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RRE Blog

Our Investment in Ursa Space Systems

Will Porteous

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RRE is excited to announce our lead investment in Ursa Space Systems Inc. (“Ursa”), a company that is using Synthetic Aperture Radar (SAR) satellites to produce alternative data sets for major industrial and energy operators, financial services, and governments.

We’ve known Adam Maher, the Founder and CEO of Ursa, for several years and made a seed investment in the company last Fall. The team is exceptionally talented technically and commercially, with decades of experience in the satellite industry and the intelligence community.

The Earth Observation (EO) market represents a $43B opportunity over the next decade with SAR-based revenues one of the fastest growing components. Cumulative global revenues from SAR imagery alone is estimated to be $6.2B by 2025. That being said, there is a lack of data infrastructure to interpret the new radar data stream, leading to the current oversupply of existing SAR capacity. And while much of the new space industry’s focus has been on commercializing optical satellites, with startups like Orbital Insights and Descartes Lab providing analytics, there is huge potential in the SAR market given these satellites can see through darkness, clouds, and other obstructions.

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With Ursa, the difficulty in obtaining and analyzing radar data is abstracted away for customers. Ursa has secured relationships with all SAR satellite operators, built the digital infrastructure needed to properly ingest radar data, and created highly-tuned, expert processing to automate reliable, consistent measurements. The firm is further able to derive useful insights for its customers by layering on contextual data and machine learning / computer vision analyses.

Ursa’s flagship commercial product, Global Oil Storage Monitoring, is a weekly report measuring crude oil storage of over 340 oil terminals worldwide with 3.6B barrels of capacity. Customers in finance and energy rely on these comprehensive and timely analytics to gain decision advantages, changing the way they conduct business. In response to this first product’s success, Ursa is extending its focus across the oil supply chain and expanding the company’s custom monitoring to other global commodities. Ursa can extract considerable value from the raw image data given the significant overlap in commodity locations, areas of interest, and customer interests.

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“Ursa is making great progress across multiple aspects of their business,” said Will Porteous, General Partner at RRE Ventures and new Ursa board member. “They’re seeing early success with enterprise customers like S&P Global Platts, and users are already requesting new applications of the product for their business. There are many potential high-value applications for the technology. In addition, they have attracted some really impressive talent. We see a really strong team with a great value proposition and a huge opportunity in front of them that we are proud to help them achieve.”

Our Investment in Parallel Domain

Jason Black

I first learned about synthetic data generation from an article that described how researchers had used 3D renderings of eyeballs under a multitude of lighting conditions, in different orientations, of various colors, etc. to determine the direction of gaze for real world video analysis. As it turns out, it’s very difficult to determine, simply from security footage or for eye tracking applications precisely where a given person is looking — and there’s no good way to collect that data. You can estimate it by hand-labelling a human’s best guess at where the eyes are gazing frame-by-frame, but the level of inaccuracy is too high to produce useful data. As they say, “garbage in, garbage out.”


 Synthetic data generated by 3D renderings of eyes.  Source .

Synthetic data generated by 3D renderings of eyes. Source.

Well, synthetic data is some of the most pristine data you can get your hands on because you get a pixel-perfect labeled data set right from the outset (because you generated it!). No outsourcing frame-by-frame images to literally thousands of error prone and probably incredibly bored humans. And luckily, because synthetic data is generated by a computer, once you have a system to procedurally generate that data, it’s a massively scalable data source.

A massively scalable data source is going to be critical for training and testing autonomous vehicle (AV) software. While it is indeed possible to drive millions of miles to gather data about the public roads (as Waymo, Uber, and others have spent untold millions of dollars to do), relying purely on real-world data is not a scalable approach.

Why? Roads, traffic conditions, weather, pedestrians, obstructions, and a whole host of other dynamic elements are in constant flux and present a gargantuan number of variations that make it incredibly difficult for AV software to safely navigate. And every time AV software companies want to push out a point update (e.g. Version 1.1 of your autonomous vehicle software), they will need to scalably test their new algorithms to ensure that their updates work safely across the trillions of possible scenarios a car can encounter on the open road. Just as much as your iOS or Android point updates sometimes inadvertently crash your entire phone based on an unforeseen bug, autonomous vehicles can and, unfortunately, will inevitably introduce bugs into their navigation stack while updating systems.


 Realist rendering and the underlying pixel-perfect labeled synthetic dataset

Realist rendering and the underlying pixel-perfect labeled synthetic dataset

Realist rendering and the underlying pixel-perfect labeled synthetic dataset.

Parallel Domain is building software that can procedurally generate worlds that, due to their synthetic nature, are both scalable and come with pixel-perfect labels (see the labeled stripe on the image to the left). In doing so, Parallel Domain enables AV companies not only to drive billions of simulated miles before they push their software to the real world, but also generate more training environments where the AV software struggles. Say, for example, an update decreases safety when making a left turn in heavy traffic or if the sensors on the car get confused in fog, rain, or snow, then Parallel Domain’s software can generate millions of small variations of those exact scenarios to help the company improve performance without risking public safety.


Real-world map data can be used to generate virtual worlds that mirror the public roads.

The RRE team and I are incredibly excited to be supporting Kevin McNamara and his world-class procedural graphics team as they build the digital infrastructure not just important, but vital to the safety of our autonomous transportation future.

Our Investment in Bend Financial

Jason Black

We are excited to announce our investment in Bend Financial, a modern Health Savings Account (HSA) platform that will revolutionize how individuals manage their healthcare expenses. When we came across this outstanding team, in a market that is ripe for disruption, we knew we had to be a part of what the company was building.

The experienced team at Bend is hyper focused on not only changing how consumers interact with healthcare spending, but also on simplifying the employers experience of offering an HSA. The team is led by serial entrepreneur, Tom Torre, who impressed us with his expertise in building businesses in both financial services and healthcare. Tom co-founded the company with strong leaders in different disciplines including technology veteran Gary Rush, corporate finance leader Gregg Santabarbara, and consumer healthcare experience expert Kerry Leonard.

HSA accounts have become an essential part of the healthcare system with the proliferation of High Deductible Health Plans, yet consumer experience has not changed and managing expenses has only gotten more complex. Enrollment has grown rapidly over the last few years and total enrollment is expected to reach 30M by 2020. Additionally, consumers are taking control of their overall healthcare experience and managing healthcare spending. The founders understood that the financial burden of healthcare is shifting to the individuals and that tools are needed to help people navigate the complexities of healthcare costs. We are confident that Bend will become a key part of this solution.

We are thrilled to be working alongside such great team and look forward to building a market-leading HSA solution that is easy for everyone to use.

Our Investment in Ladder

Maria Palma


RRE is excited to announce our lead investment in the Series B of Ladder, the company that introduced instant, simple, and smart life insurance exactly one year ago today.

While there has been innovation in the insurance industry over the past couple of years, many of the new companies created focused on the distribution of traditional products. We believe that innovation in distribution alone is not enough to build an industry leading company in this space. Innovation in technology, data, product, and consumer experience are critical to creating a leading next generation insurance company.

There is an opportunity to build such an offering in the life insurance space. Life insurance alone accounts for annual premiums of $138B in the US. Even though the industry is massive, there are many Americans today who are not adequately covered by life insurance, which accounts for an estimated $16T coverage gap today.

We feel that Ladder’s approach to technology, data, product, and customer experience, along their incredible team, set them up to be the leader in this space.  Ladder has built a full stack solution with proprietary technology across the life insurance value chain which includes customer acquisition, application, underwriting, policy admin, and claims management.

“Ladder’s full stack approach sets them up to deliver breakthrough innovation in this space,” said Stuart Ellman, Co-founder & General Partner of RRE Ventures. “We’re excited to work alongside this great team as they build the next generation life insurance offering.”

The Data Advantage

Ladder is one of the few companies we have seen in the life insurance industry that has built the full tech stack needed from the ground up to leverage data across every stage of the life insurance process.

There is an adage that life insurance is sold and not bought. In the traditional insurance ecosystem, insurance is sold and delivered by multiple parties from agents, brokers, and wholesalers to carriers and reinsurers. While incumbents in this space should have a data advantage, they often face challenges when trying to liberate their data across multiple disparate systems and entities. Yet this is an industry where data drives economics.  The better data you have, the better you can be at acquiring customers, assessing risk, underwriting, managing claims, and retaining customer over time.

Ladder built data into the foundation of everything they do, and this is a core advantage to their business.  They can use this data to create an innovative product, an ideal consumer experience, and a sustainable business model. As the industry continues to evolve, data will play an even larger role in the economics of all insurance companies.

Product Innovation

Ladder designed the term life product solely with the end consumer in mind and did not have to take into consideration agents or brokers due to their direct-to-consumer distribution model.

One of the first product innovations was the launch of dynamic life insurance, which allows people to  “Ladder Up” or “Ladder Down” coverage. The team understood that as people's lives change, their life insurance should seamlessly change with them. For example, if someone has a second child they can Ladder up their coverage, or if someone’s child graduates from college, they can save money by Laddering down their coverage. Dynamic coverage allows life insurance to become a part of a consumer’s overall financial picture, which can adapt as their financial situation changes.

Enabling product innovation in this space does not come without its technological and regulatory challenges. Ladder’s proprietary technology and full stack enable them to innovate at the product level. Equally important, they have key partnerships in the industry, from reinsurers to financial institutions, to integrate their technology and bring these products to markets. All of these foundational decisions have set them up to continue to innovate on their product and consumer experience into the future.

Consumer Experience

You can tell from Ladder’s tagline “Data is the heart, people are the soul” that they fundamentally care about the consumer experience. They bring this tagline to life by consistently striving to provide the best consumer experience from purchase to claim. They know how important this product can be to somebody’s life, as it was for CEO Jamie Hale when he was a life insurance beneficiary. By making it easy to buy life insurance, they are ensuring that more consumers have the benefits of this product.

Many prospective life insurance consumers drop out of the purchase funnel because the existing process is so cumbersome. The traditional experience takes 8 weeks, a process which Ladder has brought down to minutes.  Their system uses data analysis to help eliminate complexities within the traditional application process, enhancing the consumer experience. In their first year of launch, they are live across 44 states and writing hundreds of millions of dollars in coverage.  It is also not surprising that a majority of their customers bought life insurance policies on a mobile device outside of work hours, so perhaps it is possible after all for insurance to be bought and not sold.

Ladder’s fully-digital product eliminates manual processes and commissioned sales agents as well, and these savings are passed on to the consumer. In our first interaction with Ladder and throughout our diligence process, it was clear that they aim to delight their consumers with everything they build.


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Management Team

The final thing that sets this company apart is the outstanding management team. As early stage investors, we know that the success of a company comes down to its people. In this industry specifically, getting the right balance between technology and industry expertise is critical. We believe the Ladder team has these traits in their DNA and will define what the future of life insurance should be.

The team has experience building financial services companies and a deep understanding of their industry. They recognize that the incumbents are strong companies that will continue to adapt, so they need to be able to move faster and constantly improve their offering. Ladder continuously pushes the status quo to improve but also respects the complexity of the industry in which they operate.

Looking Forward

Ladder announced today that they have opened up their API access. This means that Ladder can now seamlessly integrate with investment platforms, lending companies, benefits managers, health and wellness innovators and more. We are proud to be backing this team with great co-investors such as Canaan Partners, Lightspeed Venture Partners, Nyca Partners, and Thomvest Ventures. We could not be more excited to work alongside Jamie, Jeff, Laura, Jack and the entire Ladder team to bring their vision for next generation insurance to life.

Bitly: A prime to B prime

Raju Rishi

I’ve consistently believed that the best companies are built with five core characteristics: an excellent team, a massive market opportunity, great product-market fit, limited competition, and a sound business plan. An integral part of the business plan is how to get from point A to point B as quickly a possible in a capital efficient manner. However, most great companies are smart enough to recognize when the market shifts and point B is the wrong destination. Then the important factor is how quickly you can move from A prime to B prime. Great companies aren’t only defined by how they start, but more importantly, how quickly they adapt to changing market conditions, case in point Bitly.

Bitly started as a social media link shortener. At the time, if you put a link in a Facebook status it took up most of the status and Twitter’s character limit made sharing links difficult. Bility’s link shortener was an elegant solution to that problem and it helped users share the content they wanted. However, over time social media companies evolved to offer their own link shortening capability. It was at that time that Bitly looked at where the market was headed. The team determined that there was still a huge opportunity for link shortening, but that it centered around enterprise link management.

No longer was Bitly trying to get to point B (world leader in link shortening) as fast as they could, now they were gunning for B prime (an enterprise-grade link management platform). The goal was to allow businesses to gain visibility into how their content was being shared and routed across platforms. By doing this, Bitly became a key part of marketing stacks for numerous enterprises because they knew what content their customers shared and where they wanted to share it. This year, Spectrum Equity realized the value that Bitly provided and purchased a majority stake in the business.

We are proud to have been part of such a strong, nimble team at Bitly. They have proven how important it is to move as fast as you can towards the end goal, while not being blind to the changing market dynamics. We are excited to see them continue to grow as they work with Spectrum Equity and are confident they will be an instrumental part of the enterprise marketing stack.

ClearGraph: Where Machine Learning is Today

Raju Rishi

We have a belief that we are seeing an inflection point in machine learning opportunities across enterprises. Technologists has been talking about machine learning for decades but four underlying shifts have made it so now is the time these companies will start to take off.

First, the availability of data has skyrocketed. Over the last 10 years enterprises started to save more and more of the data they created, leading to “big data” and consequently a trove of data for machine learning algorithms to learn on. It is estimated that more data will be created in 2017 than all prior years combined and, according to Domo, 90% of all data was created in the last two years.

Second, cloud computing has been widely adopted. The transition to cloud computing has revolutionized the world and allowed for billions of data points to be created. Additionally, users have access to large amounts of computing power for a fraction of the cost. As a result, the machine learning algorithms can be trained quickly. Even as edge computing starts to grow, cloud computing will continue to be the key place where low cost computation occurs.

Third, open source machine learning infrastructure became available. Machine learning has not been left out of the explosion of open source software, which has made machine learning more accessible to a wide variety of users. There are current open source projects that help with a number of aspects of machine learning, from projects that make it faster to train machine learning systems, to others that provide a framework to work within, to some that optimize workflow on GPUs.

Fourth, the advancements in machine learning algorithms cannot be understated. With a renewed focus in the field of machine learning there are a number of different algorithms that have evolved over last 10 years that enable people to better train what they are doing. The three broad categories (supervised learning, unsupervised learning, and reinforcement learning) have all seen significant progression.

These factors have led to a number of machine learning companies. More specifically they have provided the ability for companies to integrate machine learning into the problems they are trying to tackle, whether that be data liberation or optimization of articles on a webpage.

We saw in ClearGraph (formerly Argo) a team that was applying machine learning toward solving a large problem, enterprise search. The company was trying to solve a broad issue in a relatively crowded market, but we felt that the team as a whole stood out due to their technical expertise, clear vision, and ability to effectively execute. They succeeded in not only making data searchable through natural language, but also in solving the complex backend problems of data aggregation and normalization of data from disparate data sources. These backend problems are a key pain point for many enterprises today. The ability to accomplish all these goals, while also remaining focused on the growth of the company, helped propel ClearGraph to a terrific exit to Tableau.

Congrats to the team—we are happy to have been part of such a great company.