(upbeat music) >> We're back at AWS re:Invent 2021. You're watching theCUBE and we're really excited to have Adam Williams on, he's a senior director of engineering at Iron Mountain. Som Shahapurkar, who's the product engineering of vertical solutions at Iron Mountain. Guys, great to see you. Thanks for coming on. >> Thank you >> Thank you. All right Adam, we know Iron Mountain trucks, tapes, what's new? >> What's new. So we've developed a SaaS platform for digitizing, classifying and bringing out and unlocking the value of our customer's data and putting their data to work. The content services platform that we've developed, goes together with an IDP that we call an intelligent document processing capability to do basic content management, but also to do data extraction and to increase workflow capabilities for our customers. >> Yeah, so I was kind of joking before Iron Mountain, the legacy business of course, everybody's seeing the trucks, but $4 billion company, $13 billion market cap, the stock's been on fire. The pandemic obviously has been a tailwind for you guys, but Som, if you had to describe it to like my mother, what's the sound bite that you'd give. >> Well the sound bite, as everyone knows data is gold today, right? And we are sitting figuratively and literally on a mountain of data. And now we have the technology to take that data partner with AWS, the heavy machinery to convert that into value, into value that people can use to complete the human story of healthcare, of mortgage, finance. A lot of this sits in systems, but it also sits in paper. And we are bridging that paper to digital divide, the physical and digital divide to create one story. >> This has been a journey for you guys. I mean, I recall that when you kind of laid this vision out a number of years ago, I think he made some acquisitions. And so maybe take us through that amazing transformation that Iron Mountain has made, but help the audience understand that. >> Transformations really been going from the physical records management that we've built our business around to evolving with our customers, to be able to work with all of the digital documents and not just be a transportation and records management storage company, but to actually work with them, to put their data to work, allowing them to be able to digitize a lot of their content, but also to bring in already digitized content and rich media. >> One of the problems that always existed, especially if you go back to back of my brain, 2006, the federal rules of civil procedure, which said that emails could now be evidence in a case and everyone like, oh, I don't like, how do I find email. So one of the real problems was classifying the information for retention policies. The lawyers wanted to throw everything out after whatever six or seven years, the business people wanted to keep everything forever. Neither of those strategies work, so classification and you couldn't do it manually. So have you guys solved that problem? How do you solve that problem? Does the machine intelligence help? It used to be, I'll use support vector machines or math or probabilistic, latent, semantic, indexing, all kinds of funky stuff. And now we enter this cloud world, have you guys been able to solve that problem and how? >> So our customers already have 20 plus years of retention rules and guidelines that are built within our systems. And we've helped them define those over the years. So we're able to take those records, retention schedules that they have, and then apply them to the documents. But instead of doing that manually, we're able to do that using our classification capabilities with AI ML and that Som's expertise. >> Awesome, so lay it on me. How do you guys do that? It's a lot of math. >> Yeah, so it can get complicated real fast, but at a simple level, what's changed really from support beta machines of 2006 to today is the scale at which we can do it, right? The scale at which we are bringing those technologies. Plus the latest technologies of deep learning, your conventional neural networks going from a bag of characters and words to really the way humans look at it. You look at a document and you know this is an invoice or this is a prescription, you don't have to even know to read to know that, machines are now capable of having that vision, the computer vision to say prescription, invoice. So we train those models and have them do it at industrial scale. >> Yeah, because humans are actually pretty bad at classifying at scale. >> At scale like their back. >> You remember, we used to try to do, oh, it was just tag it, oh, what a nightmare. And then when something changes and so now machines and the cloud and Jane said, how about, I mean, I presume highly regulated industries are the target, but maybe you could talk about the industry solutions a little bit. >> Right. Regulated industries are a challenge, right. Especially when you talk about black box methodologies like AI, where we don't know, okay, why does it classify this as this and that is that? But that's where I think a combined approach of what we are trying to say, composite AI. So the human knowledge, plus AI knowledge combined together to say, okay, we know about these regulations and hey, AI, be cognizant of this regulations while you do our stuff, don't go blindly. So we keep the AI in the guardrails and guided to be within those lines. >> And other part of that is we know our customers really well. We spent a lot of time with them. And so now we're able to take a lot of the challenges they have and go meet those needs with the document classification. But we also go beyond that, allowing them to implement their own workflows within the system, allowing them to be able to define their own capabilities and to be able to take those records into the future and to use our content management system as a true content services platform. >> Okay, take me through the before and the after. So the workflow used to be, I'd ring you up, or maybe you come in and every week grab a box of records, put them in the truck and then stick them in the Iron Mountain. And that was the workflow. And you wanted them back, you'd go get it back and it take awhile. So you've digitized that whole and when you say I'm inferring that the customer can define their own workflow because it's now software defined, right. So that's what you guys have engineered. Some serious engineering work. So what's the tech behind that. Can you paint a picture? >> So the tech behind it is we've run all of our cloud systems and Kubernetes. So using Kubernetes, we can scale really, really large. All of our capabilities are obviously cloud-based, which allows us to be able to scale rapidly. With that we run elastic search is our search engine and MongoDB is our no SQL database. And that allows us to be able to run millions of documents per minute through our system. We have customers that we're doing eight million documents a day for the reel over the process. And they're able to do that with a known level of accuracy. And they can go look at the documents that have had any exceptions. And we can go back to what Som was talking about to go through and retrain models and relabel documents so that we can catch that extra percentage and get it as close to 100% accuracy as we would like, or they would like. >> So what happens? So take me through the customer experience. What is that like? I mean, do they still... we you know the joke, the paperless bathroom will occur before the paperless office, right? So there's still paper in the office, but so what's the workload? I presume a lot of this is digitized at the office, but there's still paper, so help us understand that. >> Customers can take a couple of different paths. One is that we already have the physical documents that they'd like us to scan. We call that backfile scanning. So we already have the documents, they're in a box they're in a record center. We can move them between different records centers and get them imaged in our high volume scanning operation centers. From there-- >> Sorry to interrupt. And at that point, you're auto classifying, right? It's not already classified, I mean, it kind of is manually, but you're going to reclassify it on creation. >> Correct. >> Is that electronic document? >> For some of our customers, we have base metadata that gives us some clues as to what documents may be. But for other documents, we're able to train the models to know if their invoices or if their contracts commonly formatted documents, but customers can also bring in their already digitized content. They can bring in basic PDFs or Word documents or Google Docs for instance, but they can also bring in rich media, such as video and audio. And from there, we also do a speech to text for video and audio, in addition to just basic OCR for documents. >> Public sector, financial services, health care, insurance, I got to imagine that those have got to be the sweet spots. >> Another sweet spot for us is the federal space in public sector. We achieved FedRAMP, which is a major certification to be able to work with, with the federal government. >> Now, how would he work with AWS? What's your relationship with them? How do you use the cloud? Maybe you could describe that a little bit. >> Well, yeah, at multiple levels, right? So of course we use their cloud infrastructure to run our computing because with the AI and machine learning, you need a lot of computing power, right. And AWS is the one who can reliably provide it, space to store the digital data, computing the processes, extract all the information, train our models, and then process these, like he's talking about, we are talking about eight, 12, 16 million documents a day. So now you need seconds and sub second processing times, right? So at different levels, at the company infrastructure level, also the AI and machine learning algorithms levels, AWS has great, like Tesseract is one the ones that everyone knows but there is others purpose-built model APIs that we utilize. And then we'll put our secret sauce on top of that to build that pathway up and make it really compelling. >> And the secret sauce is obviously there's a workflow and the flexibility of the workflow, there's the classification and the machine learning and intelligence and all the engineering that makes the cloud work you manage. What else is there? >> Knowledge graphs, like he was saying, right, the domain. So mortgage is not that a document that looks very similar in mortgage versus a bank stated mortgage and bank statement in healthcare have different meanings. You're looking at different things. So you have something called a knowledge graph that maintains the knowledge of a person working in that field. And then we have those created for different fields and within those fields, different applications and use cases. So that's unique and that's powerful. >> That provides the ability to prior to hierarchy for our customers, so they can trace a document back to the original box that was given to us some many years ago. >> You got that providence and that lineage, I know you're not go to market guys, but conceptually, how do you price? Is it that, it's SaaS? Is it licensed? Is it term? Is it is a consumption based, based on how much I ingest? >> We have varying different pricing models. So we first off we're in six major markets from EU, Latin America, North America and others that we serve. So within those markets, we offer different capabilities. We have an essentials offering on AWS that we've launched in the last two weeks that allows you to be able to bring in base content. And that has a per object pricing. And then from there, we go into our standard edition that has ability to bring in additional workflows and have some custom pricing. And then we have what we call the enterprise. And for enterprise, we look at the customer's problem. We look at custom AI and ML models who might be developing and the solution that we're having to build for them and we provide a custom price and capability for what they need. >> And then the nativists this week announced a new glacier tier. So you guys are all over that. That's where you use it, right? The cheapest and the deepest, right? >> Yeah, one of the major things that AWS provides us as well is the compliance capabilities for our customers. So our customers really require us to have highly secure, highly trusted environments in the cloud. And then the ability to do that with data sovereignty is really important. And so we're able to meet that with AWS as well. >> What do you do in situations where AWS might not have a region? Do you have to find your own data center to do that stuff or? >> Well, so data privacy laws can be really complex. When you work with the customer, we can often find that the nearest data center in their region works, but we also do, we've explored the ability to run cloud capabilities within data centers, within the region that allows us to be able to bridge that. We also do have offerings where we can run on-premise, but obviously our focus here is on the cloud. >> Awesome business. Does Iron Mountain have any competitors? I mean like... >> Yeah. >> You don't have to name them, but I mean, this is awesome business. You've been around for a long time. >> And we found that we have new competitors now that we're in a new business. >> They are trying to disrupt and okay. So you guys are transforming as an incumbent. You're the incumbent disruptor. >> Yes. >> Yes, it's self disruption to some extent, right. Saying, hey, let's broaden our horizon perspective offering value. But I think the key thing is, I want to focus more on the competitive advantage rather than the competitors is that we have the end to end flow, right? From the high volume scanning operations, trucking, the physical world, then up and about into the digital world, right? So you extract it, it's not just PDFs. And then you go into database, machine learnings, unstructured to structured extraction. And then about that value added models. It's not just about classification. Well, now that you have classified and you have all this documents and you have all this data, what can you glean from it? What can you learn about your customers, the customers, customers, and provide them better services. So we are adding value all throughout this chain. And think we are the only ones that can do that full stack. >> That's the real competitive advantage. Guys, really super exciting. Congratulations on getting there. I know it's been a lot of hard work and engineering and way to go. >> Thank you. >> It's fun. >> Dave: It's good, suppose to have you back. >> Thanks. >> All right and thank you for watching. This is Dave Vellante for theCUBE, the leader in live tech coverage. (upbeat music)

>> Hey, welcome back, everybody. Jefe Rick here with the Cube. We're in this war subscribed conference 2017 downtown San Francisco. 1,000 2 1,000 people talking about the subscription economy, I think is like the sixth year they've been doing this show. First time we've been here. We're excited to be here, but we're joined by a company that we spend a lot of time talking about. I ot in the industrial Internet, and that's G, but a new gas guidance bar. Ducasse. He's the head of predicts product management for Jean Digital. Welcome. >> Thanks, Jeff. Thanks for having me here. >> So you guys I mean, we were there in 2013 when Beth and Bill lost the industrial Internet initiative at the juiciest museum just across the street. So you guys have been in this space for a while. The G predicts cloud industrial cloud. You guys have been doing a lot of stuff there, so give us kind of an update. Where are you? Obviously picked a highlight. One of the key stories here. People don't think of G as necessarily a subscription economy type of play, but that >> so why are we here >> yet? So why are you here? >> We're here because we are subscription economy. I mean, what we're really focusing on with predicts is building a platform that allows third parties and first party applications to be built around the industrial space. And so a lot of what we're hearing from our customers is that they want to subscribe to those services, right. They want to subscribe to either the production of the services, but more importantly, maybe the different elements that bring the other solution. So the thing about the hospital like a digital twin, a virtual representation with physical asset A lot of times when people want to do is they want to build twins specific to specific asset. But they want to bring together the analytics and the data associated with that. Maybe some environmental factors that they subscribe to from 1/3 party, right, bring those all together doing analysis, right? And then basically give that stuff back. So they want to subscribe to things like analytics they want subscribe to data and the imports. So that's why we're here. We've been using Zamora a CZ part of our subscription service since the we kicked off G predicts last year way Wendy and February, and it's it's going to be a very flexible solution for us. >> So the parts and I don't think it's enough talk, and it really wasn't a lot of talking. The keynote is how a subscription relationship changes the way that you engage with a customer, because if you just sell him something, thank here's the transaction. You know, go off, you run your jet engine, go run your turbine. But if you have a subscription and it's an ongoing value delivery to pay for that ongoing money that they're giving you, it's a much kind of deeper relationship in kind of a single transact, well, >> it can develop. Do you have much deeper relationship? I think the thing that allows you to do is that allows you to experiment a little bit. Try a couple things, figure out what works best for you as a customer, right, and then invest in those areas. You don't have to make a big purchase order, right? Right. You don't have to go off and spend a lot of money on a bunch of software that may eventually go away, right? You can. You can. You can almost try before you buy or try as you buy, right? Probably better way of putting it right. And so what we're saying is you give people the ability to experiment. I think, you know, we talk within G about productivity, right? And the impact we could make him our own productivity to meet predicts, is much about innovation, right? Right. It's giving people the ability to try different things. Teo, Try on DH. See what happens when you bring in environmental factors. Right? Or usage data, right or operational data? Or, you know, we talked about jet engines lot looking at the different pilots. How do they operate the engines? Right, you know. So there's there's there's all these scenarios you can sort of experiment with on a subscription model. Find out what works and then go deep is necessary. >> It's interesting. 10. And the Kino talked about. What's different now is that you can buy. You can upgrade, you can cancel, you can downgrade. So again, this this interaction, as you just described, allows for a bunch of different types of engagement, not just the Big Bang and the other thing that's consistent with we hear over and over. I is a democratization, democratization of the data, democratization of the tools so that somebody does have a hypothesis that, you know, we've been looking at Obviously, a plane operating in the southwest United States is goingto have different characteristics. Is one operating in Alaska. But as you just said, maybe we should look att, pilot characteristics. Maybe we should look at, you know, back in. So when you open up that innovation platform now, you have so many more people coming up with hypothesis, testing hypothesis and you you open up the resource into your company to do so much better >> Well, and you have little innovation like so we have a partner based on Israel Plantain who's doing some stuff in the manufacturing space with G is we start thing about additive manufacturing. You want something about the composites and the materials that actually go into the engine, right, and sort of how of those and held up over time so you could build a much more longitudinal view of that. And again, it could be a subscription service where you start experimenting, you start understanding, especially with additive being sort of ah, mechanism to decentralize a lot of the manufacturing. You don't need to make a huge investment too. Doing that at those analytics. You put some software alongside the additive systems, and you've got the ability to innovate and understand better. Like what? Composites work better. I mean, you talk about the operation of the engine. But how about the manufacturing, the gym? Are there optimal environments right where you want to build those engines? And I think we've done great work. Is an industrial company to understand how to optimize systems and probably even like what the environmental factors are to build an engine effectively. But when you start distributing that, you really want to gauge that real time to understand what the impact would be, >> All right, So we were on short time leash here, actually, but I want to give you the last word. Give a plug for the critics. Transform show Coming up is part of minds of machines. We live for the first year. Last year that was 2,000 developers. Right? Ready? Great turnout for for really a development platform for an industrial Internet cloud. >> Yeah, s. So what we've done this year was a rain together transform, which is the event for our developer community with minds of machines, which is more targeted towards the business leaders were some of the leaders in the organization, and bringing them all under one roof will be here in San Francisco mid October. I don't have the exact dates, but I probably should. But it's like a roundabout on the >> Internet looking over there. >> But we're bringing those together, right? So we can have a dialogue that spans the complete spectrum. Yeah, right. It's the people that are building will have hackathons will have places where people can actually work on that will judge those those different solutions that are being hacked together on. Then we'll be presenting sort of the business value and the impact we're seeing with a lot of the industrial customers again. Many of them are, jeez, existing customers. We've got customers in different, you know, the auto industry elevator escalator industry, you know, fixtures manufacturing spaces that we haven't traditionally played. So we'll be talking about all the benefits were bringing this customer's Blessem new product introduction talk about >> All right, great event. I team meets ot. We went last year. Jeff was there. Beth was there, Bill was there all that? All the players that great show. Well, congratulations on your successful Zorro. And we look forward to seeing your minds machine. Okay, Thanks. Alright, He's Geeta Som Jeffrey, you're watching the cube crumbs or subscribe 2017. We'll be right back after this short break. Thanks for watching.