessible. It's only used by the operator or the user. So it's really about getting from that level to breaking down data silos and bringing that data together and harmonizing the data and making it useful. The next level would be about the connected plant, actually connecting machines and data lakes to begin to get more value and find more ways to improve the processes. And then you move up to using advanced analytics and AI and then ultimately have an enterprise-wide adaptive manufacturing capabilities which is really the ultimate vision and ultimate goal every manufacturer has. One of the things John that we've been talking about for the last 14 plus months or so is really the acceleration in cloud adoption, digital transformation as really a survival mechanism that many industries undertook. And we saw all of us go remote or many of us and be dependent on cloud-based collaboration tools. For example, I'm curious in the pharmaceuticals industry, again, as I said, we know that the big three and four household names that many of us have been following for the last 14 months or so. What have you seen in terms of acceleration in pharma companies going, all right, we need to figure out where we are in this maturity model. We need to be able to accelerate drug discovery, be able to get access to data. Has that accelerated in the COVID era? COVID has been the great catalyst of all time for this industry. And I think it was a wake-up call for a lot of people in the industry to recognize that just because we have the highest quality standards and we have a highest level of compliance requirements and we ultimately all think about efficacy and patient safety as our goal to achieve the highest levels of quality, everyone agrees with that. What the realization was is that we do not have the capacity in any geography or with any company to meet the demands that we're seeing today. Demands to get product to market, the demand to get the supply chain right and make it work for manufacturing. An opportunity to partner to get there. You could see that by the way companies came together to partner for COVID-19 vaccine manufacturing production. And so it was a wake-up call. That is time to get over the kind of cultural barriers, risk inversion and really come together to coalesce around a smart manufacturing strategy. That has to be combined with a GXP or good manufacturing compliance standards. And that has to be designed in to the technology and manufacturing processes. Together that's farmer 4.0. Got it, thank you. Let's dig in more to that GXP compliance. I know you guys, we talk about that in different industries, the X for X type of industry. Talk to me about the compliance regulations and your GXP AI platform and how you guys built on top of Amazon help customers evolve their maturity and facilitate compliance. Absolutely. So as I alluded to earlier, one of the biggest challenges is just getting the data together in a place that you can actually manage it. And because there's so many legacy systems and predominantly on-prem technologies in use today. Cloud is starting to gain a lot more traction, but it's been limited to kind of tier two and tier three data. So now we're seeing, you know, the recognition that just having a data lake isn't enough. And so we have to overcome, you know, the biggest barrier is really a version to change. And change management is really a huge part of any customer being successful. And I think with AWS and us, we're working together to help customers understand the type of change management that's required. And it's not enough to say, well, we're going to apply the old techniques and processes and use new technology. It just doesn't work that way. If you're adding people and scaling up people just to do validation work on a brand new platform like AWS offers and like we offer on top of AWS, you just won't get the return on investment. You won't get the outcomes and results you're targeting. You have to really have a full strategy in place. But you can start in small ways. You could start to get traction with use cases that, you know, might not have the huge impact that you're looking for, but it's a way to get started. And the AWS platform is, you know, a great way to look at a strategy to scale manufacturing not just in one site, but across multiple sites because it's really a data management strategy. For us, using AWS components to build our data collection technology was the starting point. So how do you bring this data together and make it easy and with low overhead and begin to use AI at the point of collection? So we built our technology with AWS components to do that. It's called, we call them B data feeders. And those are agents that go out and collect that data and bring it together. We also, because of the way AWS innovated around data management, we can use a multitude of components to continue to build capabilities on top of what we have today. So we're excited to partner to follow the AWS roadmap, but also continue to add value to what AWS does today for customers. Right, seems very symbiotic, but also it gives you, the platform gives you the agility and the flexibility that you need to turn things on a dime. I like how you said COVID was a catalyst. I've been saying that for a year now. There are things that it has catalyzed for the good. And one of those that we've seen repeatedly is that the need for real-time data access in many industries like life sciences and pharma is no longer a nice to have, but it's incredibly challenging to get real-time access to high-quality data, be able to run analytics on that, identify where the supply chain or the manufacturing process, for example, things can be optimized. Give me an example or some examples of some of the use cases that you guys are working with customers on. I imagine things like batch process optimization, anomaly detection, but what are some of those key use cases in which you really excel? Well, it all starts with what we can do around predictions. There's a lot of data science work being done today to understand variability and how to reduce deviations and how to get more of predictions to know what is expected to happen. But a lot of that doesn't get applied to the processes. It's not applied as a change to the process because that requires revalidation of that entire process. Our platform brings huge value to customers and partners because we do the qualification and validation on the platform in real-time. And so that eliminates the needs to go back out and deploy people and track and re-document and re-validate what's going on in the process. So that just takes a huge responsibility in some cases, liabilities off of the operators and the folks analyzing the data. So that's really to get to real-time, you have to think carefully about how to apply AI because AI was developed in a scientific way but you also have to apply it in a scientific way to these critical processes in manufacturing. And so that's only done on a platform. You can't do it on a kind of standalone basis. You have to leverage a platform because you're analyzing changes to the data and to the code being used to collect and analyze the data. That all has to be documented. And that's done by our capabilities that we're using to audit or create audit trails to any changes that are happening in the process. And so that's a critical process of monitoring capability that is almost impossible to do manually. And some would say it's impossible to do manually. So the ability to qualify algorithms to validate in real-time enables real-time manufacturing. And there's a FDA, I wouldn't say mandate but guidance called Continuous Process Verification, CPV, that they will be coming out with additional guidance on that this year. That's really there to tell manufacturers that they should be getting to real-time capabilities. They should be driving their investments and types of deployments to get to real-time manufacturing. That's the only way you can predict deviations and predict anomalies and deal with them in the process and track it. So give me a snapshot of a customer or two that you worked with in the last year as they were rapidly evolving and adjusting to the changes going on. How did you help some of these customers extract more value from their pharma manufacturing processes, understand what it is that they need to do to embrace AI and get to that real-time? Absolutely, so most of our customers are facing the challenge and dilemma that just adding more people and more resources and even upgrading existing technologies or adding more data scientists has a limit. They've reached the limit of improvement that they can make to these processes in the output in manufacturing. So the next natural step would be to say, okay, what science can I apply here and what technology is available to really get to that next one or 2% improvement in the processes? And it's really critical to look at, not just one use case, but how can I address multiple problems using the same technology? So bringing multivariable, excuse me, analysis capabilities is something that's done in every other industry, but it has not been applied here in terms of changing how manufacturing works. Today we can do that. We can do multivariable analysis in real-time. We can predict what will happen. We can actually alert the operator to make changes to the process based on a number of predictions of what will happen in a batch or a series of batches in manufacturing. We've also bring unstructured data into those calculations that wasn't possible before cloud technology came along and before AI was deployed. So now we can look at environmental inputs. We can look at upstream data that can be used for improving the yield on batches. So the main focus today is, how do I reduce my risk around asset management? How can I improve visibility into the supply chain? How can I reduce deviations in these processes? How can I get more yield? How can I optimize the yield in any given batch to improve the entire process but also reduce costs in each step of the way? So the good news is that when you apply our technology and our know-how, there's an immediate positive impact. There's a customer we're working with, a very large customer where we walked in and they said, we have this problem. We've reached a certain level of optimization in yield, we can't seem to get it to go any higher. And within six weeks, we had a solution in place and we were saving them tens of millions of dollars in material loss just in that one step in the process. That's worth hundreds of millions of dollars in terms of finished product. And if you apply that across multiple lines and across multiple manufacturing sites for that customer, we're talking hundreds of millions of dollars of savings. So- Significant impact, significant business impact that your customers, I saw on the website, ROI and what was it, six, I want to get this right. I had it here somewhere quite quickly. But the key thing there is that these organizations actually are really moving their business forward. You just gave some great examples of how you can do that in just kind of a phase one of the project. Let me ask you this, in a post COVID world, assuming we'll get there hopefully soon, where is in your opinion AI and ML for pharma companies, is it going to be something that is for those that adopt it and adopt all the change management needed to do that? Is it going to be kind of the factor in deciding the winners and the losers of tomorrow? Well, I don't want to lay down predictions like that, but what I would say is all of thought leaders out there have openly shared and privately shared that this is exactly where the industry has to go to meet the demands, not just of wrapping up COVID-19 vaccine production on a global basis, which we have to do. It's also dealing with how do we scale up for personalized medicine, which requires small batch manufacturing? How do we turn over lines of manufacturing more efficiently to get more drugs to market, more different types of drugs to market? How do contract manufacturers deal with all these pressures and still serve their customers and innovate? There's also the rise of generics there that's bringing on cost pressures for big pharma, particularly and so these are all moving the industry in the right direction. To respond to these on an individual basis would definitely require the use of AI and ML, but when you bring it all together, there's a huge, huge push for finding breakthroughs to increase capacity and quality at the same time. Yeah, tremendous opportunities. My last question for you, John, is a bit more on the personal side. I know you're a serial entrepreneur. What drew you to AISON when you have the opportunity? I can only imagine based on some of the things that you've said, but what was it that you said this is my next great opportunity? That's a great question because I asked myself that question. So having been in the industry for a long time and having been with very innovative companies my whole career, I knew that manufacturing had fallen behind even further in terms of innovating using the latest cloud technologies and AI in particular. I knew that from running another company that focused on the use of predictive analytics. And so given all the vectors coming together and the market pressure that's happening on the technology, absolutely being a maturity level that we could make these things a reality for customers and the size of the challenge and market opportunity was just overwhelming. It was enough to make me jump in with both feet. So I'm very happy to be leading such a great team and amazing, amazing talent at AISON and super excited about our partnership with AWS and where that's going and solving very complex and very critical challenges that our customers are facing together as partners. Absolutely. Well, John, thank you for joining me today and talking to us about who AISON is, what you're doing, particularly in pharma and life sciences, smart manufacturing and what you're enabling in a COVID catalysis sort of way. We appreciate you joining us here today. This has been a pleasure. Thanks for having me. Likewise, for John Vitale, I'm Lisa Martin. You're watching theCUBE.