Tag: Artificial Intelligence

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.

 

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.

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/

Applying Machine Learning to Finding Great Talent – SeekOut

I’m excited to welcome SeekOut to our portfolio. SeekOut is a game-changing solution for recruiters and hiring managers to identify and connect with high performers who have a demonstrated track record of solving problems that are relevant to their needs.

In our capacity as the Talent Team at Madrona, Matt Witt and I help our portfolio companies – and the ecosystem at large in greater Seattle – uncover, attract, select and retain a diverse workforce of high performing individuals. We need great tools to do this work. Once every couple of decades a game-changing recruiting tool like SeekOut comes along to provide an order of magnitude advantage over previous solutions. Matt and I took SeekOut out for a spin last year to give feedback to Soma on the capabilities of the product. It didn’t take long for SeekOut to become an extension of our brains and our sourcing platform of choice. We now use it daily and recommend SeekOut to our portfolio companies as one of the most important tools they need for candidate discovery and engagement.

S. Somasegar (Soma) led our investment in SeekOut and he along with the founders, Anoop Gupta and Aravind Bala, know firsthand the pain of recruiting engineers from their days at Microsoft leading teams of engineers on projects with huge scopes. But since Anoop and Arvind were not recruiters by profession they spent a lot of time with their customers and that has paid off with a rich feature set that works for recruiters across a broad set of industries. They have applied the tools they used as engineering leaders on massive computational projects to the problem of matching data on people with the needs of companies that are trying to grow both quickly and intelligently.

“We see a lot of recruiting related solutions focused on the talent market. What made SeekOut stand out was the team’s pursuit of the solution, customer focus and perseverance which has paid off with the incredible customer adoption we have seen this year since the launch of the product,” commented Soma.

SeekOut recognized there is significant room to go beyond the open web or LinkedIn in terms of both data sources and ML/AI and with that, provide better insights on candidates in competitive markets. SeekOut leverages self-reported data from platforms like LinkedIn (what individuals say they’ve done) and performance data like GitHub and Patents/Publications databases (what they’ve actually done) and who they actually are (pedigree, work history, geography and specific demographics including diversity and contact information). The result is a massive, dynamic database that is constantly being updated to more effectively reach out to highly relevant candidates. We recently published our investment themes and SeekOut is a perfect example of our intelligent app category – they are taking a huge amount of raw data, organizing it and applying intelligence to it to deliver better outcomes for users.

Anoop and Aravind also found that SeekOut made it possible for non-technical recruiters to gauge the relative merits of engineering candidates based on the quality of their work products. Gauging engineering talent is something most non-engineers find impossible to do and is a constant source of frustration between engineering leaders and tech recruiters. SeekOut’s advanced features help recruiters dissect keywords to their root and suggests derivatives and alternatives while simultaneously learning about use cases and teaching recruiters what the terms actually mean. This gives recruiters the ability to go fast and compete more successfully.

As recruiters on the front lines, we can tell you that no other tool does this combination of things as well as SeekOut. Another recruiter told me “I was able to access hundreds of candidates through SeekOut that I hadn’t ever seen before on LinkedIn. It has been powerful specifically searching for female engineering managers. I’ve recently started looking at Data Science as well, and it feels like I’ve found a gold mine!”

Why and How Intelligent Applications Continue to Drive Our Investing

Intelligent Applications have been and continue to be a focus of our investing. These apps sit on top of the infrastructure a company chooses, the data they collect and curate, the machine learning they apply to the data and the continuous learning system they build. In this deep dive we talk about why intelligent applications are a central component to our investing themes and where we see the opportunities for company creation and building.

Intelligent apps are applications that use data and machine learning to create a continuous learning system that delivers rich, adaptive, and personalized experiences for users. These intelligent apps range from “net-new” apps like those powering autonomous vehicles and automated retail stores to existing apps that are enhanced with intelligence, such as lead scoring in a CRM app or content recommendations in a media app.

Intelligent apps will have a massive impact on the way we work, live, and play, and we have already been blown away by the potential in what we are seeing companies build today. Some of the most exciting intelligent apps we have seen do at least one of the following:

Enable completely new behaviors

Some of the most impressive demonstrations of machine learning are those that use AI to create new business processes and markets that completely change the way people do things. One high-profile example is Amazon Go stores using computer vision to completely change the supermarket or convenience store experience by removing the checkout process.

Another great example is Textio. Textio offers an AI-powered ‘augmented writing platform’ which draws on massive amounts of historical data to help companies write better job descriptions that will attract higher quality applicants. Both of these examples use AI to create new processes that result in better experiences and better outcomes for their users.

Drive 10x (or better) process improvements

AI automation and insights can also be used to optimize existing processes and workflows. Automation using AI is at the cornerstone of what every enterprise is going through in terms of digital transformation. For example, UiPath’s RPA platform allows companies to drastically reduce costs by automating a wide variety of software based tasks using UiPath’s “robots.” While the UiPath platform is early in its journey to becoming an intelligent app, it is already helping its customer drive 10x process improvements.

Suplari also uses AI to improve existing business processes, namely to analyze purchase behaviors to better understand how to drive cost reductions and manage supplier risks. While Suplari’s customers may have individual processes to reduce software costs through deduplication or to identify opportunities for savings in contract renewals, using AI to proactively identify the best opportunities allows their customers to realize large efficiencies in their procurement processes.

Integrate silos (data and workflows) and capture value

Another great opportunity for AI companies is to combine data and processes to allow companies to combine different parts of the value chain and capture more value. For example, Affirm uses machine learning to approve consumer loans and uses these loans to help ecommerce companies improve shopping cart conversion rates.

One of our portfolio companies, Amperity, literally combines different silos of customer data. Companies that have customer data stored in disparate systems and tools can’t easily leverage this data to get a full picture of their customer base. Intelligently stitching these silos together drives significant business results for Amperity’s clients who can now clearly see the stitched 360-view of their customers and use it to market and sell products in a more intelligent way.

Trends Converge

Now is an exciting time for investors and entrepreneurs to be focusing on intelligent applications because of the momentum and growth of several important technology trends:

  • Massive computational power and low-cost storage are creating the infrastructure to train machine learning models
  • More data is generated and stored than ever before in many different fields like healthcare, autonomous systems, and media
  • Availability of good-enough capabilities at the edge to do a lot of the inferencing work at the edge as opposed to having to round-trip to the cloud
  • Continued improvement and development in tools and frameworks make it easier for companies and developers to begin using machine learning
  • New “user interfaces” using voice, vision, and touch are bridging the gap between the digital and physical world

As these trends make it easier for entrepreneurs to build intelligent applications, we have been developing our own frameworks to understand how all of these pieces fit together to create value for customers. Generally, we think about the intelligent application ecosystem in three main parts:

  • The Data Platform Layer
  • The Machine Learning Platform Layer
  • The Intelligent Applications and “Finished Services” Layer

The Machine Learning Platform Layer

As an early believer in the potential of AI and machine learning, Madrona has made several investments in the machine learning platform layer, including companies like Turi, Lattice, and Algorithmia. This layer of the intelligent app stack is meant to make it easier for other developers and applications to make use of machine learning by providing the tools and automating tasks such as model training, model deployment, and model management.

The ML platform includes machine learning frameworks like TensorFlow and PyTorch, managed services and tools like Amazon Sagemaker and TVM, as well as “Model as a Service” providers in the form something like AWS Marketplace that can help developers and companies develop and deploy ML models in specific environments. While many of these tools have been developed by large companies or acquired by large companies, we believe there continues to be interesting opportunities at this layer because deploying and managing machine learning systems continues to be very difficult.

As an example, while the major cloud providers have made large investments in software and hardware to train ML models in the cloud, using those models for inference at the edge continues to be a difficult problem on resource-constrained devices. Xnor.ai is a portfolio company in this segment that uses software optimizations to improve the quality of machine learning predictions on edge devices that have limited power or bandwidth.

Overall, we believe that while frameworks and tools have been improving, advanced techniques like reinforcement learning still need frameworks and tools that are easier to use, and there are many interesting opportunities to continue improving the ML platforms that intelligent apps depend on.

The Data Platform Layer

A precursor to using AI effectively and building intelligent applications is having a “data” strategy. Having a unique data strategy that could be a combination of public data sets and proprietary data sets enables companies to provide unique and differentiated value. This is a necessary first step, before you can use the data to train models and build a continuous learning system that is a core part of building an intelligent application.

Within the Data Platform layer, we think of companies and products from portfolio companies, Datacoral and Snowflake, as well as those from Databricks and Amazon’s Redshift, which offer customers different ways to connect, transform, warehouse, and analyze data in order to be used in an ML platform. What we’ve seen at this layer of the stack is that getting data into the right place, in the right format, in order to be used for machine learning continues to be very difficult, and simplifying this process is extremely valuable to customers.

Additionally, access and ownership to data itself is a key part of the data platform layer. By this, we mean that companies need to be thoughtful about their data strategies in order to find ways to gain access to, generate, or combine different data sources in order to create unique data assets. As we are seeing frequently in the news these days, companies also need to be thoughtful about data privacy and making sure customers understand what data is being used, shared, and how.

The lines between the Data Platform Layer, the ML Platform Layer, and Intelligent Apps themselves can be quite blurry, especially as companies try to offer their customers a broader set of services or learn their way into new customer needs. However, we do see a distinction between companies that are focused on helping customers manage their data vs. helping customers manage their ML models.

 

Ultimately, we are looking for companies that can benefit from the virtuous data cycle – where more data creates better user experiences, leading to better user engagement, leading to more data, and ultimately better user experiences again.

Intelligent Applications and “Finished Services” Layer

Within the Intelligent Applications and Finished Services layer, there are several ways to segment the market. We like to think about verticals – applications that focus on a specific industry such as healthcare or insurance – and horizontals – cross-industry applications such as marketing automation or robotic process automation. One of the principles that we follow when looking for these types of opportunities is to find areas where data is becoming digitized and/or more data is being collected than ever before.

For example, one promising vertical for intelligent apps is healthcare. Technology and regulatory trends have driven the healthcare field to rapidly digitize many different types of records – from basic medical histories, to insurance claims, to x-rays, MRI scans, and ‘omics’ data (e.g., genomics, proteomics, biomics). This digitization of healthcare is creating new levels of visibility into patient and population health data, and ML will be a critical tool to help decision makers make sense of these new data sources.

Workforce productivity is another promising area for horizontal intelligent applications because more data is digitized than ever before in HR and employee engagement across industries. One example of a horizontal intelligent app is Madrona Venture Labs spinout company, UpLevel, which uses unstructured data from tools like Slack to help managers get better insights on how to best engage their teams and drive productivity.

In addition to vertical and horizontal apps for business users, we also include other types of “finished services” in this bucket. This can include services like Amazon Rekognition or Amazon Forecast, which help application developers add image and video analysis or time series forecasting models to other applications. In this case, the end customer for a product may not be a consumer, but the product is a “finished service” which can be plugged into a customer-facing application.

In each of these use cases, we are looking to find companies that deeply understand customer pain points and use machine learning as a tool to solve customer problems, rather than starting with a technology and searching for use cases.

Areas of Opportunity

We believe that every successful application built today will be an intelligent application, and that is why we think there is a huge amount of opportunity for entrepreneurs in this space. In particular, we would love to see more companies that are building at the nexus of multiple large markets, companies with unique data strategies, and companies with great ML teams (because AI continues to be very difficult). Four specific areas where we are excited to meet new companies are:

  • AI for Healthcare – More healthcare data is digitized and stored than ever before, and this is creating massive opportunities to reduce costs while improving quality of care and operations. The intersection of the biological sciences with computer science is going to be a difficult area to break through, but the potential value created will be huge, and we are looking for entrepreneurs who are ready to take on these challenges.
  • AI for Work – More and more, companies want to measure and become data-driven about productivity, hiring, and employee wellness. Traditionally, HR and workforce data has been incredibly hard to collect and analyze, but new applications like Slack and Workday are creating opportunities for startups like Polly and UpLevel to analyze workplace data to generate insights for employees and managers.
  • Automation – Robotic Process Automation (RPA) vendors are one set of companies building early intelligent apps that can analyze a business process and improve productivity through automation, but they will not be the last. We think there will also be opportunities to build vertical “RPA-like” businesses in specific industries, automation of manual work that can be dangerous and expensive, and new types of autonomous systems like autonomous vehicles.
  • “End-to-End AI” – Many companies have a section of their pitch explaining how valuable their data will be. We always encourage companies to think about the best use cases for their data, and, if it makes sense, execute on those use cases themselves. Some of our favorite examples in this category are companies like Climate Corp, which started with an ML system for predicting weather, found that they could use their predictions to sell weather insurance to farms, and eventually built an end-to-end farm management software system to capture more data and use it to write insurance policies.

Conclusion

During a recent CIO roundtable, we debated whether machine learning was an over-hyped or under-hyped technology trend. The answer in most people’s minds was both. There are incredibly high expectations for machine learning, and many of those expectations are not grounded in the reality of what ML can do today.

However, we believe that as we move forward, the ability to build new applications and continuously improve systems and processes using machine learning will be a core part of any app, and machine learning will be immensely impactful in every fabric of the society that we work and live in.

Current or previous Madrona Venture Group portfolio companies mentioned in this blog post: Algorithmia, Amperity, Datacoral, Lattice, Snowflake, Suplari, Turi, Xnor.ai, UIpath

Investment Themes for 2019

2018 was a busy year for Madrona and our portfolio companies. We raised our latest $300 million Fund VII, and we made 45 investments totaling ~$130 million. We also had several successful up-rounds and company exits with a combined increase of over $800 million in fund value and over $600 million in investor realized returns. We don’t see 2019 letting up, despite the somewhat volatile public markets. Over the past year we have continued to develop our investment themes as the technology and business markets developed and we lay out our key themes here.

For the past several years, Madrona has primarily been investing against a 3-layer innovation stack that includes cloud-native infrastructure at the bottom, intelligent applications (powered by data and data science) in the middle, and multi-sense user interfaces between humans and content/computing at the top. As 2019 kicks off, we thought it would be helpful to outline our updated, 4-layer model and highlight some key questions we are asking within these categories to facilitate ongoing conversations with entrepreneurs and others in the innovation economy.

For reference, we published our investment themes in previous years and our thinking since then has both expanded and become more focused as the market has matured and innovation has continued. A quick scan of this prior post illustrates our on-going focus on cloud infrastructure, intelligent applications, ML, edge computing, and security, as well as how our thinking has evolved.

Opportunities abound within AND across these four layers. Infinitely scalable and flexible cloud infrastructure is essential to train data models and build intelligent applications. Intelligent applications including natural language processing models or image recognition models power the multi-sense user interfaces like voice activation and image search that we increasingly experience on smartphones and home devices (Amazon Echo Show, Google Home). Further, when those services are leveraged to help solve a physical world problem, we end up with compelling end-user services like Booster Fuels in the USA or Luckin Coffee in China.

The new layer that we are spending considerable time on is the intersection between digital and physical experiences (DiPhy for short), particularly as it relates to consumer experiences and health care. For consumers, DiPhy experiences address a consumer need and resolve an end-user problem better than a solely digital or solely physical experience could. Madrona companies like Indochino, Pro.com and Rover.com provide solutions in these areas. In a different way, DiPhy is strongly represented in Seattle at the intersection of machine learning and health care with the incredible research and innovations coming out of the University of Washington Institute for Protein Design, the Allen Institute and the Fred Hutch Cancer Research Center. We are exploring the ways that Madrona can bring our “full stack” expertise to these health care related areas as well.

While continuing to push our curiosity and learning around these themes, they are guides not guardrails. We are finding some of the most compelling ideas and company founders where these layers intersect. Current company examples include voice and ML applied to the problem of physician documentation into electronic medical records (Saykara), integrating customer data across disparate infrastructure to build intelligent customer profiles and applications (Amperity), or cutting edge AI able to run efficiently in resource constrained edge devices (Xnor.ai).

Madrona remains deeply committed to backing the best entrepreneurs, in the Pacific NW, who are tackling the biggest markets in the world with differentiated technology and business models. Frequently, we find these opportunities adjacent to our specific themes where customer-obsessed founders have a fresh way to solve a pressing problem. This is why we are always excited to meet great founding teams looking to build bold companies.

Here are more thoughts and questions on our 4 core focus areas and where we feel the greatest opportunities currently lie. In subsequent posts, we will drill down in more detail into each thematic area.

Cloud Native Infrastructure

For the past several years, the primary theme we have been investing against in infrastructure is the developer and the enterprise move to the cloud, and specifically the adoption of cloud native technologies. We think about “cloud native” as being composed of several interrelated technologies and business practices: containerization, automation and orchestration, microservices, serverless or event-driven computing, and devops. We feel we are still in the early-middle innings of enterprise adoption of cloud computing broadly, but we are in the very early innings of the adoption of cloud native.

2018 was arguably the “year of Kubernetes” based on enterprise adoption, overall buzz and even the acquisition of Heptio by VMware. We continue to feel cloud native services, such as those represented by the CNCF Trail Map, will produce new companies supporting the enterprise shift to cloud native. Other areas of interest (that we will detail in a subsequent post) include technologies/services to support hybrid enterprise environments, infrastructure backend as code, serverless adoption enablers, SRE tools for devops, open source models for the enterprise, autonomous cloud systems, specialized infrastructure for machine learning, and security. Questions we are asking here include how the relationship between the open source community and the large cloud service providers will evolve going forward and how a broad-based embrace of “hybrid computing” will impact enterprise customer product/service needs, sales channels and post-sales services.

For a deeper dive click here.

Intelligent Applications with ML & AI

The utilization of data and machine learning in production has probably been the single biggest theme we have invested against over the past five years. We have moved from “big data” to machine learning platform technologies such as Turi, Algorithmia and Lattice Data to intelligent applications such as Amperity, Suplari and AnswerIQ. In the years ahead, “every application is intelligent” will likely be the single biggest investment theme, as machine learning continues to be applied to new and existing data sets, business processes, and vertical markets. We also expect to find interesting opportunities in services that enable edge devices to operate with intelligence, industry-specific applications where large amounts of data are being created like life sciences, services to make ML more accessible to the average customer, as well as emerging machine learning methodologies such as transfer learning and explainable AI. Key questions here include (a) how data rights and strategies will evolve as the power of data models becomes more apparent and (b) how to automate intelligent applications to be fully managed, closed loop systems that continually improve their recommendations and inferences.

For a deeper dive click here.

Next Generation User Interfaces

Just as the mouse and touch screen ushered in new applications for computing and mobility, new modes of computer interaction like voice and gestures are catalyzing compelling new applications for consumers and businesses. The advent of Alexa Echo and Show, Google Home, and a more intelligent Siri service have dramatically changed how we interact with technology in our personal lives. Limited now to short simple actions, voice is becoming a common approach for classic use cases like search, music discovery, food/ride ordering and other activities. Madrona’s investment in Pulse Labs gives us unique visibility into next generation voice applications in areas like home control, ecommerce and ‘smart kitchen’ services. We are also enthused about new mobile voice/AR business applications for field service technicians, assisted retail shopping (E.g., Ikea’s ARKit furniture app) and many others including medical imaging/training.

Vision and image recognition are also rapidly becoming ways for people and machines to interact with one another as facial recognition security on iPhones or intelligent image recognition systems highlight. Augmented and virtual reality are growing much more slowly than initially expected, but mobile phone-enabled AR will become an increasingly important tool for immersive experiences, particularly visually-focused vocations such as architecture, marketing, and real estate. “Mobile-first” has become table stakes for new applications, but we expect to see more “do less, but much better” opportunities both in consumer and enterprise with elegantly designed UIs. Questions central to this theme include (a) what ‘high-value’ new experiences are truly best or only possible when voice, gesture and the overlay of AR/VR/MR are leveraged? (b) what will be the limits of image (especially facial recognition) in certain application areas, (c) how effective can image-driven systems like digital pathology be at augmenting human expertise, and (d) how will multi-sense point solutions in the home, car and store evolve into platforms?

For a deeper dive click here.

DiPhy (digital-physical converged customer experiences)

The first twenty years of the internet age were principally focused on moving experiences from the physical world to the digital world. Amazon enabled us to find, discover and buy just about anything from our laptops or mobile devices in the comfort of our home. The next twenty years will be principally focused on leveraging the technologies the internet age has produced to improve our experiences in the physical world. Just as the shift from physical to digital has massively impacted our daily lives (mostly for the better), the application of technology to improve the physical will have a similar if not greater impact.

We have seen examples of this trend through consumer applications like Uber and Lyft as well as digital marketplaces that connect dog owners to people who will take care of their dogs (Rover). Mobile devices (principally smartphones today) are the connection point between these two worlds and as voice and vision capabilities become more powerful so will the apps that reduce friction in our lives. As we look at other DiPhy sectors and opportunities, one where the landscape will change drastically over the coming decades is physical retail. Specifically, we are excited about digital native retailers and brands adding compelling physical experiences, increasing digitization of legacy retail space, and improving supply chain and logistics down to where the consumer receives their goods/services. Important questions here include (a) how traditional retailers and consumer services will evolve to embrace these opportunities and (b) how the deployment of edge AI will reduce friction and accelerate the adoption of new experiences.

For a deeper dive click here.

We look forward to hearing from many of you who are working on companies in these areas and, most importantly, to continuing the conversation with all of you in the community and pushing each other’s thinking around these trends. To that end, over the coming weeks we will post a series of additional blogs that go into more depth in each of our four thematic areas.

Matt, Tim, Soma, Len, Scott, Hope, Paul, Tom, Sudip, Maria, Dan, Chris and Elisa

(to get in touch just go to the team page – our contact info is in our profiles)

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!

The Finalists in Microsoft Ventures and Madrona Venture Group Innovate.AI Startup Competition

It’s been an exciting time since we announced the Innovate.AI startup competition with our partners at Microsoft Ventures last October. What started as an idea we shared with our friends there, evolved into a global competition generating interest from some of the most innovative companies in the ML/AI field. We’ve been thrilled with the enthusiasm and strong response we received and would like to thank each participating company for their submission and the judges for the countless hours they spent evaluating each application.

The competition showcased the breadth of problems and use cases that companies are addressing by applying ML/AI. A couple of interesting observations about trends from the applicant pool emerged:

  • Intelligent Applications are on the rise – as data become plentiful and easily available and accessible, using AI and ML to build a continuous learning system is a fundamental fabric of every application is the way of the future. Many of the companies are targeting a variety of industries with plentiful and readily available datasets.
  • Innovation follows data availability – most companies that are thinking about innovative ways to provide insights and predictive analytics focus a lot on their data strategy and how to best organize and use the data they have as an integral part of the value they can and want to deliver to their customers.
  • Business models are still evolving: most ML/AI companies don’t fit the traditional software model of selling licenses or software-as-a-service. We saw a combination of business models, some leaning towards pure professional services, others a hybrid between licensing and SaaS. It’s clearly an area that will evolve as the companies mature.

Additionally, we saw a concentration in the following verticals:

  • Healthcare & Research: personal and mental health assistants, drug research and diagnosis, and computer vision to spot patterns and abnormalities.
  • Financial Services: research summaries and insights for investment professionals.
  • IoT & Edge Computing: analyzing data from edge devices, predictive maintenance, security and autonomous vehicle applications
  • Sales & Marketing: optimizing leads and focusing sales people on top opportunities.
  • Retail: Using computer vision to automatically recognize and tag items in images and video, enhanced advertising & shopping experiences.

And finally, we’d like to congratulate all of our finalists and welcome them to the final stage of the competition. Here is a closer look at who they are:

  • Alpha Vertex: cognitive systems for the financial services community.
  • ConceptualEyes: accelerates the speed of pharmaceutical research and discovery with artificial intelligence.
  • Envisagencis, Inc.: uses artificial intelligence to unlock cures for hundreds of diseases caused by RNA splicing.
  • FunnelBeam: a customizable sales intelligence platform.
  • ID R&D Inc: next-generation authentication solutions including voice, behavioral, and fusion biometrics.
  • TARA Intelligence Inc: a SaaS application to scope projects, assign developers, and monitor ongoing performance to build software faster.
  • Uru: fusing computer vision and artificial intelligence to create better ad experiences for video.
  • Wallarm: an adaptive, intelligent, application security platform.
  • Waygum, Inc.: intelligent IOT platform and mobile app for manufacturing.

 

To see a list of finalists in Europe and Israel, visit Microsoft Ventures.

 

Announcing the Microsoft Ventures and Madrona Venture Group AI Startup Competition

Today we are announcing our partnership with Microsoft Ventures to launch the Innovate.AI global startup competition. We are thrilled to be working with Microsoft on this project to find the world’s most promising early stage startups tackling unsolved problems, at scale, using machine learning and artificial intelligence.

At Madrona we have been early believers and investors in the transformative power of Artificial Intelligence and intelligent applications. The convergence of cloud computing (massive amounts of compute and storage available on the cloud at reasonable price points), specialized chips for machine learning (e.g., GPUs and FPGAs), and new advances in machine learning algorithms has created a unique opportunity for startups to build high value intelligent applications powered by unique and proprietary sources of data.

We have invested early with several platform companies and are actively looking at vertical applications. Examples include Turi, purchased by Apple, Lattice Data, purchased by Apple, Xnor.ai, MightyAI, Amperity, SmartAssist, Suplari, Saykara, Versive and others.

In thinking about how we might reach companies as early as possible we came up with a couple of ideas including developing an “American Idol” like competition. In discussions with our friends at Microsoft – we saw that we were aligned on interests in finding, supporting and investing in AI focused startups in the early days and we worked together to develop the AI:Innovate competition which Microsoft is taking Global.

We believe that in the next five to ten years, the impact of these technologies will transform most industries, from education, to real estate, to manufacturing, to transportation, to healthcare and we have only begun to see the impact of machine learning on the economy and society.

Over the next six months, Madrona will work closely with Microsoft Ventures to select a batch of promising startups that are either building new products and platforms with data and machine learning at their core or using AI to build the next generation intelligent applications. These startups will compete for a $1M investment from Madrona and Microsoft Ventures as well as an Azure package of credits, Office 365 licenses, and technical support worth over $500K.

Additionally, startups may also win an “AI for Good” prize that will be awarded to one startup that is using AI to create a positive impact on society, which will earn $500K in funding from Microsoft Ventures and Azure package of credits, licenses, and support worth over $500K.

The qualification requirements for startups to participate in the Innovate.AI global startup competition are as follows:

  • Companies that have raised no more than $4M in equity funding and/or loans at day of application
  • Companies that offer or intend to release or build a product or platform which utilize machine learning; such product/service/platform must be based on a model developed by the company, and/or be trained with data obtained/generated by the company or a 3rd party, and/or make use of pre-trained ML/AI APIs
  • Companies can utilize any technology stack or cloud platform to build their product, but the intention will be to work with the winners to help them utilize Azure and MS technology through our BizSpark offers and portfolio development efforts

See Peggy Johnson’s (EVP, Microsoft) blog post for more details about this competition.

Madrona is excited to kick off the Innovate.AI competition with Microsoft Ventures to find the next generation of great machine learning and artificial intelligence companies. The competition begins this month with applications, and we intend to select a final winner by Spring 2018. To find out more about entering the competition or submit an application, please visit the Innovate:AI page to view more information!

 

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.