>>LA Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and don't along with its ecosystem partners. >>Hey, welcome back to the cubes coverage of AWS reinvent 19 from Las Vegas. This is our third day of covering the event. Lots of conversations, two cube sets, as John would say, a Canon of cube content. Lisa Martin here with my esteemed colleague Justin. Um, and Justin, I have a couple of guests joining us. We've got to my left jus bhandarkar had a product for FreshWorks inc and Broughton Sahar VP and GM of machine learning services from Amazon. Gentlemen, welcome to the cube. You still have voices, which is very impressive after three days. A lot of practice it does or hiding out in quiet areas. Right? So Tay, Jess FreshWorks inc I souping on the website. Justin and I were talking before we went live, you guys have 150,000 of businesses using your technologies. I hadn't heard of FreshWorks, but it looks like it's about customer relationship management and customer experience. Tell our audience a little bit about what FreshWorks is and the technologies that you deliver. >>Okay. So we were founded in, uh, back in 2010. We were born in the cloud in the AWS cloud. Uh, and we started off as a, uh, customer support, uh, application. And we have grown on to now deliver a suite of customer engagement applications that include marketing and automation capabilities, CRM, uh, customer support and customer success. And so what we really are looking after is, is to deliver a value across the entire customer journey. Uh, you know, >>so there's been some big legacy CRMs around for a long time. What was the market opportunity back in 2010? The FreshWorks folks saw there's a gap here. We need to fill it. >>Yeah. We, well, we, uh, like any other startup, we decided to focus in one place and our focus, uh, was really around SMBs. We felt like SMBs were underserved and, uh, we felt like as rich as the technology is and the experiences have become, uh, we felt like we needed to democratize access to that. And because SMBs tended to have fewer resources and maybe, uh, in some cases weren't as tech savvy. Uh, we felt like they were kind of getting left behind. And so we wanted to step in there and make them whole and kind of offer them the same set of richness that you would expect, you know, for a large enterprise customer to have. And for that actually working in conjunction with AWS has been super important for us cause we have really been able to deliver on that promise. >>Maybe you can tell us a bit about the relationship between yourselves and FreshWorks. I believe the fresh works is built completely on AWS and always has been. >>Yeah. So how did that relationship begin and how has that grown as, as FreshWorks has grown into, into this massive company that you've become? >>Yeah, so, um, FreshWorks got off on AWS and then when we launched Sage maker and as you know, we have 700 tens of thousands of customers today doing the machine learning on AWS and on Sage maker. What customers have seen is that they get significant benefits in terms of features and developer productivity. And lower cost of ownership and FreshWorks saw that they could reduce that time to getting the models out by an order of magnitude. And their house was saying for example, that they used to take couple of days to get the models out to production. And by using Sage maker they were able to get it down to a couple of hours. And we have seen this happen with many other customers into it. For example, got down from six months to about a week. And just because of the productivity, performance and cost benefits that Sage makeup provides, you have seen the house FreshWorks and then many other companies, many of the customers more to AWS for the machine learning. >>Are they what are you using this machine learning to do? So you have all of these different models and we were talking a little bit before we went live about how you, how you use different models for different customers. But what are those models actually used to do? What service do they provide? >>Okay. So as you know, we have a set of these applications which are built around functional use cases. And so if you take a given customer, they might have multiple products from us and they might be doing multiple different use cases on us. And so you can quickly think of this as being, you know, maybe three to five specific use cases that require, you know, machine learning, you know, assistance. Uh, and so as a result, as we scale this up to the our entire, uh, set of customers, we now literally have thousands and thousands of these ML models that we have built, addressed, uh, geared to, uh, addressing specific pain points of that particular customer. Right? So it's all about catering the ML model for a specific use in a specific context. And then it's not only just about building it, uh, which, you know, obviously Sage Merker does a great job of helping us do that, but it's also about maintaining it over time and making sure that it stays relevant and fresh and so on. >>And again, working with AWS has been instrumental in for us to kind of stay ahead of that curve and make sure that we're continuing to drive accuracy and scale and simplicity into, uh, into, you know, into those particular use cases for customers. Then, you know, we released many features this year that makes this important. So one of the things that we have as part of Sage maker studio is a Sage make a model monitor that automatically monitors predictions and allows a customer to say, when are those predictions not being of the appropriate quality? And then we can send an allowance. So we are really building Sage maker out as a machine learning platform that they get all of the undifferentiated heavy lifting so that customers can really focus on what they need to do to build a model, train the model, and deploy the model. >>So in terms of your users, you mentioned too just the, the, the gap in the market back in 2010 was the small, the SMB space that probably something like a Salesforce or an Oracle was possibly too complex for an SMB. But now we're talking about emerging technologies, machine learning, AI. What is the appetite for the smaller, are you dealing with, I guess my question is a lot of SMBs that are born in the cloud companies, so smaller and more agile and more willing to understand and embrace technology versus legacy SMBs that might be, I don't want to say technology averse but not born within it. >>Yeah. So, so we, uh, we run through the entire gamut. So we obviously have, uh, you know, Silicon Valley based startups. We have more traditional companies around travel and hospitality and real estate and other, other verticals. Uh, and what we have really, really seen the commonality has been is that, uh, as good as the technology has become for AI and ML, uh, there is still some disparity in how people are able to consume it. Right. And if you have a lot of resources, a lot of skilled engineers, it is very easy for you to do that, thanks to all of the capabilities that are delivered by AWS. But in the other cases, uh, they do require more handholding specifically for those use cases that really impact them. Like how do I reduce my churn amongst customers? How do I maximize the chances of closing a deal? How do I make sure that the marketing campaign I run delivers on all of the, the objectives that I have? Right? So all of those things they re they need help. And so we are in there to kind of simplify that for them and leveraging all of the underlying technologies from AWS. We're able to deliver that together >>and going in from the beginning all in on AWS when AWS was only about four years old or so, right? Back in 2010. Um, talk to me about the opportunities that that is opened up for FreshWorks to evolve, you know, offer a suite of different solutions. Talk to us about Amazon and AWS is evolution and how quickly that they're evolving and developing new products and services as like fuel for FreshWorks business. >>Yeah. So really the big focus that we have always had is to deliver the right experiences that really impact end users. For those particular functional use cases around marketing, sales support and customer success, right? So as part of that, while we are focusing on on that experience, we also need to be focusing on delivering all of these services at scale, right? And with all the right security built in and all the right, uh, other, you know, tool set that that's built in. And so, so the synergy that we have found with between us and AWS is that we're able to rely on all of the right things for AWS to deliver upon. So they are also all about offering simple API APIs about making things scalable right from the get go about being extremely cost effective about uh, continuing to drive innovation. And these are all the things that drive us as well for our customers. And so it's been a very complimentary partnership from that respect is, you know, we kind of like go on this journey together and in our customer obsession is a key leadership principle. And so everything we do at AWS is really working back from the customer and making sure that we are really addressing all of the pain points. And making them successful? >>Well, because customer experience is a D it can be a deal breaker for companies, right? You think of you have a problem with your ISP and you call in or you go through social media or um, a chat bot and you can't get that problem resolved. As a consumer, you have so much choice to go to another vendor who might be able to better meet your needs or have the use the data to make sure they already know what's the problem. It's the same thing in the CRM space, right? If businesses don't have the right technologies to use the data to really know their customers, this customer's churn. And so it's really, we see CX as a driving force in any industry that if you can't get that right, customers are going to go, I'm going to go somewhere else because I have that choice. >>Yes. I mean customer expectations that you said have risen customer inpatients with bad experiences gone down. And one of the things that we have really focused on is as we go through this entire journey, we collect the data of that customer's journey. And we learn from it and we're able to visualize that for the sales person or the tech support person who's actually working with that customer. So they can actually see the journey of that customer. They visited the pricing page a couple of times, maybe they're interested to make a purchase or they visited the cancellation policy page. Okay, maybe I need to do something about that. Right. And so that is really been instrumental kind of in success success. And you know, what we are doing at AWS and Sage maker is making sure that all our customers get access to this technology. And that is where we start with how do we make machine learning accessible to all developers so that all of the experiences that we have gained at Amazon from investing in machine learning for the last 20 years, we take all of those learnings and make it available to our customers so they can apply machine learning for transforming their businesses. >>Yup. >>And that's exactly what it can be as transformational. Well gentlemen, thank you very much for joining Justin and me on the program talking to us about FreshWorks. What you guys are doing with Amazon and the opportunity to really dial up that CX experience with machine learning. We appreciate your time. >>Thank you. Thank you very much. >>All right. For my car is Justin Warren. I'm Lisa Martin and your Archie, the cube from AWS. Reinvent 19 from Vegas. Thanks.