Tag: AI

Advice From 5 IA40 Leaders

We polled the founders of five companies from our inaugural Intelligent Application 40 list to bring together some of the best advice they have from hard lessons they’ve learned throughout their journey or the best advice they received during that time.

When setting out to found a company, advice is never hard to come by. Mentors, advisers, professors, investors, Meetup groups, entrepreneur networks – there is no shortage of places to go to seek advice. The problem is – that advice can be as varied as the types of companies one can found.

We polled the founders of five companies from our inaugural IA40 list to bring together some of the best advice they have from hard lessons they’ve learned throughout their journey or the best advice they received during that time.

Cristobal Valenzuela, CEO of RunwayML, which is one of the Intelligent Application 40 winners
Listen to our podcast with Cristobal here.

Cristobal Valenzuela is the Co-founder and CEO of Runway, which offers web-based video editing tools that utilize machine learning to automate what used to take video editors hours if not days to accomplish. He says he learned a lot when he was just starting out — spinning his company out of this thesis project at NYU. Still, he says the most important thing to always keep in mind is your rate of learning — entrepreneurs should never stop learning.

“How fast you are learning as a company, as a team and as a product, how fast you are learning about your customers, how fast you are learning about the industry, about the competition, about the market, about technology. That rate of learning and how fast you can do something you’ve never done before, experiment, learn as much as possible, and then adapt is really, really, really, really important. It’s easy to get stuck and not be able to adapt. So, just have that mentality that you’re always learning. And then everything else will come.”

Justin Borgman, CEO of Starburst Data, which is one of the Intelligent Application 40 winners
Listen to Justin’s IA40 podcast here.

Justin Borgman is the Co-founder and CEO of Starburst, which provides a query and analytics engine to unlock the value of distributed data. Justin says the advice he gives any entrepreneur at any stage in their journey, but particularly to those just starting out, is to look inside themselves and consider whether they have the perseverance required because that is the single most important attribute to being an entrepreneur.

“You have to have a high pain threshold and a willingness to push through that pain because it is not for the faint of heart. It is not easy. I think some people are just built for that. They have the stubbornness, the drive to push through that when others get overwhelmed by it and bogged down.

One piece of advice I will share that I heard myself — I actually asked a now public company CEO founder, ‘Does this ever get easier?’ Because as you’re building, you always think, ‘Okay, at some point, it’s just going to get easy, right? Like I’m going to be relaxing on the beach, this thing’s going to run itself.’ And he said, ‘No, it’s just different kinds of hard.’ And that stuck with me because particularly as you scale, every new chapter has been a new challenge and in a totally different way. That’s part of what’s amazing about startups, I think, just from a personal growth perspective. You are always having to improve yourself and scale to the next level. And so that really stuck with me. It never gets easier, just different kinds of hard.”

Anoop Gupta, CEO of SeekOut, which is one of the Intelligent Application 40 winners
Listen to Anoop’s podcast here.

Anoop Gupta is Co-founder and CEO of SeekOut, which provides the Talent platform companies use to find, hire, grow, and retain talent. Anoop spent much of his career at Microsoft, but as he’s transitioned into the world of entrepreneurship and helping others evolve in their own careers, he said he’s started to better understand the importance of setting a company culture – and how it needs to be foundational for any entrepreneur.

“Throughout my career, I have worked with incredible people and was lucky enough to be at a place with a culture that really invested in people. In a larger organization, you kind of take culture for granted — in the sense that it is already baked in. In starting SeekOut, my appreciation and conviction that people and culture are paramount has grown. Having the right people and creating a culture of gratitude, humility, and empathy is foundational to success. My advice for others starting their own companies is to be proactive about defining your culture and to stay true to that culture as you grow.”

Clem Delangue, CEO of Hugging Face, which is one of the Intelligent Application 40 winners
Listen to our podcast with Clem here.

Clem Delangue is Co-founder and CEO of Hugging Face, an AI community and platform for ML models and datasets, which just landed $100 million in financing this year. He thinks the beauty of entrepreneurship is owning one’s own uniqueness and building a company that plays to each entrepreneur’s individual strengths. He shared his biggest learning during his early days was to always take things one step at a time.

“You don’t really know what’s going to happen in three years or five years. So just deal with the now. Take time to enjoy your journey and enjoy where you are now because when you look back at the first few years, at the time you may have felt like you were struggling, but at the end of the day, it was fun. Also, trust yourself as a founder. You’ll get millions of pieces of advice, usually conflicting. For me, it’s been good to learn to trust myself, to go with my gut and usually it pays off.”

Luis Ceze, CEO of OctoML, which is one of the Intelligent Application 40 winners
Listen to our podcast with Luis here.

Luis Ceze is the Co-founder and CEO of OctoML, an ML model deployment platform that automatically optimizes and deploys models into production on any cloud or edge hardware. Luis is an entrepreneur and tenured professor at the University of Washington. He said as a professor, you can have impact by writing papers that people read and then do something as a result. And you can directly impact your students – what they go on to learn, research – maybe even become a professor themselves. But getting into company building – where you actually put a product into the hands of a consumer has been a new and exciting experience for him. One of the most important lessons he’s learned, he said, has been to surround himself with people that he genuinely likes to work with because it creates a more supportive, trusting environment.

“People who are supported, they can count on people around them and feel like there is a very trusting relationship with the folks that you work closely with. I have no worries about showing weaknesses and always having to be right. I think it’s great when you say, ‘You know what, I was wrong, I’m going to fix it.’ It’s much better to admit when you’re wrong and fix it quickly than trying to insist on being right.”

SeekOut CEO Anoop Gupta and VP of People Jenny Armstrong-Owen on AI-powered talent solutions, developing talent, and maintaining culture

SeekOut CEO Anoop Gupta and VP of People Jenny Armstrong-Owen

This week on Founded and Funded, we spotlight our next IA40 winner – SeekOut. Investor Ishani Ummat talks to SeekOut Co-founder and CEO Anoop Gupta and VP of People Jenny Armstrong-Owen about their AI-powered intelligence platform, the importance of not only finding and recruiting new hires but also developing and retaining employees within a company, and maintaining SeekOut’s own culture while seeing significant growth over the last year.

This transcript was automatically generated and edited for clarity.

Soma: Welcome to Founded and Funded. I’m Soma, Managing Director at Madrona Venture Group. And this week we are spotlighting one of our 2021 IA40 winners – SeekOut. Madrona Investor Ishani Ummat talks with CEO and Co-founder Anoop Gupta and their Head of People, Jenny Armstrong-Owen. SeekOut is one of our portfolio companies, and so we were very honored that our panel of more than 50 judges selected them for our inaugural group of IA40 winners. SeekOut provides an AI- powered talent 360 platform to source, hire, develop, and retain talent while focusing on diversity, technical expertise and other hard-to-find skillsets.

We led SeekOut’s Series A round of financing, and have worked with the team closely since before then as they fine tuned their initial product offering. The company has had massive success. And earlier this year they secured $115 million Series C round to scale their go to market and to build out their product roadmap, including powering solutions for internal talent, mobility, employee retention and the like- all topics that are Anoop and Jenny will dive into with Ishani today. With that, let me hand it over to Ishani.

Ishani: Hi, everyone. I’m delighted to be here with a Anoop Gupta, the CEO of SeekOut, and Jenny Armstrong-Owen, SeekOut’s head of people. SeekOut is building an AI powered talent 360 platform for enterprise talent optimization and was selected as a top 40 intelligent application. We define intelligent applications as the next generation of applications that harness the power of machine intelligence to create a continuously improving experience for the end user and solve a business problem better than ever before. I’m so excited to dive in today with Anoop and Jenny, thank you both so much for being here.

Anoop: Hey, Ishani, it’s wonderful to be here. Thank you for making time for us.

Jenny: Agreed. Thank you so much. It’s great to be here.

Ishani: So, I’d love to start out by going way back. Anoop, you were a professor of computer science for over 10 years, co-founded the virtual classroom project that quickly got acquired by Microsoft. In 2015, you left Microsoft to start the precursor to SeekOut. Tell us about what led you to the core talent problem that SeekOut is solving today.

Anoop: So, Ishani, when we left Microsoft, we left because you know, Microsoft was just an absolutely fantastic place to innovate, but what Microsoft legitimately wants you to do is to get on an 18-Wheeler and discover some big island, and we wanted to be on a mountain bike exploring opportunities because it’s such an exciting world out there. Given my background of running Skype and Exchange, actually the first thing we settled on, was Nextio, which was a messaging application. And the whole notion was that today people hide their email address and phone number because once you give it out, people can spam them. And we were not being so successful there, so we built an application called Career Insights. What Career Insights was about is you analyze all resumes in the world, and if you do that, then we can say, “Hey, if you are a UI designer at Microsoft, what are the next possibilities? Where are your peers going? And if they were going to Facebook, we could tell you where are the Facebook UI designers leaving for and doing next. So, it became Career Pathways inside that. And we said, “Oh, this is so useful for recruiters and talent people” that we pivoted there, and since then, our passion, our understanding of what is missing and what could be done better has led to our growth of SeekOut and talent acquisition and what we bring to the table.

Ishani: That’s so great. You sort of found your way to the recruiting market, to the recruiter as an end customer, but beginning with this problem of career pathing and pathways. It’s only something that’s amplified over the course of the last decade, let’s call it and it seems sort of prescient, but now that we look at this moment in time that seems like a very acute foresight.

Jenny I’d love your perspective. This talent environment has evolved so much in the last few years in ways that even Anoop and SeekOut could not have predicted with the pandemic and everything like that. We all see and feel the Great Resignation, the ongoing talent war in the tech world. You’ve been in talent teams for 20 years — what elements of this were predictable and what has taken you by surprise?

Jenny: Well, definitely what is very predictable is that the tech world continues to explode and grow. I read a statistic in the New York Times that the tech unemployment is 1.7%, which is basically negative unemployment. So, that’s not a surprise. What was not predictable was COVID, was the ability for folks to literally work from their homes. And it released the boundaries around what was possible for folks. And I think that’s one of the biggest challenges for organizations. And if you didn’t snap and adapt to that, you were not going to be able to meet your hiring goals.

One of the things that I love about being here at SeekOut, is going and finding people wherever they are. And so for us, we’re not restricted to Bellevue, Washington, or Seattle, Washington, and I think that’s one of the things, especially about our tool, that is so incredibly powerful. If you’re an organization that can embrace remote, that can actually make you so much better than restricting yourself geographically. That’s one of the things that I think has been a huge benefit for us. I think we’re embracing a new paradigm of relationships with employees, and it’s going to be a much more virtual relationship at times than it is a physical one.

Anoop: One of the things when we got into this, is we said, “Hey, digital talent, technology talent, is really important,” and what COVID did was, Satya said “Two years of transformation in two months,” right? So the accelerating rate of digital transformation, something we were focusing on, wasn’t there and that really increased the value of what we’re doing. The second thing that’s happened over the last two years is the emphasis on diversity. A lot of young people are saying, “I don’t want to join a company if I don’t see that they are embracing diversity, inclusion, and belonging in a genuine, authentic way.” We believe a lot of talent exists. It begins with how do you hire, how do you understand what exists in talent pools, and then being able to find them. The problem that leaders have — business leaders, talent leaders — is, they have good intentions, but translating those great intentions into concrete actions and results has been hard, and SeekOut really facilitates that.

Ishani: It’s such a good point on the market, evolving in some ways that you are able to control and some ways you can react really responsibly and control around. In other ways, that they are so out of your control where you sometimes tools can help you with that, tools like SeekOut, and sometimes you have to build that internally. It’s a culture thing. It’s an intangible. But let’s talk a little bit about the tool you’ve actually built. The way I think of SeekOut is it’s a product that’s evolved a lot from a talent acquisition tool to really a more 360 degree talent intelligence platform. But it didn’t start that way. Walk us through this journey from a talent acquisition tool to really an intelligence platform.

Anoop: My Ph.D. thesis was on AI and systems. My co-founder Aravind came from building the Bing search engine. When you look at all of these areas, AI is just a core part of it. So, to use an analogy — when you go to Google and do a flight search — UA 236. It understands that you are doing a flight search that UA is United Airlines, and you’re probably looking for arrival or departure times and therefore this is the relevant information. So, in a similar vein, SeekOut is a people search engine. So, we need to understand a lot about people. So, when I search for Anoop Gupta, our search engine realizes that Anoop is a first name and Gupta is a last name — and that it is a common name in India, right. So, we can get a lot of information that helps us. Similarly, normalizing for universities and companies is really important. SeekOut is very special in that it brings data from many, many different sources and combines it together. So, as we want it to go to technical folks and technical talent, and I’m just using that as an example, and you get GitHub, you see the profile on the GitHub, how does it match to the profile, you know, they might have LinkedIn and they are the same person. You know, it takes AI to figure that out. Then you want to look at all the code and information that you find, and you say, what is their coder score? How good a coder are they? Do they know Python? Do they know C++? So, we started bringing those things inside of it and all of those are inferred things. When we do security clearance, as an example, people don’t mention security clearance often, so what we go and look at is we look at job descriptions for the last many years, and we say did the job description say “This role requires security clearance and top secret or whatever?” And then we say, if there are enough of these positions where that is required at that company, at that location — then we say, you likely have security clearance. So, AI is fundamentally baked into the product, but we also take an approach that while AI is everywhere, it is designed as a complement to the human and not as a substitute to the human recruiter or sourcer that is there. That is an important principle for ourselves. The human is doing what they are best at, and all of the AI and logic are doing what they are good at to facilitate the human being more successful.

Ishani: We talk a lot about intelligent applications having a data strategy. And in order to augment workflows and make them solve a business problem really better than ever before. All of what you described is so well steeped in that philosophy around pulling in data from a host of public sources and then being able to really drive a better product around that and surface insights that matter. Customers love as one of the core features of SeekOut, the search functionality. So I’m sitting on top of all that data, the search just works. Can you talk a little bit about how you handle and process all of this data to just make it work like magic for a consumer?

Anoop: So one is, you’re very right. It’s actually a very hard problem when you have 800 million profiles and data coming from lots of sources, and the data is not static data — people are changing jobs, people are changing things. It’s all dynamic data, so, how one makes it work, how one makes it very performant? You know, my co-founder again — one of the movers and shakers behind the Bing search engine, and because we come from that background, Googles and Bings have to handle very large amounts of data, so how do you construct the index structures? How do you do the entity formation combined together? So that is core to what we do. And then on top of all of that big data, when you say can you clone Jenny and find us similar features? Now that is an impossible task. Because people may do the job with her humor, and her other parts are so hard to replicate, and the nice person that she is, then you have to do all of the matching, right? Or when you parse a PDF resume, how do you extract the skills or when you parse a PDF job description, how do you parse the requirements and what are the must-have requirements? What are the nice-to-have requirements? So, there’s just infinite amounts of problems, and we keep tackling them one at a time.

Ishani: It seems like you also, though, have to be so semantically aware of the context, right? That’s exactly what you’re talking about with the job description. How do you parse out requirements versus any of the other components? And how do you parse out whether someone might have met those requirements? So much is evolving in this field of semantic awareness, semantic search, and natural language processing. What are the kinds of underlying models that you use? Have they really evolved in the last few years as we see some of the transformer models or CNNs start to make a step-change in technology?

Anoop: Our models are continuously evolving based on what the users are doing, how they’re using it, and what their needs are. We do a lot of building ourselves, but we also leverage third parties. We also, you know, we have a notion of a power filter or something. So, if you think and look at synonyms, right? So, you say people who know JavaScript, they are a short distance away from TypeScript, right? Or people who know machine learning, there’s so many different kinds of words that people use in GitHub, whether it’s Keras or TensorFlow, PYTorch, whatever kinds of things, how do you find the equivalencies? You can find some things through correlations or other algorithms. What makes sense, what does not make sense. So, Ishani, there’s just a lot of different things that we are continuously doing. There are different kinds of algorithms and networks that get used for different types of natural language parsing and what we do. But I’ve always said from when we were at Microsoft, eventually, it is the data that you have because everybody publishes their algorithm and if you have the right data, you can do so much more. It is the data, and then the intelligence on the top that I think is really important. You got to have the right data. And then, of course, the right people and the algorithms to get to that intelligence.

Ishani: So, it really goes back to this concept of having a data strategy early. Being able to be nimble in evolving underlying technology and application intelligence. We always talk about garbage in, garbage out. So, being able to really understand where your data’s coming from, semantically parse and structure it to then be able to give to your end user as we call it magic.

Anoop: Yes. Yes. The problem with data is data is not clean. So, you know how you can efficiently clean up that data and use ML models to say these are extreme, exceptions and what to look at become super important.

Ishani: So let’s zoom out a bit. We’ve talked about this briefly, but over the last two and a half years or so, work has changed so much. Hiring has become hard. Engaging with employees has never been more important than it is today. Retention is hard, and SeekOut is doing really well in part because of that macro tailwind. From a company growth perspective, how did you recognize and take advantage of that moment in time?

Anoop: Helping companies get a competitive advantage, recruiting hard-to-find and diverse talent was a model for us from the very beginning. Then all these things happened and we’ve grown 30X in revenue over the last three years, our valuation is 50X where it was from three years ago and we have very high net retention and amazing customers. But we hadn’t thought of everything. We were focused on talent acquisition. That is how do we bring external people? Then with COVID, and the great reshuffle, the great resignation, many companies like Peloton stopped hiring externally and we said, what are the opportunities we can create for the people that are inside? So, our more recent focus on retention is really big. So, here’s the big story that we talk about. It is truly about the future of enterprise. We believe winning companies are realizing that the growth of people and the organization are inextricably linked. So, our mission has broadened, and it’s become to help great companies and their people dream bigger, perform better, and grow together. So that’s the mission and it’s a fundamental mission for every CEO and business leader and not just the HR leader. Then what we are doing is, you know, use technology to ensure that companies and talent are aligned and empowered and growing together. Or in another way what we’re saying is, “Hey, we going to help organizations thrive by helping them hire, retain, and develop great and diverse talent.”

Ishani: You know, SeekOut was really the right place at the right time to take advantage of, and actually really help people through that transition. But you have to be experiencing this internally as well? You talked about 30 X in terms of growth, but you also have triple headcount in the last year. I think you anticipate doing it again this year. How do you maintain, and Jenny, this is a question for you, culture and such a high growth environment?

Jenny: It’s one of my favorite questions I get it a lot in interviews. Culture has become probably the most important thing in a world where people are free agents, and they want to work at a place that aligns with their values and the way that they want to grow and develop with a company. So, I will share this. For me, I was looking at a number of different companies, and I met Anoop, and our first conversation, Anoop, I don’t know if you remember this, it was supposed to go for an hour. We went over 90 minutes, and in that moment, I knew that this was different. This was a different place. The culture here really does emanate from Anoop, Aravind, John and Vikas — the folks that started this company. From my perspective, our job is to make sure we don’t have cultural drift because we don’t have to fix our culture. Our culture is phenomenal. Candidates across the board tell us they’ve never had a candidate experience like this before. Everybody they meet with is super kind and helpful and collaborative. So for us, it’s really keeping our eye on these cultural anchors and making sure that we’re staying true to those.

So, in the hiring process, making sure that every single person who comes here, there’s a diversity interview where we talk about what is important to you in terms of diversity, belonging, equity, and inclusion. To Anoop’s point, people want to go where they feel like they’re going to belong. And then diversity can thrive, and equity can thrive, but you have to have that sense of belonging first. So for us, it’s very much staying focused on that. And everything that we do is around driving programs and opportunities and conversations that reinforce that. We start every Friday All Hands — in fact, I will admit, I suggested to Anoop early on that this was not going to scale as we grow. We’re 150 people today. But we start every all hands with 15 minutes of gratitude. I admit that it is absolutely scalable, and we’re going to continue to do it because it is by far the most favorite meeting of the entire week. That moment that we set aside to say nothing is more important for us in this moment than sharing our gratitude with each other. So I think that’s, for us, I feel super fortunate to be able to be at this intersection at a time where, it is tough, right? Companies are struggling to keep their culture intact in a world in which everything’s shifting so quickly.

Ishani: That’s such a good point that begins in the interview process and it continues in the onboarding process. Then it’s an everyday commitment to reinforcing your culture. I think people do have really good elements of each of those. But it’s rare that you find somebody so committed to all of them.

Jenny: It starts with Anoop.

Anoop: So, you know, so Jenny said it so well it comes from just a deep belief that people are the most foundational element to our success. We truly, believed that for ourselves. I’ll give you an example in a story. So we were looking for, I think the CRO, we had an executive search firm, and they said, ” Anoop you seem to be open to meeting a lot of people. Are you sure you have enough time?” And I said, ” I’m always there when it’s a people question. People are so important.” We have four OKRs now, these are the company goals. Our main goal is our people, culture, execution are our competitive advantage. I truly believe in that. It is not our AI knowledge. It is not we are smarter. It is that as a company, who we bring in, how we think, how we execute, how we collaborate, how we decide to disagree, yet, find commitment, you know, hold each other accountable, be nice.

We want to be the ones to show that nice people can win. Kind people, people with empathy can win. You don’t have to be a jerk to get ahead. So that is just a fundamental belief for us. And that has helped with our retention. That’s helped with our recruitment. That’s helped with the energy and their whole self that people bring to the company every day. And I think that’s a huge part of our success.

Ishani: The recruiting example of the CRO is so interesting because it really does delineate there is a real and important place for tools, but there’s certainly a line where that stops. Where you, Anoop, taking the time, you know, it wouldn’t be a little bit facetious as a talent optimization platform, if you didn’t take the time to bring in your own talent and really make sure that they fit the organization’s culture and the ethos, and they want to be where they are. So certainly, it has, there’s good continuity there with SeekOut’s mission and SeekOut’s product and how you operate.

But also, that there’s a role for the talent optimization platform that you use. And that presumably you use SeekOut, at SeekOut.

Anoop: So, you know, the other side story is. Every exec firm that I talk to, they give me some candidates and sometimes they are diverse, sometimes they’re not diverse. I say, well, let me find you some women candidates, let me find you some, you know, black candidates. They exist — you just don’t know; you need a better tool.

Ishani: It’s very much clear that there are roles, and these tools are augmenting how people do their jobs and in ways that haven’t ever happened before. But that it is an augmentation with learning, with intelligence, and with automation. But there’s still very clear roles for how do you build, for example, a culture like Jenny, right? And how do you maintain that? It also speaks to one of the product focus areas of SeekOut, which is on retention and really retaining your talent and looking internally. Jenny, talk to us a little bit about some of the strategies that you use, whether or not it’s related to SeekOut’s product, to maintain the talent and retain talent.

Jenny: Yeah. And thanks. I think it’s actually one of the reasons why, when I with Anoop, and he cast the vision for what SeekOut was going to be, was what got me so excited. As someone who’s led people teams now for way too many years to admit, I think getting folks in the door, getting them hired, is absolutely critical and important. I think growing, developing, and evolving as teams with folks who are committed and engaged, that is the job, right? That is every day. All day thinking about the people that we already have here. That’s one of the things about the enterprise talent optimization, where we’re going there, it’s going to revolutionize people teams. I mean, it’s like the best way for me after so many years of not having really effective tools on people teams —you know, we’re building a world in which they are going to be so complementary and it’s going to free people teams and leaders up to do what they do best, which is really about developing people.

So, for example, yeah, we’re 150 people. Well, we’re going to be implementing a people success platform. We’re going to be making sure we’re touching base on the things that matter the most to people, which is all about skill development, acquisition, growth. That’s fundamentally why folks will leave, right? Especially in the tech world, because they want to do different things, or they want to be able to stretch and grow. One of the things that’s awesome about startups is you have infinite ability to grow your people in whatever direction they want to, because the opportunity is here. It’s one of the reasons why I stayed at my first tech company for so long — I was able to do and grow and be so many things, and that’s one of the things that we talk to people about in terms of our value prop when we’re interviewing them is, “Hey, we are interested in you for this, but guess what? The world is your oyster at SeekOut and wherever your passion wants to take you, we are going to support that passion.”

Ishani: What you’re saying around giving people, the opportunity to grow is incredibly aligned with SeekOut, with the mission of the company. But also again, the product. It is also very hard to execute on. To say — we have a high-performing software engineer in our machine learning division who wants to go try out product management. Right? What are the tools that you used at SeekOut, and how do you actually execute on that?

Jenny: Well, I think that we are still in our nascent stages. We started last year at 40 people. We’re now at150 people. What I would say is building the capability in leaders to be aware and to be having these conversations and to be free enough to be able to think beyond the roadmap and the things that are getting done today. So, I think you have to hold both things tightly and loosely at the same time, if that makes any sense. And it requires a high level of change management and org development skills. Like we have to build whole-brained leaders who can look at our people with both things in mind. Executing on the deliverables that we have today, but fundamentally making sure you’re having this other conversation and that you’re driving that consistently in a way so that there’s never any dissonance. I think that’s the challenge? Creating too much space between those conversations or even having those conversations at all creates the dissonance. Then that creates the drag and the drifting. So, for me, that’s one of the things that we talk about a lot is who do we have?

Anoop, I would love for you to give your kind of ETO summary, because I think it is so compelling about the tools that we’re going to be able to provide. To your point, Ishani, I don’t have specific tools today. I mean, I can use my SeekOut tool, which is awesome, but we’re also small enough that we kind of can do a lot of this, you know? One-on-one but Anoop, if I would love for you to add onto that.

Anoop: You know, the cost when a great employee leaves is almost two X their salary for the annual salary, because it takes so much for the new person to come in and get up to speed, and meanwhile, the products are delayed and other things that delay whatever function they might have been going. So that’s why it’s so critical. And that’s why people care about it a lot. One of the things I say is that companies are deluged with data. There’s data flowing out of everything, but when it comes to data about their people, companies don’t understand the data is siloed. The data doesn’t exist. They may not have the external data. They may not have what they did before. And there is missing data. You know, your manager doesn’t know, Hey, in a large company like Microsoft or VMware or Salesforce where are the open jobs. What are the matching jobs? What are the skills? What does it look like? So, the data about employees is missing, the data about opportunities is missing, and then how do you take opportunities and data to match them to people? So, we can tell you about career path, if you’re going from a software development to a product manager, we can point you to people who made a different transition. We might be able to point you to people who made that transition, who might be from the same school, might be from the same gender and you don’t have to talk to the hiring manager, you can talk to people below and say, what is the culture of the team? Basically, we bring amazing data from outside. But then we take data from inside the company —this may come from management hierarchies. This may come from Salesforce. This may come from your developer systems and GitHub — and give you the most comprehensive thing. Then we engage with people. We really have two audiences. One of our audiences is the employee. Okay, who in a private secure way are mapping out their career, their growth, their learning journeys, their growth and development journeys. The second is the HR and the business leaders who are saying, we’ve got to deliver. There’s a strategy we want to do. Do we have the right talent? How does my group compare to competitors? How does it grow across the companies and how do we optimize?

So, we are super excited about it in any conversation that we are having, with CHROs, with other leaders, there’s a lot of excitement about what’s possible what SeekOut can do for them.

Ishani: So, SeekOut today is a really amazing example of an intelligent application for 360 talent optimization, not just the external component, but also internally. This speaks so much to both the environment and you’re reacting and being nimble around, how do you create offerings that people need? Without revealing too much, give us a peek into what the future holds for SeekOut.

Anoop: So future wise, Ishani, each of these broad areas that I’m talking about, there is immense depth in that. As we go deeper into it, there is a lot of work that is involved. So, if you look three to five years just executing on even the components that we have talked about and becoming a star We’re thinking you know, I believe this is a new category. HR don’t even realize what is possible in terms of data, the insights they can have, what they can do for their employees. So, there’s always a market and a mind shift that is involved and people are the slowest to change in some sense. So, I think our journey just making it, and if we do it right, and if we are the leaders, this is more than a hundred billion-dollar company, I believe. Okay. So there’s lots of growth and possibility, in this because talent is central to organizations and their success.

Ishani: Anoop and Jenny, we tend to end these podcasts with a lightning round of questions. So, we’ll go quickly through three questions that we ask every company that comes on this podcast. The first for both of you, aside from your own, what startup or company are you most excited about that is an intelligent application?

Anoop: So, for me, I would say, you know, some company like Gong or basically people who give you intelligence about how your salespeople are doing, how can you be better? What those calls are. Do the natural language analysis and all of that. So, it is just a hot topic, so it could be more, but that’s top of mind for me.

So let me just name that.

Jenny: I have an appreciation for Amperity and what they’ve been up to and what they’ve been doing. So that would be mine.

Ishani: Awesome. Both actually are also intelligent app top 40 companies. So, congratulations to Amperity and Gong. Outside of enabling and applying AI and ML to solve real-world challenges, what do you think will be the greatest source of technological innovation and disruption over the next five years?

Anoop: Certainly, you know, machine learning/AI will have a huge impact. But I think it will also be coupled with that it works on lots of data. We are instrumenting everything, on how the washing machine is being used, how your toaster is being used, how you’re driving. So, I think, the data and the machine learning together. But with the caveat of us making sure that it is not biased. Every tool in humanity can be used for good and it can be used for bad. But I think if we use these things intelligently, we can make a lot of good happen.

Jenny: Yeah, I would have to agree. I can’t say it any better than Anoop did. I think that making sure that technology is being inclusive as well. I think that’s a huge area of focus and concern.

Ishani: I couldn’t agree more. Final question. What is the most important lesson? Likely something you wish you did better, perhaps not, that you’ve learned over your startup journey.

Anoop: I will say, throughout my career, I always kind of knew people were important, and culture was important. You know, people would talk about it. But my appreciation and conviction that it is about people and culture as the fundamentals and foundations to success has been a realization. You know, if you asked me this question five years ago, I would not have answered it this way. You kind of take culture for granted, is not granted in the sense that it is already kind of baked for you in a larger organization. I think here, there was the opportunity to say — you get to define it — then it just made so much sense that this is the thing to focus on.

Jenny: That’s awesome, Anoop. I love that. I would say that for me learning that, you can put people at the top of the pyramid, and you can be very successful, is something that makes me incredibly happy that I’m getting the chance to learn and experience.

Ishani: Anoop and Jenny, it’s been so great to talk to you today about SeekOut, but also about people and how important they are in the organization. SeekOut is a great tool that enables you to find, recruit, and hopefully retain the best people that are going to build your organization. Thank you so much for taking the time and it was a great chat.

Anoop: Thank you so much for having us really appreciate the time.

Thank you for listening to this week’s episode of Founded & Funded. Tune in in a couple of weeks for the next episode with UW’s robotics expert Sidd Srinivasa.

 

Announcing OthersideAI – Making Email Magical Through AI

Natural Language Processing is having a major moment. NLP enables machines to analyze, understand and manipulate language. It is undergoing a rapid and transformational shift, powered by forms of deep learning.

Led largely by a new generation of pre-trained transformer models, innovators are unlocking NLP use-cases and entirely new applications seemingly at the speed of light. For some context – in February 2020, Microsoft announced what was then the largest and most powerful language model: Turing-NLG. Turing is 17 billion parameter language model that outperformed the state of the art in on a variety of language modeling benchmarks. A matter of months later, OpenAI released GPT-3 – a 175 billion parameter generative transformer model: a 10x increase in size.

These models join a growing number in the “billion parameter club”- to sit among the likes of 9.4B from Facebook and Google’s Meena chatbot. But make no mistake – this is not only a big tech game. New companies like HuggingFace and open-source options like DistillBERT are driving drastic performance improvement in transformer models and redefining the standards by which we evaluate them. The progress is nothing short of astonishing.

OpenAI’s GPT-3 lit the entrepreneurial Twitterverse on fire this summer – and with good reason. The third iteration of OpenAI’s autoregressive language model is a step change in deep learning generative language models. In a matter of days, beta users were demonstrating GPT-3 enabled re-imaginations of tasks like text summarization, code generation, and auto-translation and completion, just to name a few.

At Madrona, we see next-gen NLP generally and these transformer models specifically as enablers to building new companies and applications. Over the past months, we have closely followed the infrastructure developments and potential applications, searching for the right combination of team and use case to support. It came in the form of three young and ambitious founders who, just this summer, formed their company, OthersideAI.

OthersideAI is a next-gen productivity tool that increases the speed of communication. In the few short months since incorporating, the team has leveraged GPT-3 to build a product that takes short-form user input and generates full length emails in the user’s style of writing. It is delightfully simple, contextually aware, and ever-learning.

Since meeting the founders Matt Shumer, Jason Kuperberg and Miles Feldstein in August, we have been continually impressed with the speed of progress and future vision from this team. Just as their tool re-imagines communication, the company re-imagines processes. With OthersideAI, development cycles are measured in days, new features are shipped in hours, inboxes are cleared in minutes, and emails are written in seconds.

OthersideAI has been in alpha mode, working closely with the OpenAI team to refine and customize filters on top of the existing base GPT-3 models. Currently, the product is a Chrome extension that integrates with Gmail. With this announcement, Otherside is officially launching their beta and will start to onboard the first cohort of users from the growing waitlist.

Productivity tools like OthersideAI are a key element of our Future of Work investment theme, augmenting a digital-first workflow with automation. Using OthersideAI promises to drastically reduce the hours spent writing email every day. With the push of a button, it opens access to powerful automation driven by the latest in artificial intelligence and natural language processing. And – alpha testers say it is working – enabling them to get through their inboxes 4x faster already.

We are thrilled to start this journey from Day One with the OthersideAI team and lead their seed round. Join the waitlist here!

Welcome WhyLabs – the AI Observability Platform for massive scale data monitoring and collaborative AI operations

At Madrona, we love partnering with founders from their very early days of idea formation and for the long run. Now that WhyLabs emerged from stealth, we are thrilled to announce that we led the $4 million Series Seed investment in WhyLabs, the AI observability platform for massive scale data monitoring and collaborative AI operations. We joined forces with AI2, Bezos Expeditions, Defy Partners and Ascend.vc.

Thanks to an intro from Jacob Colker at the Allen Institute for Artificial Intelligence (AI2), Maria first met the WhyLabs CEO, Alessya Visnjic, a long-time Amazon software engineer and technical leader, when she was in the early phase of exploring the idea. She kept coming back to a pain point that she experienced at Amazon as a pager-carrying engineer on what later became the core ML team. She was on-call to respond to model failures and data quality issues in internal ML deployments. It quickly became apparent to her that AI needed its own specialized tooling ecosystem. Knowing that the kinds of tools that software engineers rely on to monitor traditional software at scale are not suitable for AI applications, she set out to build WhyLabs.

Alessya’s conviction matched perfectly with our investment thesis around both AI/ML Infrastructure and Intelligent Applications. Through our investments in companies like Algorithmia, OctoML, Snowflake, Turi, XNOR.ai, and Lattice Data, we had come to the same conclusion: while Intelligent Applications are on the rise, the tools that AI builders rely on are at best immature, causing data scientists and engineering teams to spend precious time and effort on non-value added work such as data sampling, error detection, and debugging.

With enterprises of all sizes adopting AI rapidly, global spending on AI is expected to double to $110 billion in four years, according to IDC. The demands on AI practitioners to deliver transparent and non-biased AI systems are ever higher. WhyLabs is a company built on first principles and their solution starts with the data as the single source of truth. The WhyLabs Platform sends AI builders actionable alerts, enabling them to respond to data quality issues and model meltdowns in real-time. We’ve been impressed with how scalable, intuitive, and elegant the platform is – it integrates with existing tools and workflows to support any data type at any scale. While we expect that cloud platforms and many ML platforms for model building and deployment will offer their own model monitoring, we think customers will also need a truly platform-agnostic AI monitoring solution that provides consistent, best-of-breed insights and observability into model performance regardless of where it is running. This follows the pattern demonstrated by traditional APM and monitoring leaders like DataDog and New Relic.

Consistent with Madrona’s strategy of investing in and supporting founders from “Day One for the Long Run,” Maria in particular has worked with Alessya and the team since before they even incorporated. She helped them hone their idea, validate it with customer prospects, develop go-to-market plans and overall strategy, and recruit co-investors. Maria led the investment round for Madrona and served as the Madrona board director. Along the way, Maria, Alessya and the WhyLabs team continued to work together so closely and cohesively that they decided to join forces with her coming on board as co-founder and COO.

Madrona was thrilled with this serendipitous, massive win-win for all parties. Maria brings incredible start-up and GTM experience to this deeply technical team. She joined Cloudflare pre-revenue as an early executive and head of business development helping it grow into the cloud security juggernaut it is today. Her experience scaling GTM dovetails and compliments Alessya, Andy, and Sam’s deep product and technical expertise, making us at Madrona even more excited about the trajectory for this company.

And this is the fourth collaboration Madrona has had with AI2 to fund and spin-out new companies (along with KITT.ai, XNOR.ai, and Lexion), and we are excited to continue to work with them to create new intelligent applications leveraging cutting edge AI to solve important customer problems.

You can experience the WhyLabs Platform or schedule a live demo on their website at www.whylabs.ai or join their community of AI builders on Slack.

Founded and Funded – AI, NLP and Technology in the Physician’s Office with Saykara

(Dr. Graham Hughes and Harjinder Sandhu of Saykara)

On the newest Founded and Funded podcast, join Madrona managing director, Tim Porter, as he sits down with Founder, Harjinder Sandhu, and President, Dr. Graham Hughes of Saykara to discuss the future of AI in the healthcare provider’s domain. Saykara is an AI, Natural Language Processing and speech recognition-based iOS application for physicians to help them significantly decrease the amount of time they need to spend charting – entering information into the electronic medical record. Saykara listens and assists the physician, much like a human scribe might do, interpreting and transforming the doctor- patient conversations into the salient content required for notes, orders, referrals, and scheduling.

But building a business around natural language processing and speech recognition, as you’ll hear, was no easy task. Harjinder and Graham reflect on how these two technologies have evolved – and how combining them with AI has led to an application that is changing physicians’ lives across the country. The two also talk about the challenges and thrills of building a startup that is doing something entirely new.

When Madrona first invested in Saykara in 2016 we were excited about the technology trends around voice, machine learning and natural language processing and it’s rewarding to see this come to fruition and be changing physician’s lives for the better.

Listen here or on the podcast platform of your choice!

Transcript

Tim

Hi, Harjinder. Hi Graham. Great to be here with you today in this fully remote virtual world. This is my first podcast recording where we’re not all in one little studio, but nice to see you both today.

Saykara is really the first truly intelligent AI assistant that automates physician charting. So, we’ll get more into that. Quick background on Harjinder and Graham, we’re going to talk a little bit today about Saykara, a little bit about startups and the challenge of founding and scaling startups successfully, and a little bit about things going on in healthcare IT broadly of which Saykara is in a really interesting part in the middle of that. Harjinder is the co-founder and CEO of the company. He started his career back as a professor of computer science at York University, and then co-founded a company called Med Remote, which was really one of the first automated medical speech transcription companies that was then acquired by Nuance and helped build what’s become a powerhouse business for Nuance around healthcare speech services. This is his second startup since Med Remote. He co-founded a company called Twistle also in healthcare IT, so he’s a veteran of healthcare and ML-related startups. So, lots of great insights there. And Graham joined as president of Saykara last year and comes with an interesting set of backgrounds as well. Most recently, as CEO of Sutherland Healthcare Solutions and before that, his experience ranged from being a doctor, he’s an MD himself, as well as building healthcare software systems like EMRs, working at GE healthcare and also working on machine learning applied to healthcare at SAS. And so, a great wealth of experience here that’s coming to bear to build Saykara.

 

Let’s jump in. Harjinder, as I mentioned, you’ve been working at the forefront of speech rec and ML in healthcare for over 20 years. Can you just give us a brief history, a primer, on how those technologies developed and where you see things today?

 

Harjinder

Healthcare has been one of the key drivers in the adoption of speech recognition. While in other industries, voice and speech have been interesting modalities to use and it’s alongside other things in healthcare, voice has always been one of the primary modalities that providers have used for documentation. Whenever a physician, a provider sees a patient, they’re required to document that encounter. They need to do so both for legal and billing and clinical purposes. If you’re a typical physician and you see 20 to 25 patients a day, every encounter that you do, with each of those patients, requires a page or two of documentation. If you add all of that up in a typical day, that’s a lot of documentation that a physician has to do.

Back in the day before the internet, physicians would dictate their notes and you’d have transcriptionists listening to those dictations and typing them up. Back in around 2000 or so, myself and a colleague and a few others started looking at how to apply speech recognition to this natural use of voice, which is how do we take this dictation and rather than have people sitting there typing it up, how do we apply speech recognition to this and make this process much more cost effective. Back in those days, transcription used to be about a 10 to 20-billion-dollar industry in this country. So that’s really where speech recognition found a home. In 2000, speech recognition was still very much in its early days, it wasn’t really ready for this space. It took us about three years to get speech recognition to the point where it was actually useful in this space and useful actually meant not that it was replacing transcriptionists, but that it was augmenting the transcriptionists.

 

So, if you think about the cost of transcription, the spend in the millions of dollars for a typical health system, our goal was to use speech recognition to augment the transcriptionist in a way that reduced that transcription costs. We were able to do so and do so very successfully, and speech recognition continued to improve over the years. The use of speech recognition as a tool directly for physicians started around the same time, but really was just for early adopters that would use it for documenting care, but it was really the wealth of data that we were able to capture through this speech recognition process, where we were augmenting transcriptions that gave rise to the real success of speech recognition in healthcare.

 

NLP, natural language processing, was always a secondary focus and that really came about because again, in healthcare, if you think about the use case of physician documentation — 20 years ago, almost all medical documentation was in the form of these narrative notes that were being created through the dictation process or else being typed up by physicians. So, the idea was, and many people were thinking about this back then, is that as long as medical documentation is a narrative format, it’s not really amenable to any kind of automated processes. You can’t do simple analytics on it. You can’t do any kind of drug-drug interaction checking, if all you have is a bunch of narrative that talks about these medications and allergies and all that kind of stuff. So, we had started working on ways to interpret what the system was hearing through this dictation process and that NLP work continues till this day. It actually turned out that NLP was a much harder challenge when it came to interpreting natural language dictations by physicians than speech recognition. Speech recognition has gotten to the point of course today where it’s a commodity. Lots of companies do speech recognition very, very well, but NLP continues to be a challenge.

 

Tim

That’s great. When we originally invested in Saykara a few years ago, we loved the technology trends around voice, around machine learning and around NLP, and as we dug into the use case and problem you’re solving here for physicians, we just thought it was one of the great applications of these set of technologies to solve a really burning pain point for customers. I mean, you’ve both seen both large companies and small companies succeed and fail, maybe fail more often than not, trying to kind of crack the nut around these technologies in healthcare. What are some of the places you’ve seen, quickly, both succeed and fail and kind of what are those characteristics that gets to the kind of why now for Saykara?

 

Harjinder

I would say in terms of success, speech recognition by and large has been a tremendous success. It’s taken many years, but it’s been successful. The primary source of failure I think has been that NLP never kept pace with the quality of speech recognition. So, you think about, again, what everybody wanted to do was put structured data within the medical record and a lot of focus went into how do we take what physicians are saying through their speech recognition process and break it apart into the discrete data components. So, you want to be able to put what medications a patient’s on, what problems they’re being diagnosed with, what procedures they’re undergoing and then all the details around all of those directly into discrete fields in the EHR. And that, by and large, in years past failed. And I think the tail end of your question, in terms of why now for Saykara, largely, because we’re looking at right now is the confluence of speech recognition and NLP capabilities that are now able to do what we weren’t able to do 10 years ago. So, it’s not a bold prediction anymore to say that we are now on the cusp of having automated systems that can both transcribe and interpret what physicians are saying and put that data directly into the medical record.

 

Tim (

Graham, do you want to add anything there?

 

Graham

What I would say is that electronic health record systems and other vendors who’ve tried to look at this space have often made it too complicated and too much of a burden on the physician, or on the backend, it’s not granular enough to really be usable and meaningful for the physician. So, the confluence of technologies and the pressures that doctors are under, I think just makes this the right time.

 

Tim

Yeah, that’s great. So some big . . . the confluence of some big technology trends that have been building for years now, and some big healthcare trends, and I want to come back and talk more about some of those healthcare specific trends, but maybe describe exactly what Saykara is doing. Right? We sort of shorthand AI virtual scribe. What is that and what makes Saykara different and talk about what really are we solving for physicians in the healthcare system.

 

Harjinder

Ultimately physicians just want to talk to their patients, and they want to provide care. They don’t want the tedium of documentation. So, what Saykara focuses on is listening in on doctor-patient conversations and interpreting those conversations and generating out of that conversation a note that otherwise a physician would have to create. Ultimately, the holy grail in this space is that a system such as ours can listen in on that doctor-patient encounter and simply interpret and create that note. I’ve already mentioned that NLP has come a long way, but it remains a very difficult challenge to figure out how to interpret conversations. It’s hard enough to just interpret a dictation, let alone a two-way conversation between a doctor and a patient and then sometimes of course you have more than just two parties in the room. And so, I think the differentiator here for Saykara as a company is our ability to do this successfully in an augmented fashion. What I mean by that is, that having a system that can do this completely autonomously without any human assistance remains our goal, our vision, and something that we’re working towards and making progress towards, but in the meantime, we use a augmented AI solution and have humans that are helping the system to learn. What differentiates us from, I think, virtually everybody else in this space is our ability to actually make that AI system continuously learn from the human in that loop. I would say there’s a lot of companies in this space that are purely human only solutions and they may talk about how they’re trying to incorporate AI into their solutions, but they’re by and large just human transcription companies. And what we’ve been able to do is create a platform that incorporates a combination of AI and human, and in many tasks now, the AI is actually able to do this completely autonomously on specific kinds of tasks within that encounter. That capability is getting better and better over time.

 

Tim

We have this broad investment theme around intelligent applications, and I think a core piece to successful intelligent applications needs to be this continuous learning loop. We were impressed from the beginning and your vision for how to implement that and we think that fully automating this process with this learning system, yet being able to fully delight doctors right away, with the product that we have today, but yet it’s going to continue to get more efficient and over time, is really insightful and exciting part about Saykara and what differentiates you. We also always think about with an investment, you know, who are all the constituents that matter here. And so, clearly, helping physicians is the number one driver, and it helps them with their documentation burden, etc., but health systems like this too, because physician burnout is a key issue for them. They’re not getting the data into the EHR that they originally intended, you know, to be there. I think patients, you know, we’ve learned, like this also kind of makes it more transparent, “What is my physician doing when I’m trying to tell her or him what’s wrong and they’re typing away on the screen and not paying attention to me?” So, really this kind of value prop that’s for the physician, for the system and for the patient I think is really important and exciting about this space. But Graham, not to put blame on you, but you built some of these EHR systems, how do we get to this point, right, where the EHR was supposed to capture all this information, and it’s not fully succeeding at that and yet, physicians like hate it, and so it’s sort of on both ends, you know, causing friction and a problem, you know, in the world right now? How do we sort of get to this point and maybe talk a little bit about how you got excited about Saykara’s solution for it and have come on board here in the last year?

 

Graham

Yeah. That’s true. I kind of joke to Harjinder that I’m here to pay my penance for building these things. What the electronic health record companies tried to do, to their credit, was to try and make this as comprehensive as possible so that you could cover all angles. You know, they ended up, unfortunately though, by trying to accommodate all of those things, is that they created a monster, which meant that doctors ended up getting dragged into using the computer screen and pulled away from the patient interaction. You talked about burnout. That’s really a feeling that about 50% of the doctors in this country have, depending on specialty, where, they’re feeling disempowered, they’ve lost control, they don’t really have the passion for the work anymore and they’re doing the best that they can under terrible situation, where they’re spending sometimes up to three hours at night, trying to catch up on the documentation. With that said, my background, having been involved in physician workflow all my life — as a physician, then working in this space — and then having spent so much time working on natural language processing and analytics and advanced AI at SAS, it just felt to me that this was a problem that needs to be solved. And, I had the great fortune of meeting Harjinder. I love to work with people who live in the future. They’re developing assets that we will be using in four or five- or ten-year’s time. This one was incubated well enough that I could see the great potential, but also the reality of how this could work. So, I’m excited about it, Tim, and I get more excited every day and seeing what we’re doing with customers and the promise of the tech.

 

Tim

That’s great. Really a unique set of backgrounds that allowed you to see this problem/opportunity from all sides. Strategically the opportunity made a lot of sense to you and your experience allowed you to see it from various sides. You know, we had a lot of listeners to the podcast who maybe are not in healthcare related, but they maybe are at a big company thinking about going to a smaller company. So, maybe just talk for a minute on a personal level, making that decision, it made sense strategically, but then, it’s going to work every day and look, Saykara has hundreds of physicians on the system; Harjinder had built the company to a really interesting place, so it wasn’t like this was a de novo startup when you joined, but compared to Sutherland, much smaller. Talk about what that transition was like and how you kind of thought that this was the right time for you personally to make that kind of move once you saw that strategically the company and the solution made great sense.

 

Graham

Yeah, no, you’re right, a lot of organizations at the larger level are somewhat risk averse because they’re trying to keep their performance machine, the existing engine running, and innovation, which could potentially disrupt your existing business, is problematic. And you’ve probably seen, Tim, many, many organizations that have struggled with how to incubate new offerings out of large companies. And so, I actually had found that my passion lay with that type of innovation and transformation. And for me, having run large organizations, it just felt that working with a bunch of really smart people, people who I like, who have deep experience in the industry, strong tech, definitely are thinking about the transformation in healthcare that needs to happen is that for me, I wanted to take everything that I’d learned at a large product and services organizations and then just bring it into Saykara and see what we could do. I love the mission, love the challenge, love the people. Let’s do something really meaningful to transform the lives of hopefully hundreds of thousands of doctors.

 

Tim

You used the word passion and as we’ve seen people successfully go from bigger companies to smaller ones, that’s the number one thing. The second one is, kind of, revel in getting your hands dirty and going and doing stuff and not just kind of directing. We’ve seen that as you’ve jumped in with customers and analytics and internal processes, and we’ll get into a little more of that, but back to the offering. One of the fun things about Saykara is we all know doctors. We’ve heard about this burnout issue, but what are we seeing about the receptivity to use this type of technology? Where are we kind of in the cycle from an adoption standpoint?

 

Graham

It’s interesting because you often think of cool new tech and interesting tech, and you think about, you know, hey, this is going to appeal to the PlayStation generation of doctors — people who grew up with technology in their back pocket, they can’t remember life without Google, and they think that maybe they would be the only folks who are really drawn to this. In fact, nothing could be further from the case. I think that there are physicians who started their career with the idea of just focusing on the patient and whether they had gone through the years of dictation and transcription or whatever, they immediately understand the idea that, hey, this thing would get me away from a computer, it allows me to focus. It’s by reducing all this hassle that’s been brought into their lives over the last 10 years, it’s actually like coming home. So, for doctors, it’s like what they got into medicine for. The time’s right, we’re just seeing an incredible amount of demand out there in the industry, not surprising. Whether it’s doctors in large health systems, individual group practices, large multispecialty practices, ambulatory surgery centers, it’s all about just getting the friction out of their day-to-day life. That’s probably no different than many, many other industries. If you can make it easy for someone to get their job done, and you can make it as transparent as possible, people are going to want to do that.

 

Tim

You refer to the product as a virtual scribe, an AI virtual scribe. So, some physicians actually are fortunate enough to have a person that follows them around and that I believe is sort of why it’s called a scribe. One of the things we loved about, and I loved about Saykara, is it really democratizes access to that type of service, right? Where I think scribes today, it’s very high end specialties who can afford that, and Saykara is at a price point and capability where family docs can have access to this, and also, Saykara doesn’t leave for medical school every one or two years, having to retrain someone else. What are your thoughts and sort of that — Saykara, sort of this AI option versus an in-person?

 

Graham

There’s been an absolute explosion over the last few years of medical scribes, but the problem is, that’s a very expensive model is bringing another person into the room for every visit. It’s also, as you said, these are difficult to train up, you try to bring someone on board and make sure they understand your specialty, understand the way you work, understand the types of way that you document and work with patients. Often times, patients don’t feel that comfortable either having another person in the room. So, we found a tremendous amount of receptivity to the idea that there’s this virtual AI assistant on a mobile device, it just sits there on your mobile phone and, and is unintrusive. The doctor says, “Hey, do you mind if I use my AI assistant — this system that’ll help me here to make sure that we capture everything?” And, the patient has always agreed. I don’t think I’ve ever seen anyone or heard of anyone who’s got a problem with it.

 

Tim

Not to mention, there hasn’t been a lot of people standing together in rooms here over the last few months, how has COVID impacted this whole market? The move to telemedicine is well understood and well-documented, but has this new normal, is this a tailwind for Saykara? Is it a challenge? What have you seen?

 

Graham

For us, it’s a tremendous tailwind. Our system works just great for telemedicine visits. All that it has to do is to be able to really hear the physician, if it can hear the patient too, even better. But if you’re on a video call with a patient, it’s very difficult to be on your computer, hunting and pecking and clicking through menus. So, yeah, telemedicine visits when you’re face to face with a patient in that way, the ability to just pick up on the voice. Doctors love that ability to just continue to have a hundred percent attention through telemedicine visits and Kara picks it up just as it would do if the patient was in the room.

 

Tim

So, Graham shared some color on what it’s been like and why he made a move from a bigger company to an earlier stage company. Harjinder I guess, at this point, you’re a serial entrepreneur. This is number three. What made you take this leap again? Why do you keep going after these healthcare problems and starting companies? Maybe share a little of the motivations and starting Saykara beyond the technical kind of strategic reasons you shared earlier.

 

Harjinder

Uh, in a word, I like punishment, I guess. Tim, as you probably know, having invested in a lot of companies that the highs and the lows of a startup are pretty extreme versus being in a big company where you have a salary regardless of your successes or failures, but in a startup, you can go from one extreme to another very rapidly. The thrill that I get of a startup is particularly around building solutions that people want to use and that make a difference in people’s lives. I think that I would put a premium on that. When people, when physicians use our solution, it makes a real difference. We hear from physicians that they’re spending hours, literally hours less per day on documentation and the physician burnout problem is so huge, as Graham also talked about. But then, the other side of that, as I said, is getting to work on problems that are unsolved problems that are really difficult problems. Conversational AI is one of those problems. Twenty years ago, speech recognition was one of those problems and getting to kind of join these two things together, the joy of users getting satisfaction out of using your system and building something that’s really complex and hard and has never been done before. That’s what wakes me up every day and gets me excited about working.

 

Tim

You were mostly joking about the punishment comment, but is it fair to say that that largely refers to the sales cycles can be long, that you sort of like, hey, this solution solves the problem, but yet there can be a fair amount of friction to sort of successfully get installed and implemented? Is that sort of some of the challenge? And maybe expand a little bit on other people starting companies in the healthcare space, any advice from what you’ve learned across these three companies and what it takes to be successful?

 

Harjinder

Yeh, so healthcare is particularly challenging largely. In healthcare, for most digital health companies, sales cycles can be anywhere from 9 to 18 months long. That’s typically what you’ll hear quoted from a lot of veterans in this space. Fortunately, ours are not that long, in general, and it really comes down to your sales strategy. We’re able to sell both to very large health systems, which do have these lengthy sales cycles, but next to that we’re also selling to small independent practice or specialty groups and they make decisions much more rapidly. We’ve been able to turn around a lot of deals within the space of a couple of months, which is phenomenal in healthcare. As far as advice to other entrepreneurs, I think the biggest thing I generally think about, and I recommend to others is that, when you’re doing a startup, you have to have, I think, two components, you have to have a big vision and you have to have a small vision. The big vision is, you know, if we’re wildly successful, what can we do here that’s earth shattering, changes the game completely. And you need that to motivate yourself, you need that as kind of that guiding vision of where we can get to. The problem that I see a lot of people get into is they get hung up on that big vision and they don’t actually see or map out the steps that are required to get there, and so, I always like to couple what we do with both that big vision and the small vision. The small vision for us is, there’s a physician that wants to do documentation, and forget about everything else, how do we make that physician happy? How do we save that physician two or three hours a day? Because if we can make that individual physician happy in his or her day-to-day work, we’re going to have the ability to do many more exciting things. I would say, having both those components in mind is critical.

 

Tim

That’s great advice. Couple your big vision with an initial specific problem that you’re going to go solve for a customer, and that they’re going to pay you money for it. I couldn’t say it better myself. Graham, you know, on this sales cycle and go-to-the market side of things, a big piece of what you’re leading for the company is scaling the go-to-market side of the business. Maybe give just a little bit of a sense for where things are today and what you see as the keys to scale to the next level and beyond. Again, with an eye towards, these I think principles and things you’re doing are really broadly applicable while you’re a healthcare IT company or another tech founder here looking to scale their company.

 

Graham

What I’ve learned over the years is you’ve got nothing if you don’t have happy customers. So it always, to me, on a go-to-market, sounds kind of, sort of back to front, which is you focus on your customers to be able to sell more, but it’s kind of obvious as well, is that focusing intensely on delighting our customers with great service and great responsiveness and a level of humility that shows that we respect the complexity of the work they do, and the problem that we’re trying to solve. Once you have that, then you’re in a position I think to start to work on the other pillars, which is figuring out how best to segment and target the market that you want to go after, who’s the right fit for your product in the short term. You’ll take those that happen to sort of find your products as well, but that you focus really your attention right on building the right market awareness, the right outreach, get the target market that you want. For us, we’re small getting the word out there and also managing channels. So, channel partnership relationships become very important. So, I’d say, start with the customer, make sure you’ve got the right market and then start to aggressively communicate to that market and you have to build channel partnerships to help you scale. So those are the three points that we’re focused on.

 

Tim

Talk a little bit more about the channel partnership part. We see a fairly consistent misconception or maybe mistake is for early stage companies to think they’re going to get partners and channels to help them early on before they build enough sort of scale direct selling, customer references, etc. On the other hand, particularly for our market, without figuring out how you work with the other people in the ecosystem and who might be partners, I think you’re really hamstringing yourself, also. So how do you kind of balance that — the direct selling piece versus channel partners, for our market?

 

Graham

Yeah. The point you’re making is right on. I mean our sense is that we’re now at a level where we, I think, have proven our capability in the marketplace, we’ve grown and we’re pretty well penetrated into a number of the markets or at least really started to get a lot of traction. My sense is that we’re looking now not for someone to sort of be the mega company that will push and promote our software and services and the capabilities we have. It’s more to do with finding those types of very strategic alliances where it’s in everyone’s best interest. Where I would recommend that people start is, pick one or two potential strategic partnerships that can help your channel, but no more than that, because you really need to stick to your knitting and make sure that you control the message, control your brand and control the service that you’re delivering. So, much of that, when I’m talking about channels, is on mutually beneficial sales channel relationships and co-marketing arrangements. You still need to control your brand, your customer experience, and ideally, you’re still controlling the majority of the sales cycle once you’ve identified the lead opportunity.

University of Washington AI Project Takes Madrona Prize At Industrial Affiliates Day

Photo: CoAI team, Joseph Janizek and Gabriel Erion with Tim Porter at the Allen School

Madrona awarded the 14th Annual Madrona Prize to the CoAI team at the University of Washington’s Paul G. Allen School of Computer Science & Engineering. CoAI: Cost Aware Artificial Intelligence for Health Care applies ML to help healthcare professionals use accurate predictive models in time-sensitive and potentially life-threatening situations. The field of cost-sensitive ML builds algorithms that automate the feature selection step, automatically choosing the best subset of input variables to make a high-accuracy prediction. CoAI applies this field to the clinical setting — where “cost” is time — and enables, for instance, an EMT to run the appropriate predictive model while in the ambulance ride to the ER, rather than losing critical minutes after the patient arrives.

The team of consisted of PhD/MD graduate students Gabriel Erion and Joseph Janizek with MDs, Carley Hudelson and Nathan White and the head of UW’s Laboratory for Artificial Intelligence for Medicine and Science, Professor Su-In Lee.

The Madrona Prize is awarded at the end of the Allen School’s annual Industry Affiliates Research Day. The event includes technical talks throughout the day and culminates in an open house and poster session that showcases the latest research projects and papers being pursued by faculty and students at the school. The Madrona Prize has been awarded for 13 straight years and goes to the project that combines excellent research with what we feel is the greatest commercial potential. Since Madrona’s inception more than two decades ago, Madrona has funded 18 companies out of the Allen School. These companies include Impinj (NAS:PI), SkyTap and Turi (acquired by Apple), and most recently, OctoML, a company based on the TVM research project that won a Madrona Prize in 2017.

“The Allen School at the UW is an incredibly important resource for our region and as the school has grown and actively attracted researchers from many different areas, we have seen the breadth and depth of innovation grow,” said Tim Porter managing director, Madrona Venture Group. “Talking with the students during the research day is truly one of the highlights of our year, and we are both excited and inspired by all of the innovative projects we saw.”

Each year, the Madrona committee also awards runner-up prizes. This year the runners up were:

Runners Up
AuraRing: Precise Electromagnetic Finger Tracking via Smart Ring
Farshid Salemi Parizi, Eric Whitmire, Alvin Cao, Tianke Li, Ishan Chatterjee
Advisor: Shwetak Patel

HomeSound: Exploring Sound Awareness In The Home For People Who Are Deaf And Hard Of Hearing
Dhruv Jain, Kelly Mack, Steven Goodman
Advisors: Leah Findlater and Jon Froehlich

Molecular tagging with nanopore-orthogonal DNA strands
Katie Doroschak, Karen Zhang, Melissa Queen, Aishwarya Mandyam, Jeff Nivala
Advisors: Karin Strauss and Luis Ceze

For past winners visit click here.

Tesorio, Applying AI to the Office of the CFO

Today, we are thrilled to announce leading Tesorio’s $10m Series A funding round. As a career CFO, I am always looking for ways to automate the back office and to apply modern technologies, such as ML/AI and RPA to the office of the CFO. When you are managing a company that is growing quickly, it is imperative that processes scale and do not break as the organization changes. That is why I was excited when I met Tesorio and saw a practical application of new algorithms and technology in a space that I have been involved in my entire professional life.

Throughout my career at both private and public companies I was constantly frustrated by how many important analyses happen in a bespoke excel spreadsheet. In today’s modern era, it is amazing how many crucial decisions are made, key conclusions are formed and key metrics are created with spreadsheets that are on the brink of breaking – too many links, formulas, dependencies and worksheets!

The ultimate financial metric for a company is Cash. Not just the current balance, but the trajectory of the balance. In the vast majority of companies this analysis is performed on a spreadsheet. One containing many links, often circular references, and pulling in data from multiple sources. The risk of an error, a break, is high. Equally importantly, a spreadsheet is not exactly a living, breathing thing even though we might pretend otherwise. Changes to data sitting in different silos do not flow easily into spreadsheets without complex processes and significant human involvement.

When I met the Tesorio team, it was exciting to be able to quickly dive into a product that was replacing the spreadsheet and adding an intelligent layer to the cash flow forecasting process. By pulling actual transactions from back-office systems, adding ML/AI to that history and allowing the user to add in unique transactions, the system uses a 3-part process to generate a cash flow.

In addition, Tesorio enables their clients to impact and improve their cash flow. The building blocks of cash flow – the inputs and outputs–are addressed in the Tesorio offering. Their AR Automation offers the ability to streamline the collection of AR (Accounts Receivables) by understanding when customers typically pay, automating customer contact to speed payment, and delivering a dashboard for finance teams to manage the workflow and communication that is core to successful collections. The same is true with the management of AP (Accounts Payable) and the forecasting and planning of hedging strategies. The result of all these areas is that much of the Finance and Accounting teams spend their day in the Tesorio application – all ultimately feeding the cash flow forecast.

The founding team, Carlos Vega and Fabio Fleitas together bring a unique combination of technical and financial expertise. They partnered together at UPenn, where Carlos was studying analytics at Wharton after spending nearly a decade in finance, and Fabio was studying computer science in the School of Engineering where he founded PennApps Fellows. Together they have brought to market a product that has already been adopted and used by an impressive list of companies including Veeva Systems, Box, WP Engine, Instructure, and Couchbase.

Finally, Tesorio squarely fits our Intelligent Applications thesis that we at Madrona have been focused on for several years. As we have discussed here, we expect intelligent applications to disrupt every business process by collecting data across different silos and applying ML/AI to that data to extract unique insights, automate workflows and even obliterate obsolete processes in some instances. In Tesorio, we believe we have finally found a product that provides the CFO and her team unique insights into the business and optimizes its finances like never possible before.

Our Investment In Lexion: Finding The “Data Needle” In A Haystack Of Documents

Today, Lexion announced a $4.2 million Series Seed funding round led by Madrona, and we couldn’t be more thrilled.

The business world is filled with and ruled by an ever-increasing mix of complex documents that need to be constantly managed – from customer and vendor contracts to insurance agreements to commercial real estate agreements. Exactly how and when “business gets done” is determined by the key details in these documents, and yet the process for tracking and acting upon these details is often highly manual, time consuming, and inconsistent. This is where the power of Artificial Intelligence (AI) and Natural Language Processing (NLP) come in.

Enter Lexion, a new offering for underserved mid-market businesses that provides a powerful application, based on AI and NLP technology developed at Seattle’s Allen Institute for Artificial Intelligence (AI2), built from the ground-up to ingest and understand your corporate contracts. Whether it be a pricing term, a partnership revenue share agreement, a renewal date, or an extension clause, the details are tedious to find and track and yet necessary to understand and be able to act on in order to run a business well. Lexion is your powerful magnet for locating that “needle hidden in the haystack” of your paperwork, and it helps make you smarter, faster, and more action-oriented when running your business.

Lexion Founders, Emad Elwany, Gaurav Oberoi, and James Baird.

Gaurav Oberoi, Lexion founder and CEO, is a Seattle-based entrepreneur whom we have known and have wanted to work with for a long time. He is a tremendous founder and well-known in the tech community both for his successes in and his enthusiasm for the Seattle startup scene. He founded and bootstrapped successful startups Billmonk (acquired by Obopay) and Precision Polling (acquired by SurveyMonkey) and originated the still popular startup email list, STS (SeattleTechStartups), over a decade ago. Gaurav, together with co-founders Emad Elwany and James Baird, came together at AI2 and formed the idea for Lexion over discussions on the common customer problem of intelligent document management and then built Lexion as a solution to solve it.

At Madrona, we love this customer- and problem-first approach to company creation and are excited to support the Lexion team from Day One. This investment also supports one of our core investment themes of ML-driven intelligent applications, in this case using advanced text mining and NLP techniques to extract structured data and insights from large corpuses of unstructured information to solve a vertical business problem, here specifically contract management. We think there will continue to be more opportunities in this form of entity extraction from large text sets, and this investment builds on the theme behind our other startup in this space, Lattice Data (acquired).

In talking to dozens of portfolio and other midmarket and growth companies, we heard this pain point and market opportunity reiterated over and over again. We were enthused as this company and technology came together to solve the problem, and are very excited to also have forward-thinking law firm, Wilson Sonsini, Goodrich and Rosati, the premier legal advisor to technology, life sciences, and growth enterprises worldwide, invest a sizable amount and join the board. WSGR clearly sees their clients dealing with this same issue of contract management and understands deeply the differentiated approach Lexion is bringing to market. We are looking forward to working with David Wang at WSGR and the team there to help build Lexion’s success.

Lexion is also the latest company to spin out from AI2 and the third we have funded (previous early stage investments were Kitt.ai and Xnor.ai). AI2 has proven to be an incredible incubation ground for companies – and it’s great to see Seattle and our region nurturing AI for the broader good.

To learn more about intelligently and cost-effectively managing your contracts – visit https://lexion.ai/

The Road to Cloud Nirvana: The Madrona Venture Group’s View on Serverless

S. Somasegar – Managing Director, Madrona Venture Group

The progression over the last 20 years from on-premise servers, to virtualization, to containerization, to microservices, to event-driven functions and now to serverless computing is allowing software development to become more and more abstracted from the underlying hardware and infrastructure. The combination of serverless computing, microservices, event-driven functions and containers truly form a distributed computing environment that enables developers to build and deploy at-scale distributed applications and services. This abstraction between applications and hardware allows companies and developers to focus on their applications and customers—not worrying about scaling, managing, and operating servers or runtimes.

In today’s cloud world, more and more companies are moving towards serverless products like AWS Lambda to run application backends, respond to voice and chatbot requests, and process streaming data because of the benefits of scaling, availability, cost, and most importantly, the ability to innovate faster because developers no longer need to manage servers. We believe that microservices and serverless functions will form the fabric of the intelligent applications of the future. The massive movement towards containers has validated the market demand for hardware abstraction and the ability to “write once, run anywhere,” and serverless computing is the next stage of this evolution.

Madrona’s Serverless Investing Thesis

Dan Li – Principal, Madrona Venture Group

Today, developers can use products like AWS Lambda, S3, and API Gateway in conjunction with services like Algorithmia, to assemble the right data sources, machine learning models, and business logic to quickly build prototypes and production-ready intelligent applications in a matter of hours. As more companies move towards this mode of application development, we expect to see a massive amount of innovation around AI and machine learning, application of AI to vertically-focused applications, and new applications for IOT devices driven by the ability for companies to build products faster than ever.

For all the above-mentioned reasons, Madrona has made several investments in companies building tools for microservices and serverless computing in the last year and we are continuing to look for opportunities in this space as the cloud infrastructure continues to evolve rapidly.Nevertheless, with the move towards containerization and serverless functions, it can be much harder to monitor application performance, debug applications, and ensure that applications have the correct security and policy settings. For example, SPIFFE (Secure Production Identity Framework for Everyone) provides some great context for the kinds of identity and trust-related work that needs to happen for people to be able to build, share, and consume micro-services in a safe and secure manner.

Below, you’ll hear from three of the startups in our portfolio and how they are building tools to enable developers and enterprises to adopt serverless approaches, or leveraging serverless technologies to innovate faster and better serve their customers.

Portfolio Company Use Cases

Algorithmia Logo

Algorithmia empowers every developer and company to deploy, manage, and share their AI/ML model portfolio with ease. Algorithmia began as the solution to co-founders Kenny Daniel and Diego Oppenheimer’s frustrations at how inaccessible AI/ML algorithms were. Kenny was tired of seeing his algorithms stuck in an unused portion of academia and Diego was tired of recreating algorithms he knew already existed for his work at Microsoft.

Kenny and Diego created Algorithmia as an open marketplace for algorithms in 2013 and today it services over 60,000 developers. From the beginning, Algorithmia has relied on serverless microservices, and this has allowed the company to quickly expand its offerings to include hosting AI/ML models and full enterprise AI Layer services.

AI/ML models are optimally deployed as serverless microservices, which allows them to quickly and effectively scale to handle any influx of data and usage. This is also the most cost-efficient method for consumers who only have to pay for the compute time they use. This empowers data scientists to consume and contribute algorithms at will. Every algorithm committed to the Algorithmia Marketplace is named, tagged, cataloged, and searchable by use case, keyword, or title. This has enabled Algorithmia to become an AWS Lambda Code Library Partner.

In addition to the Algorithm Marketplace, Algorithmia uses the serverless AI Layer to power two additional services: Hosting AI/ML Models and Enterprise Services where they work with government agencies, financial institutions, big pharma, and retail. The AI layer is cloud, stack, and language agnostic. It serves as a data connector, pulling data from any cloud or on-premises server. Developers can input their algorithms in any language (Python, Java, Scala, NodeJS, Rust, Ruby, and R), and a universal REST API will be automatically generated. This allows any consumer to call and chain algorithms in any combination of languages. Operating under a Kubernetes-orchestrated Docker system allows Algorithmia’s services to operate with the highest degree of efficiency.

As companies add AI/ML capabilities across their organizations, they have the opportunity to escape the complications that come with a monolithic application and begin to implement a serverless microservice architecture. Algorithmia provides the expertise and infrastructure to help them be successful.

Pulumi Logo

Pulumisaw an opportunity in 2017 to fundamentally reimagine how developers build and manage modern cloud systems, thanks in large part to the rise in serverless computing intersecting with advances in containers and managed cloud infrastructure in production. By using programming languages and tools that developers are already familiar with, rather than obscure DSLs and less capable, home-grown templating solutions, Pulumi’s customers are able to focus on application development and business logic rather than infrastructure.

As an example, one of Pulumi’s Enterprise customers was able to move from a dedicated team of DevOps Engineers to a combined Engineering organization—reducing their cloud infrastructure scripts to 1/100th the size in a language the entire team already knew and empowering their developers – and is now substantially more productive than ever before in building and continuously deploying new capabilities. The resulting system uses the best of what the modern cloud has to offer—dozens of AWS Lambdas for event-driven tasks, replacing a costly and complex queuing system, several containers that can run in either ECS or Kubernetes, and several managed AWS services like Amazon CloudFront, Amazon Elasticsearch Service, and Amazon ElastiCache—and now runs at a fraction of the cost before migrating to Pulumi. They have been able to spin up entirely new environments in minutes where it used to take weeks.

Before the recent culmination of serverless, containers, and hosted cloud infrastructure, such an approach simply would not have been possible. In fact, we at Pulumi believe that the real magic is in these approaches living in harmony with one another. Each has its own strengths: containers are great for complex stateful systems, often taking existing codebases and moving them to the cloud; serverless functions are perfect for ultra-low-cost event- and API-oriented systems; and hosted infrastructure lets you focus on your application-specific requirements, instead of reinventing the wheel by manually hosting something that your cloud provider can do better and cheaper. Arguably, each is “serverless” in its own way because infrastructure and servers fade into the background. This disruptive sea change has enabled Pulumi to build a single platform and management suite that fully realizes this entire spectrum of technologies.

The future is bright for serverless- and container-oriented cloud architectures, and Pulumi is excited to be right at the center of it helping customers to realize the incredible benefits.

IOpipe co-founders Erica Windisch and Adam Johnson went from virtualizing servers at companies like Docker, to going “all in” on serverless in 2016. Erica and Adam identified serverless as the next revolution in infrastructure, coming roughly 10 years after the launch of AWS EC2. With a shift in computing moving towards a serverless world, there are new challenges that emerge. From dozens of production Lambda user interviews, Erica and Adam identified that one of the major challenges in adopting serverless was a lack of visibility and instrumentation. In 2016, Erica and Adam co-founded IOpipe to focus on helping companies build, ship, and run serverless applications, faster.

IOpipe is an application operations platform built for serverless architectures running AWS Lambda. Through the collection of high fidelity telemetry within Lambda invocations, users can quickly correlate important data points to discover anomalies and identify issues. IOpipe is a cloud-based SaaS offering that offers tracing, profiling, metrics, logs, alerting, and debugging tools to power up operations and development teams.

IOpipe enables developers to debug code faster by providing real-time visibility into their functions as they develop them. Developers can dig deep into what’s really happening under the hood with tools such as profiling and tracing. Once they’re in production, IOpipe then provides a rich set of observability tools to help bubble up issues before they affect end-users. IOpipe saw customers who previously spent days debugging tough issues in production now able to find the root cause in minutes using IOpipe.

Since launching the IOpipe service in Q3 of 2017, the company has seen customers ranging from SaaS startups to large enterprises enabling their developers to build and ship Lambda functions into production at an incredibly rapid pace. What previously took one customer 18 months can now be done in just two months.

IOpipe works closely with AWS as an advanced tier partner, enabling AWS customers to embrace serverless architectures with power-tools such as IOpipe.

Innovate.AI – Announcing Our Investment in Envisagenics

Pictured S. Somasegar and Nagraj Kashyap

Today, in partnership with M12 (the newly renamed Microsoft Ventures), we are excited to announce the N. American winner of the Innovate.AI competition, a global startup competition to find the next generation of companies building intelligent applications.

The winning company, Envisagenics, has been awarded a $1M investment from Madrona Venture Group and M12, and we look forward to joining them on their journey to help solve some of the toughest problems in healthcare and biotechnology today.

Envisagenics Team.

We first explored the idea of an AI-focused startup competition with M12 in early 2017. Both Madrona and M12 have made large investments in AI, and we thought M12 would be the best partner to take this idea and help us reach a larger base of startups across North America. M12 was excited to partner with us on this and in addition took it global.

We received 250+ applications from companies in North America. Startups needed to have built products, achieved some level of customer progress and have raised limited capital to enter.

Working together with the team at M12, we pared this list of 250+ down to 9 finalists, who were working on intelligent applications in the fields of healthcare, financial services, cybersecurity, software development, enterprise sales, computer vision and manufacturing automation.

Each of the final teams were then invited to spend two days in Seattle at the Madrona and Microsoft offices to get to know one another and pitch to a team of VCs from both firms. The final presentations typically involved a presentation, Q&A, and live demo of the products, and we were very impressed with the quality of products that these teams have built.

Envisagenics, our winning company, is in the business of applying AI to RNA splicing. Founded by Maria Pineda and Martin Akerman, the company is using their proprietary data and machine learning models to build a drug discovery platform targeted at the large number of diseases caused by RNA splicing defects.

This is a big problem and an impactful solution. Approximately 15% of all diseases are caused by disrupted splicing, including 50% of rare genetic disorders. Healthcare companies can drastically accelerate their drug discovery processes by using Envisagenics’s data and machine learning models to prioritize drug targets and biomarkers.

We believe there is massive potential in the future of intelligent applications, and we are thrilled to be investing in a company that is applying the power of ML and AI to a key pain point in the world of healthcare.

Please join us in welcoming Envisagenics to the Madrona and M12 families!

Saykara – Out of Stealth, Alexa for Physicians

At Madrona, we like to invest in the best entrepreneurs in the Pacific NW attacking the biggest technology markets in the world. Beyond this, we love when we find a founding team that understands and is addressing an acute customer pain point in a way that aligns with our key investment themes. Few times in the last decade have we found a company and a team that meets all of these criteria better than Saykara.

Madrona led the Series Seed in Saykara in 2016, and we are excited for the company to now emerge from stealth mode. Saykara provides an AI-powered, voice-activated virtual scribe for physicians. Think of it as an Alexa for doctors. Thematically, this aligns very well with several of our key investment themes: (1) voice and natural language as a key UI for applications and (2) ML/AI applied to vertical markets.

From a customer perspective, we talked to many physicians and health systems during due diligence and the pain point they have with laboriously filling out the electronic health record (EHR) is incredibly high. Physicians today face a dilemma: (a) type away in the EMR during an exam and thus disrupt and de-personalize the physician-patient interaction or (b) spend hours at night dictating or entering information and losing control of their personal lives. Health systems also have a dilemma. They can provide an in-person (human) scribe who follows the physician around and enters notes into the EHR, but this is generally cost prohibitive for all but the highest revenue generating physicians and specialties.

Creating further pressure for health systems, offering human scribes is becoming a competitive factor in determining which health system a physician decides to join or stay. Nevertheless, most physicians still use the old-fashioned approach of after-hours dictation, an $18B market. Not only is this time-consuming, the resulting EHR entry is the equivalent of an appended Word document – unstructured data that is difficult to search and analyze. There are other newer options using specialized equipment such as Google Glass for capturing the full recording audio/video of the patient visit, which is transcribed overseas. This is also an expensive option, and typically results with output data that has similar challenges to traditional transcription. The end result for health systems is not only overworked and frustrated physicians, but an EMR that is insufficiently populated and lacking in structured data that enables the type of patient-outcome improving and cost-saving analytics that were the original promise of the EMR.

Despite this searing pain point, this problem is a tough nut to crack. The technology is non-trivial, to say the least, and healthcare can be a difficult industry. It takes founders and a team who know both the tech and the market intimately. Co-founders Harjinder Sandhu (CEO) and Kulmeet Singh (board member) fit this bill perfectly. Harjinder and Kulmeet are pioneers in this space, founding the first automated medical transcription company (MedRemote) acquired by Nuance. From getting into the business with MedRemote, Nuance Healthcare has grown into a multi-hundred-billion-dollar business, and Harjinder was the VP/Chief Technologist of R&D for 5 years. Earlier in his career, Harjinder was a CS professor at York specializing in distributed computing.

Not only do we love the problem space and founding team, we think Saykara is building a better mousetrap underpinned with AI and ML technologies that are tuned just for this market. Saykara uses a Siri- or Alexa-like hotword (“OK Kara”) or physical tap on their smartphone to start and stop voice capture. The physician can then talk to the patient as they normally would. The Saykara system accurately transcribes the audio to text, parses the information into structured data, and intelligently inserts the structured data into the correct fields in the EHR. They are building ML that comes into play in two general areas: (1) specialized voice-to-text for natural language and medical vocabulary that accurately captures a physician’s natural verbal interaction with a patient and (2) intelligent parsing of the transcribed information and insertion into the correct field in the EHR.

Thus far the reception has been tremendous. Physicians love it because they can interact with patients in the natural way they always have, without using special equipment, typing, or after-hours dictation. Patients love it because they actually hear what and when the physician is capturing in their medical record. Health systems love it because it improves physician satisfaction, is significantly less expensive than a human scribe or other alternatives, and they end up with EHR data that (over time) can be analyzed to improve clinical decision support.

It is also important to note that Saykara’s ML-driven approach leveraging existing smartphone technology enables a price point that is accessible for all doctors, including family doctors for whom other options are generally price prohibitive.

We are excited to see Saykara come out of stealth and continue to help them in their mission to give ALL physicians back control of their lives and address this important pain point for the healthcare industry.

Welcome SmartAssist To The Madrona Family

It is exciting for me to announce our investment in SmartAssist and to welcome the team to our Madrona family. SmartAssist is applying AI to the business of customer support and is already assisting customers of brands you know including MailChimp and Twilio.

Application development and applications the last 10 years were primarily defined by movement to the cloud, SaaS delivery and touch as an interface. Looking ahead, we strongly believe that applications are going to be defined as intelligent applications with a broader set of natural user interfaces including voice/speech and vision. In our opinion, any application of consequence that is getting built now is an intelligent application. What differentiates the intelligent applications is the use of ML/AI and other techniques to apply on the ever-increasing data sets that enable applications to continuously learn and deliver more relevant and appropriate experience for the customers.

Applying ML/AI to intelligently automate use cases and workflows in enterprises is an area where we see a tremendous amount of opportunity and some of our recent investments reflect that investment thesis. As we think about beachhead use cases of ML/AI within enterprises, customer support stands out as one of the most tangible areas that could be fundamentally disrupted through technology.

By using intelligent routing, automated responses, and predictive modeling, SmartAssist helps enterprises significantly increase the efficiency and quality of services while decreasing customer service costs. The company is based on the platform developed by Wise.io which was acquired by GE in 2016. The team of Pradeep Rathinam and Prashant Luthra had a passion for this business and are taking that core business and building it into SmartAssist. Already they have secured some name brand customers. GE has an interest in the company and we expect to work with them as the company grows.

Another big reason for why we are excited about this investment is the entrepreneurial strength of the founding team. Both Pradeep and Prashant have led and been a part of successful start-ups in the past. Their focus and passion to make a difference in this space makes it a delight to partner with them.

We are thrilled to back the compelling vision of this leadership team and be part of a Seattle area start-up that is focused on driving customer success using ML/AI. All of us at Madrona are jazzed at the potential of what is possible here.

Looking forward to helping realize the potential with the SmartAssist team!

Madrona’s 2017 Investment Themes

Every year in March, Madrona wraps up what happened in 2016 and we sit down with our investors to talk about our business – the business of finding and growing the next big Seattle companies. First and foremost, our strategy is to back the best entrepreneurs in the Pacific NW attacking the biggest markets. But we also overlay this with key themes and trends in the broader technology market. As part of our annual meeting we present our key investment themes for the year. Below is a snapshot of what we are focusing on:

Business and Enterprise Evolution to Cloud Native

Tim Porter-Madrona-Venture Capital Seattle
Tim Porter

The IT industry is in the early innings of its next massive shift. The transition to “cloud native” is as big or bigger as the move from PC to mainframes, the adoption of hypervisors, or the creation of public clouds. Cloud native at its core refers to applications or services built in the cloud that are container-packaged, dynamically scheduled, and microservices-oriented. Cloud Native enables all companies to take advantage of the application architectures that were once the province of Google or Facebook. Companies like Heptio and Shippable are at the forefront of disrupting how IT infrastructure has traditionally been managed with vastly increased agility, computing efficiency, real-time data, and speed. We firmly believe software that helps applications complete the journey from development on a cloud platform to deployment on different clouds, and running them at scale, will become the backbone of technology infrastructure going forward. As such, we are interested in meeting more companies that are making it easier to network, secure, monitor, attach storage, and build applications with container-based, microservice architectures.

Intelligent Applications

Customers today demand their software deliver insights that are real-time, nimble, predictive, and prescriptive. To accomplish this, applications must continuously ingest data, increasingly using event-driven architectures, coupled with algorithm-powered data models and machine learning to deliver better service and novel, predictive recommendations. The new generation of intelligent applications will be “trained and predictive” in contrast to the old generation of software programs that were created to be “programmed and predictable.” We believe that intelligent applications which rely on proprietary datasets, event-driven cloud-based architectures, and intuitive multisense interfaces will unlock new business insights in real-time and disrupt current categories of software. Investments in intelligent app companies that leverage these trends will likely be our largest area of investment in coming years.

Voice and XR Interfaces for Businesses and Consumers

We believe the shift we are seeing for human computer interactions will be as fundamental as the mouse click was for replacing the command line or touch/text was for the rise of mobile computing. This shift will be as pertinent for the enterprise as it is for consumers, and in fact will serve to further blur the lines between productivity and social communication.

With voice, we are most excited by companies that can leverage existing platforms such as Alexa to create a tools layer, or build intelligent vertical end-service applications.

In the realm of XR (from VR to AR), we believe this is a long game. VR will not be an overnight phenomenon, but will play out over the next 5 years as mobile phones become VR capable and, particularly, as truly immersive VR headsets become less expensive and cumbersome. We are committed to this future and are particularly focused on VR/AR technologies that bring the major innovation of “presence” into a shared or social space, as well as “picks-and-shovels” technology that are needed by the XR community now to start the building process now even in advance of a largescale install base of headsets.

Vertical Market Applications that use proprietary data sets and ML/AI

As algorithms continue to become more accessible by way of access to open-source libraries and platforms such as the one our portfolio company, Algorithmia, provides, we believe that proprietary data will be the bottleneck for intelligent apps. Companies and products with ML at their core must figure out how to acquire, augment, and clean proprietary, workable data sets to train the machine learning models. We are excited about the companies with these data sets, as well as companies, such as Mighty AI, that help build these data sets or work with companies to help them leverage their proprietary data to deliver business value.

One area where we see this is happening is when ML/AI and proprietary data is applied to intelligent apps in vertical markets. Vertical market focus allows companies to amass rich data sets and domain expertise at a far faster pace than companies building software that tries to be omni-intelligent, providing both product and go-to-market advantage. Most industry verticals are ripe for this innovation, but several stand out including manufacturing, healthcare, insurance/financial services, energy, and food/agriculture.

AI, IoT and Edge Computing

Linda Lian

IoT can be an ambiguous term, but fundamentally we see the explosion of devices connected to the Internet creating an environment where enterprise decision-making and consumer quotidian life will be crucially dependent on real-time data processing, analytics, and shorter response times even in areas where connectivity may be inconsistent. Real time response is crucial to success and is difficult to meet in the centralized, cloud-based model of today. For example, instant communications between autonomous vehicles cannot afford to be dependent on internet access or the latency of connecting to a cloud server and back. Edge computing technologies hope to solve this by bringing the power of cloud computing to the source of where data is generated. We are particularly committed to companies building technologies that are focused on solving how to bring AI, deep learning, machine vision, speech recognition, and other compute-heavy services to resource-constrained and portable devices and improve communication between them.

Another facet of IoT where we continue to have investment interest is new vertical devices for consumer (home, vehicle, wearable, retail), healthcare, and industrial infrastructure (electrical grid, water, public safety), along with enabling supporting infrastructure. Opportunities persist for networking solutions that improve access, range, power, discoverability, cost, and flexibility of edge devices and systems management that provide enhanced security, control, and privacy.

Commerce Experiences that Bridge Digital to Physical

Retail is in a state of flux and technologies are disrupting traditional models in more ways than e-commerce. First, physical retail isn’t going away, but it has a fresh new look. 85% of shoppers say they prefer shopping in stores due to a variety of factors including seeing the product and the social aspect. This has led the new generation of web-native brands such as Indochino, Warby Parker, Glossier and Bonobos to open stores – but they are very different, carrying little physical inventory and geared towards intimacy with customers and helping find the right product for the buyer.

Second, the decreasing cost of IoT hardware technologies such as Impinj’s RFID, advancements in distributed computing, and intelligent software such as computer vision will fundamentally alter physical retail experiences. Experiments are already underway at Amazon Go where shoppers can pick what they want and casually stroll out without waiting in a check-out line.

Within e-commerce, vertically integrated, direct-to-consumer models remain viable and compelling. They bypass costly distribution channels and can build strong brands and intimate customer experiences like Dollar Shave Club, Blue Apron, or Stitch Fix. Marketplaces that leverage underutilized resources or assets; or the technology that underlies these marketplaces remain relevant and compelling particularly for the millennial generation that prioritize access over ownership.

Security and Data Privacy

While certain security categories have been massively over-funded, new investment opportunities continue to arise. Security and data privacy are areas of massive concern for businesses, particularly in the current macro environment. Internally, enterprises demand full visibility, remediation tools, and monitoring capabilities to guard against increasingly sophisticated attacks. Particularly vulnerable are companies that house massive amounts of customer data such as financial services, big retailers, healthcare, and the government. Externally, the collection and analysis of massive amounts of real-time consumer behavioral and personal data is the bread and butter of sales, marketing, and product efforts. But new privacy laws in the US and imminent from the EU are creating heightened awareness of both the control and security of this data. We continue to be interested in companies and technologies that take novel approaches to protecting consumer data and helping corporations and organizations protect their assets.

Technologies Supporting Autonomous Vehicles

Transportation technology is experiencing a massive disruption. Autonomous driving will be the biggest innovation in automobiles since the invention of the car, impacting suppliers, car makers, ridesharing, and everything in between. Lines are blurring between manufacturer and technology provider. We believe the value creation in AVs will, not surprisingly, shift to software, and the data that makes it intelligent. More innovation is required in areas such as computer vision and control systems. Important advancements also remain to be made in component technologies such as radar, cameras, and other sensors. Indeed, there are billions of edge cases due to construction, pedestrians, weather, and a murky regulatory environment that must be ironed out both at the technology and policy level before the promise of AV is a reality.

Additionally, the rise of AV could massively disrupt current modes of car ownership. Fleet and operations management software will become increasingly important as AV transportation-as-a-service becomes more and more tangible. Software and systems for other vehicles including drones, trucks, and ships will also be huge markets and create new investment opportunities.

Seattle and the PNW are emerging as thought leaders in the area of AV, and we believe a technology center of excellence as well, creating new investment opportunities. We are deeply interested in all the threads that go into this complex and massive shift in technology, the car industry and in social culture.

Well, there you have it – Madrona’s key investment themes for 2017. Thanks for reading. If you are working on a startup in any of these areas – we would love to talk to you. Please shoot any of us a note – our email addresses are on in our bios on our website.

Xnor.ai – Bringing Deep Learning AI to the Devices at the Edge of the Network

Photo – The Xnor.ai Team

Today we announced our funding of Xnor.ai. We are excited to be working with Ali Farhadi, Mohammad Rastegari and their team on this new company. We are also looking forward to working with Paul Allen’s team at the Allen Institute for AI and in particular our good friend and CEO of AI2, Dr. Oren Etzioni who is joining the board of Xnor.ai. Machine Learning and AI have been a key investment theme for us for the past several years and bringing deep learning capabilities such as image and speech recognition to small devices is a huge challenge.

Mohammad and Ali and their team have developed a platform that enables low resource devices to perform tasks that usually require large farms of GPUs in cloud environments. This, we believe, has the opportunity to change how we think about certain types of deep learning use cases as they get extended from the core to the edge. Image and voice recognition are great examples. These are broad areas of use cases out in the world – usually with a mobile device, but right now they require the device to be connected to the internet so those large farms of GPUs can process all the information your device is capturing/sending and having the core transmit back the answer. If you could do that on your phone (while preserving battery life) it opens up a new world of options.

It is just these kinds of inventions that put the greater Seattle area at the center of the revolution in machine learning and AI that is upon us. Xnor.ai came out of the outstanding work the team was doing at the Allen Institute for Artificial Intelligence (AI2.) and Ali is a professor at the University of Washington. Between Microsoft, Amazon, the University of Washington and research institutes such as AI2, our region is leading the way as new types of intelligent applications takes shape. Madrona is energized to play our role as company builder and support for these amazing inventors and founders.

AWS re:Invent – the Big Announcements and Implications

The momentum continues to build and scale in leaps and bounds. That’s the overwhelming observation and feeling at the end of the 5th annual conference that Amazon hosted in Las Vegas last week for AWS (Amazon Web Services).

Here are some of the key take-aways that we think will have the highest industy impact.

Event-driven Functions and Serverless Computing

Serverless has definitely arrived. As expected, there were a number of new capabilities announced around Lambda, including C# language support, AWS Lambda@Edge to create a “CDN for Compute” and AWS Step Functions to coordinate the components of distributed applications using visual workflows through state machines. Beyond this, it was clear that Lambda, and the serverless approach overall, is being broadly woven into the fabric of AWS services.

In the world of event-driven functions, thinking about a standard way for people to publish events that make it easy to consume those events is going to be critical. Whichever platform gets there first will likely see a tremendous amount of traction.

Innovation in Machine and Deep Learning

AWS has had a machine learning service for a while now, and it was interesting to see a whole slew of new machine learning, deep learning and AI suite of services including Amazon Image Rekognition, Amazon Polly (Text to Speech deep learning service) and Amazon Lex (Natural Language Understanding engine inside Alexa that is now available as a service).

While the concrete use cases are still relatively spare, we – like Amazon – believe this functionality will be integrated into the functionality of virtually all applications in the future.

It is also clear that the proprietary data used to train models are what create differentiated and unique intelligent apps. The distinction between commodity and proprietary data is going to be critical as algorithms become more of a commodity.

Enterprise Credibility

In past years, whether it was intended or unintended, the perception was that taking a bet on AWS meant taking a bet on the public cloud. In other words, there was an unintended consequence of AWS as “all in on public cloud or nothing”. With the VMWare partnership, which was announced a couple months ago, but solidified on stage with VMWare’s CEO, Amazon clearly is supporting the hybrid infrastructure that many enterprises will be dealing with for years to come.

Equally noteworthy was the appearance of Aneel Bhusri, CEO of Workday, on stage to announce that Workday was moving to AWS as their primary cloud for production workloads. Clearly no longer just the realm of primarily dev and test, this is perhaps the strongest statement yet that the public cloud – and AWS in particular – is enterprise production capable.

Moving Up a Layer From a Set of Discrete Services to Solution-based Services

One big theme that showed through this year at AWS was the movement from a set of discrete services to complete solutions both for developers and for operators of applications and services. The beauty of this all is that AWS continues to move forward on this path in a way that is highly empowering for developers and operators.

This approach really shone through during Werner Vogels keynote on Day 2. He laid out AWS’ approach for the “modern data architecture” and then announced how the new service AWS Glue (fully managed data catalog and ETL service) covers all the missing pieces in terms of their end-to-end solution for a modern data architecture on AWS.

Eat the Ecosystem

One of the implications of AWS’ continued growth towards complete solutions is that they continue to eat into the domain of their partner ecosystem. This has been an implied theme in years past, but the pace is accelerating.

Some of the examples that drew the biggest notice:

• AWS X-Ray (analyze and debug distributed applications in production) which aims directly at current monitoring companies like New Relic, AppDynamics and Datadog
• AWS Lightsail (virtual private servers made easy) that, at $5/month, will put significant pressure on companies like Digital Ocean and Linode
• Rekognition (image recognition, part of AI suite described above) that provides a service very similar to Clarifai, who had actually been on a slide just a few prior to the service announcement!

No one should be surprised that AWS’ accelerating expansion will step on the toes of partners. An implication, as @benkepes tweeted, is that the best way to partner and extend AWS is to go very deep for a given use case because AWS will eventually provide the most common horizontal scenarios.

Partner Success = AWS Success

Although some of the new services conflicted with partner offerings, the other side of the coin was that AWS continues to embrace partners and is vested in partners’ success. They clearly understand that having their partners be successful ultimately contributes to more success for AWS. Having customers like Salesforce, WorkDay and Twilio take a complete bet on AWS , making the product of a partner like Chef be available as a fully-managed service on AWS, having a partner like Netflix excited to switch off their last datacenter as they are completely on AWS, and having a company like VMWare embrace AWS as their public cloud partner are some of the great examples of how Amazon is systematically working to ensure that their partners remain successful on AWS, all of which accrues more value and consumption of AWS.

Summary

The cloud opportunity is gigantic and there is room for multiple large players to have a meaningful position strength. However, as of today, Amazon is not just the clear leader but continues to stride forward in an amazing way.

First Published by Geekwire.