Dec
18
2018
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Ex-Googlers meld humans & machines at new cobotics startup Formant

Our distinct skill sets and shortcomings mean people and robots will join forces for the next few decades. Robots are tireless, efficient and reliable, but in a millisecond through intuition and situational awareness, humans can make decisions machine can’t. Until workplace robots are truly autonomous and don’t require any human thinking, we’ll need software to supervise them at scale. Formant comes out of stealth today to “help people speak robot,” says co-founder and CEO Jeff Linnell. “What’s really going to move the needle in the innovation economy is using humans as an empowering element in automation.”

Linnell learned the grace of uniting flesh and steel while working on the movie Gravity. “We put cameras and Sandra Bullock on dollies,” he bluntly recalls. Artistic vision and robotic precision combined to create gorgeous zero-gravity scenes that made audiences feel weightless. Google bought his startup Bot & Dolly, and Linnell spent four years there as a director of robotics while forming his thesis.

Now with Formant, he wants to make hybrid workforce cooperation feel frictionless.

The company has raised a $6 million seed round from SignalFire, a data-driven VC fund with software for recruiting engineers. Formant is launching its closed beta that equips businesses with cloud infrastructure for collecting, making sense of and acting on data from fleets of robots. It allows a single human to oversee 10, 20 or 100 machines, stepping in to clear confusion when they aren’t sure what to do.

“The tooling is 10 years behind the web,” Linnell explains. “If you build a data company today, you’ll use AWS or Google Cloud, but that simply doesn’t exist for robotics. We’re building that layer.”

A beautiful marriage

“This is going to sound completely bizarre,” Formant CTO Anthony Jules warns me. “I had a recurring dream [as a child] in which I was a ship captain and I had a little mechanical parrot on my should that would look at situations and help me decide what to do as we’d sail the seas trying to avoid this octopus. Since then I knew that building intelligent machines is what I would do in this world.”

So he went to MIT, left a robotics PhD program to build a startup called Sapient Corporation that he built into a 4,000-employee public company, and worked on the Tony Hawk video games. He too joined Google through an acquisition, meeting Linnell after Redwood Robotics, where he was COO, got acquired. “We came up with some similar beliefs. There are a few places where full autonomy will actually work, but it’s really about creating a beautiful marriage of what machines are good at and what humans are good at,” Jules tells me.

Formant now has SaaS pilots running with businesses in several verticals to make their “robot-shaped data” usable. They range from food manufacturing to heavy infrastructure inspection to construction, and even training animals. Linnell also foresees retail increasingly employing fleets of robots not just in the warehouse but on the showroom floor, and they’ll require precise coordination.

What’s different about Formant is it doesn’t build the bots. Instead, it builds the reins for people to deftly control them.

First, Formant connects to sensors to fill up a cloud with LiDAR, depth imagery, video, photos, log files, metrics, motor torques and scalar values. The software parses that data and when something goes wrong or the system isn’t sure how to move forward, Formant alerts the human “foreman” that they need to intervene. It can monitor the fleet, sniff out the source of errors, and suggest options for what to do next.

For example, “when an autonomous digger encounters an obstacle in the foundation of a construction site, an operator is necessary to evaluate whether it is safe for the robot to proceed or stop,” Linnell writes. “This decision is made in tandem: the rich data gathered by the robot is easily interpreted by a human but difficult or legally questionable for a machine. This choice still depends on the value judgment of the human, and will change depending on if the obstacle is a gas main, a boulder, or an electrical wire.”

Any single data stream alone can’t reveal the mysteries that arise, and people would struggle to juggle the different feeds in their minds. But not only can Formant align the data for humans to act on, it also can turn their choices into valuable training data for artificial intelligence. Formant learns, so next time the machine won’t need assistance.

The industrial revolution, continued

With rock-star talent poached from Google and tides lifting all automated boats, Formant’s biggest threat is competition from tech giants. Old engineering companies like SAP could try to adapt to the new real-time data type, yet Formant hopes to out-code them. Google itself has built reliable cloud scaffolding and has robotics experience from Boston Dynamics, plus buying Linnell’s and Jules’ companies. But the enterprise customization necessary to connect with different clients isn’t typical for the search juggernaut.

Linnell fears that companies that try to build their own robot management software could get hacked. “I worry about people who do homegrown solutions or don’t have the experience we have from being at a place like Google. Putting robots online in an insecure way is a pretty bad problem.” Formant is looking to squash any bugs before it opens its platform to customers in 2019.

With time, humans will become less and less necessary, and that will surface enormous societal challenges for employment and welfare. “It’s in some ways a continuation of the industrial revolution,” Jules opines. “We take some of this for granted but it’s been happening for 100 years. Photographer — that’s a profession that doesn’t exist without the machine that they use. We think that transformation will continue to happen across the workforce.”

Dec
15
2018
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The limits of coworking

It feels like there’s a WeWork on every street nowadays. Take a walk through midtown Manhattan (please don’t actually) and it might even seem like there are more WeWorks than office buildings.

Consider this an ongoing discussion about Urban Tech, its intersection with regulation, issues of public service, and other complexities that people have full PHDs on. I’m just a bitter, born-and-bred New Yorker trying to figure out why I’ve been stuck in between subway stops for the last 15 minutes, so please reach out with your take on any of these thoughts: @Arman.Tabatabai@techcrunch.com.

Co-working has permeated cities around the world at an astronomical rate. The rise has been so remarkable that even the headline-dominating SoftBank seems willing to bet the success of its colossal Vision Fund on the shift continuing, having poured billions into WeWork – including a recent $4.4 billion top-up that saw the co-working king’s valuation spike to $45 billion.

And there are no signs of the trend slowing down. With growing frequency, new startups are popping up across cities looking to turn under-utilized brick-and-mortar or commercial space into low-cost co-working options.

It’s a strategy spreading through every type of business from retail – where companies like Workbar have helped retailers offer up portions of their stores – to more niche verticals like parking lots – where companies like Campsyte are transforming empty lots into spaces for outdoor co-working and corporate off-sites. Restaurants and bars might even prove most popular for co-working, with startups like Spacious and KettleSpace turning restaurants that are closed during the day into private co-working space during their off-hours.

Before you know it, a startup will be strapping an Aeron chair to the top of a telephone pole and calling it “WirelessWorking”.

But is there a limit to how far co-working can go? Are all of the storefronts, restaurants and open spaces that line city streets going to be filled with MacBooks, cappuccinos and Moleskine notebooks? That might be too tall a task, even for the movement taking over skyscrapers.

The co-working of everything

Photo: Vasyl Dolmatov / iStock via Getty Images

So why is everyone trying to turn your favorite neighborhood dinner spot into a part-time WeWork in the first place? Co-working offers a particularly compelling use case for under-utilized space.

First, co-working falls under the same general commercial zoning categories as most independent businesses and very little additional infrastructure – outside of a few extra power outlets and some decent WiFi – is required to turn a space into an effective replacement for the often crowded and distracting coffee shops used by price-sensitive, lean, remote, or nomadic workers that make up a growing portion of the workforce.

Thus, businesses can list their space at little-to-no cost, without having to deal with structural layout changes that are more likely to arise when dealing with pop-up solutions or event rentals.

On the supply side, these co-working networks don’t have to purchase leases or make capital improvements to convert each space, and so they’re able to offer more square footage per member at a much lower rate than traditional co-working spaces. Spacious, for example, charges a monthly membership fee of $99-$129 dollars for access to its network of vetted restaurants, which is cheap compared to a WeWork desk, which can cost anywhere from $300-$800 per month in New York City.

Customers realize more affordable co-working alternatives, while tight-margin businesses facing increasing rents for under-utilized property are able to pool resources into a network and access a completely new revenue stream at very little cost. The value proposition is proving to be seriously convincing in initial cities – Spacious told the New York Times, that so many restaurants were applying to join the network on their own volition that only five percent of total applicants were ultimately getting accepted.

Basically, the business model here checks a lot of the boxes for successful marketplaces: Acquisition and transaction friction is low for both customers and suppliers, with both seeing real value that didn’t exist previously. Unit economics seem strong, and vetting on both sides of the market creates trust and community. Finally, there’s an observable network effect whereby suppliers benefit from higher occupancy as more customers join the network, while customers benefit from added flexibility as more locations join the network.

… Or just the co-working of some things

Photo: Caiaimage / Robert Daly via Getty Images

So is this the way of the future? The strategy is really compelling, with a creative solution that offers tremendous value to businesses and workers in major cities. But concerns around the scalability of demand make it difficult to picture this phenomenon becoming ubiquitous across cities or something that reaches the scale of a WeWork or large conventional co-working player.

All these companies seem to be competing for a similar demographic, not only with one another, but also with coffee shops, free workspaces, and other flexible co-working options like Croissant, which provides members with access to unused desks and offices in traditional co-working spaces. Like Spacious and KettleSpace, the spaces on Croissant own the property leases and are already built for co-working, so Croissant can still offer comparatively attractive rates.

The offer seems most compelling for someone that is able to work without a stable location and without the amenities offered in traditional co-working or office spaces, and is also price sensitive enough where they would trade those benefits for a lower price. Yet at the same time, they can’t be too price sensitive, where they would prefer working out of free – or close to free – coffee shops instead of paying a monthly membership fee to avoid the frictions that can come with them.

And it seems unclear whether the problem or solution is as poignant outside of high-density cities – let alone outside of high-density areas of high-density cities.

Without density, is the competition for space or traffic in coffee shops and free workspaces still high enough where it’s worth paying a membership fee for? Would the desire for a private working environment, or for a working community, be enough to incentivize membership alone? And in less-dense and more-sprawl oriented cities, members could also face the risk of having to travel significant distances if space isn’t available in nearby locations.

While the emerging workforce is trending towards more remote, agile and nomadic workers that can do more with less, it’s less certain how many will actually fit the profile that opts out of both more costly but stable traditional workspaces, as well as potentially frustrating but free alternatives. And if the lack of density does prove to be an issue, how many of those workers will live in hyper-dense areas, especially if they are price-sensitive and can work and live anywhere?

To be clear, I’m not saying the companies won’t see significant growth – in fact, I think they will. But will the trend of monetizing unused space through co-working come to permeate cities everywhere and do so with meaningful occupancy? Maybe not. That said, there is still a sizable and growing demographic that need these solutions and the value proposition is significant in many major urban areas.

The companies are creating real value, creating more efficient use of wasted space, and fixing a supply-demand issue. And the cultural value of even modestly helping independent businesses keep the lights on seems to outweigh the cultural “damage” some may fear in turning them into part-time co-working spaces.

And lastly, some reading while in transit:

Dec
13
2018
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They scaled YouTube — now they’ll shard everyone with PlanetScale

When the former CTOs of YouTube, Facebook and Dropbox seed fund a database startup, you know there’s something special going on under the hood. Jiten Vaidya and Sugu Sougoumarane saved YouTube from a scalability nightmare by inventing and open-sourcing Vitess, a brilliant relational data storage system. But in the decade since working there, the pair have been inundated with requests from tech companies desperate for help building the operational scaffolding needed to actually integrate Vitess.

So today the pair are revealing their new startup PlanetScale that makes it easy to build multi-cloud databases that handle enormous amounts of information without locking customers into Amazon, Google or Microsoft’s infrastructure. Battle-tested at YouTube, the technology could allow startups to fret less about their backend and focus more on their unique value proposition. “Now they don’t have to reinvent the wheel” Vaidya tells me. “A lot of companies facing this scaling problem end up solving it badly in-house and now there’s a way to solve that problem by using us to help.”

PlanetScale quietly raised a $3 million seed round in April, led by SignalFire and joined by a who’s who of engineering luminaries. They include YouTube co-founder and CTO Steve Chen, Quora CEO and former Facebook CTO Adam D’Angelo, former Dropbox CTO Aditya Agarwal, PayPal and Affirm co-founder Max Levchin, MuleSoft co-founder and CTO Ross Mason, Google director of engineering Parisa Tabriz and Facebook’s first female engineer and South Park Commons founder Ruchi Sanghvi. If anyone could foresee the need for Vitess implementation services, it’s these leaders, who’ve dealt with scaling headaches at tech’s top companies.

But how can a scrappy startup challenge the tech juggernauts for cloud supremacy? First, by actually working with them. The PlanetScale beta that’s now launching lets companies spin up Vitess clusters on its database-as-a-service, their own through a licensing deal, or on AWS with Google Cloud and Microsoft Azure coming shortly. Once these integrations with the tech giants are established, PlanetScale clients can use it as an interface for a multi-cloud setup where they could keep their data master copies on AWS US-West with replicas on Google Cloud in Ireland and elsewhere. That protects companies from becoming dependent on one provider and then getting stuck with price hikes or service problems.

PlanetScale also promises to uphold the principles that undergirded Vitess. “It’s our value that we will keep everything in the query pack completely open source so none of our customers ever have to worry about lock-in” Vaidya says.

PlanetScale co-founders (from left): Jiten Vaidya and Sugu Sougoumarane

Battle-tested, YouTube-approved

He and Sougoumarane met 25 years ago while at Indian Institute of Technology Bombay. Back in 1993 they worked at pioneering database company Informix together before it flamed out. Sougoumarane was eventually hired by Elon Musk as an early engineer for X.com before it got acquired by PayPal, and then left for YouTube. Vaidya was working at Google and the pair were reunited when it bought YouTube and Sougoumarane pulled him on to the team.

“YouTube was growing really quickly and the relationship database they were using with MySQL was sort of falling apart at the seams,” Vaidya recalls. Adding more CPU and memory to the database infra wasn’t cutting it, so the team created Vitess. The horizontal scaling sharding middleware for MySQL let users segment their database to reduce memory usage while still being able to rapidly run operations. YouTube has smoothly ridden that infrastructure to 1.8 billion users ever since.

“Sugu and Mike Solomon invented and made Vitess open source right from the beginning since 2010 because they knew the scaling problem wasn’t just for YouTube, and they’ll be at other companies five or 10 years later trying to solve the same problem,” Vaidya explains. That proved true, and now top apps like Square and HubSpot run entirely on Vitess, with Slack now 30 percent onboard.

Vaidya left YouTube in 2012 and became the lead engineer at Endorse, which got acquired by Dropbox, where he worked for four years. But in the meantime, the engineering community strayed toward MongoDB-style non-relational databases, which Vaidya considers inferior. He sees indexing issues and says that if the system hiccups during an operation, data can become inconsistent — a big problem for banking and commerce apps. “We think horizontally scaled relationship databases are more elegant and are something enterprises really need.

Database legends reunite

Fed up with the engineering heresy, a year ago Vaidya committed to creating PlanetScale. It’s composed of four core offerings: professional training in Vitess, on-demand support for open-source Vitess users, Vitess database-as-a-service on PlanetScale’s servers and software licensing for clients that want to run Vitess on premises or through other cloud providers. It lets companies re-shard their databases on the fly to relocate user data to comply with regulations like GDPR, safely migrate from other systems without major codebase changes, make on-demand changes and run on Kubernetes.

The PlanetScale team

PlanetScale’s customers now include Indonesian e-commerce giant Bukalapak, and it’s helping Booking.com, GitHub and New Relic migrate to open-source Vitess. Growth is suddenly ramping up due to inbound inquiries. Last month around when Square Cash became the No. 1 app, its engineering team published a blog post extolling the virtues of Vitess. Now everyone’s seeking help with Vitess sharding, and PlanetScale is waiting with open arms. “Jiten and Sugu are legends and know firsthand what companies require to be successful in this booming data landscape,” says Ilya Kirnos, founding partner and CTO of SignalFire.

The big cloud providers are trying to adapt to the relational database trend, with Google’s Cloud Spanner and Cloud SQL, and Amazon’s AWS SQL and AWS Aurora. Their huge networks and marketing war chests could pose a threat. But Vaidya insists that while it might be easy to get data into these systems, it can be a pain to get it out. PlanetScale is designed to give them freedom of optionality through its multi-cloud functionality so their eggs aren’t all in one basket.

Finding product market fit is tough enough. Trying to suddenly scale a popular app while also dealing with all the other challenges of growing a company can drive founders crazy. But if it’s good enough for YouTube, startups can trust PlanetScale to make databases one less thing they have to worry about.

Dec
08
2018
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Why you need a supercomputer to build a house

When the hell did building a house become so complicated?

Don’t let the folks on HGTV fool you. The process of building a home nowadays is incredibly painful. Just applying for the necessary permits can be a soul-crushing undertaking that’ll have you running around the city, filling out useless forms, and waiting in motionless lines under fluorescent lights at City Hall wondering whether you should have just moved back in with your parents.

Consider this an ongoing discussion about Urban Tech, its intersection with regulation, issues of public service, and other complexities that people have full PHDs on. I’m just a bitter, born-and-bred New Yorker trying to figure out why I’ve been stuck in between subway stops for the last 15 minutes, so please reach out with your take on any of these thoughts: @Arman.Tabatabai@techcrunch.com.

And to actually get approval for those permits, your future home will have to satisfy a set of conditions that is a factorial of complex and conflicting federal, state and city building codes, separate sets of fire and energy requirements, and quasi-legal construction standards set by various independent agencies.

It wasn’t always this hard – remember when you’d hear people say “my grandparents built this house with their bare hands?” These proliferating rules have been among the main causes of the rapidly rising cost of housing in America and other developed nations. The good news is that a new generation of startups is identifying and simplifying these thickets of rules, and the future of housing may be determined as much by machine learning as woodworking.

When directions become deterrents

Photo by Bill Oxford via Getty Images

Cities once solely created the building codes that dictate the requirements for almost every aspect of a building’s design, and they structured those guidelines based on local terrain, climates and risks. Over time, townships, states, federally-recognized organizations and independent groups that sprouted from the insurance industry further created their own “model” building codes.

The complexity starts here. The federal codes and independent agency standards are optional for states, who have their own codes which are optional for cities, who have their own codes that are often inconsistent with the state’s and are optional for individual townships. Thus, local building codes are these ever-changing and constantly-swelling mutant books made up of whichever aspects of these different codes local governments choose to mix together. For instance, New York City’s building code is made up of five sections, 76 chapters and 35 appendices, alongside a separate set of 67 updates (The 2014 edition is available as a book for $155, and it makes a great gift for someone you never want to talk to again).

In short: what a shit show.

Because of the hyper-localized and overlapping nature of building codes, a home in one location can be subject to a completely different set of requirements than one elsewhere. So it’s really freaking difficult to even understand what you’re allowed to build, the conditions you need to satisfy, and how to best meet those conditions.

There are certain levels of complexity in housing codes that are hard to avoid. The structural integrity of a home is dependent on everything from walls to erosion and wind-flow. There are countless types of material and technology used in buildings, all of which are constantly evolving.

Thus, each thousand-page codebook from the various federal, state, city, township and independent agencies – all dictating interconnecting, location and structure-dependent needs – lead to an incredibly expansive decision tree that requires an endless set of simulations to fully understand all the options you have to reach compliance, and their respective cost-effectiveness and efficiency.

So homebuilders are often forced to turn to costly consultants or settle on designs that satisfy code but aren’t cost-efficient. And if construction issues cause you to fall short of the outcomes you expected, you could face hefty fines, delays or gigantic cost overruns from redesigns and rebuilds. All these costs flow through the lifecycle of a building, ultimately impacting affordability and access for homeowners and renters.

Startups are helping people crack the code

Photo by Caiaimage/Rafal Rodzoch via Getty Images

Strap on your hard hat – there may be hope for your dream home after all.

The friction, inefficiencies, and pure agony caused by our increasingly convoluted building codes have given rise to a growing set of companies that are helping people make sense of the home-building process by incorporating regulations directly into their software.

Using machine learning, their platforms run advanced scenario-analysis around interweaving building codes and inter-dependent structural variables, allowing users to create compliant designs and regulatory-informed decisions without having to ever encounter the regulations themselves.

For example, the prefab housing startup Cover is helping people figure out what kind of backyard homes they can design and build on their properties based on local zoning and permitting regulations.

Some startups are trying to provide similar services to developers of larger scale buildings as well. Just this past week, I covered the seed round for a startup called Cove.Tool, which analyzes local building energy codes – based on location and project-level characteristics specified by the developer – and spits out the most cost-effective and energy-efficient resource mix that can be built to hit local energy requirements.

And startups aren’t just simplifying the regulatory pains of the housing process through building codes. Envelope is helping developers make sense of our equally tortuous zoning codes, while Cover and companies like Camino are helping steer home and business-owners through arduous and analog permitting processes.

Look, I’m not saying codes are bad. In fact, I think building codes are good and necessary – no one wants to live in a home that might cave in on itself the next time it snows. But I still can’t help but ask myself why the hell does it take AI to figure out how to build a house? Why do we have building codes that take a supercomputer to figure out?

Ultimately, it would probably help to have more standardized building codes that we actually clean-up from time-to-time. More regional standardization would greatly reduce the number of conditional branches that exist. And if there was one set of accepted overarching codes that could still set precise requirements for all components of a building, there would still only be one path of regulations to follow, greatly reducing the knowledge and analysis necessary to efficiently build a home.

But housing’s inherent ties to geography make standardization unlikely. Each region has different land conditions, climates, priorities and political motivations that cause governments to want their own set of rules.

Instead, governments seem to be fine with sidestepping the issues caused by hyper-regional building codes and leaving it up to startups to help people wade through the ridiculousness that paves the home-building process, in the same way Concur aids employee with infuriating corporate expensing policies.

For now, we can count on startups that are unlocking value and making housing more accessible, simpler and cheaper just by making the rules easier to understand. And maybe one day my grandkids can tell their friends how their grandpa built his house with his own supercomputer.

And lastly, some reading while in transit:

Dec
06
2018
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Looker snags $103 million investment on $1.6 billion valuation

Looker has been helping customers visualize and understand their data for seven years, and today it got a big reward, a $103 million Series E investment on a $1.6 billion valuation.

The round was led by Premji Invest, with new investment from Cross Creek Advisors and participation from the company’s existing investors. With today’s investment, Looker has raised $280.5 million, according the company.

In spite of the large valuation, Looker CEO Frank Bien really wasn’t in the mood to focus on that particular number, which he said was arbitrary, based on the economic conditions at the time of the funding round. He said having an executive team old enough to remember the dot-com bubble from the late 1990s and the crash of 2008 keeps them grounded when it comes to those kinds of figures.

Instead, he preferred to concentrate on other numbers. He reported that the company has 1,600 customers now and just crossed the $100 million revenue run rate, a significant milestone for any enterprise SaaS company. What’s more, Bien reports revenue is still growing 70 percent year over year, so there’s plenty of room to keep this going.

He said he took such a large round because there was interest and he believed that it was prudent to take the investment as they move deeper into enterprise markets. “To grow effectively into enterprise customers, you have to build more product, and you have to hire sales teams that take longer to activate. So you look to grow into that, and that’s what we’re going to use this financing for,” Bien told TechCrunch.

He said it’s highly likely that this is the last private fundraising the company will undertake as it heads toward an IPO at some point in the future. “We would absolutely view this as our last round unless something drastic changed,” Bien said.

For now, he’s looking to build a mature company that is ready for the public markets whenever the time is right. That involves building internal processes of a public company even if they’re not there yet. “You create that maturity either way, and I think that’s what we’re doing. So when those markets look okay, you could look at that as another funding source,” he explained.

The company currently has around 600 employees. Bien indicated that they added 200 this year alone and expect to add additional headcount in 2019 as the business continues to grow and they can take advantage of this substantial cash infusion.

Dec
04
2018
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Cove.Tool wants to solve climate change one efficient building at a time

As the fight against climate change heats up, Cove.Tool is looking to help tackle carbon emissions one building at a time.

The Atlanta-based startup provides an automated big-data platform that helps architects, engineers and contractors identify the most cost-effective ways to make buildings compliant with energy efficiency requirements. After raising an initial round earlier this year, the company completed the final close of a $750,000 seed round. Since the initial announcement of the round earlier this month, Urban Us, the early-stage fund focused on companies transforming city life, has joined the syndicate comprised of Tech Square Labs and Knoll Ventures.

Helping firms navigate a growing suite of energy standards and options

Cove.Tool software allows building designers and managers to plug in a variety of building conditions, energy options, and zoning specifications to get to the most cost-effective method of hitting building energy efficiency requirements (Cove.Tool Press Image / Cove.Tool / https://covetool.com).

In the US, the buildings we live and work in contribute more carbon emissions than any other sector. Governments across the country are now looking to improve energy consumption habits by implementing new building codes that set higher energy efficiency requirements for buildings. 

However, figuring out the best ways to meet changing energy standards has become an increasingly difficult task for designers. For one, buildings are subject to differing federal, state and city codes that are all frequently updated and overlaid on one another. Therefore, the specific efficiency requirements for a building can be hard to understand, geographically unique and immensely variable from project to project.

Architects, engineers and contractors also have more options for managing energy consumption than ever before – equipped with tools like connected devices, real-time energy-management software and more-affordable renewable energy resources. And the effectiveness and cost of each resource are also impacted by variables distinct to each project and each location, such as local conditions, resource placement, and factors as specific as the amount of shade a building sees.

With designers and contractors facing countless resource combinations and weightings, Cove.Tool looks to make it easier to identify and implement the most cost-effective and efficient resource bundles that can be used to hit a building’s energy efficiency requirements.

Cove.Tool users begin by specifying a variety of project-specific inputs, which can include a vast amount of extremely granular detail around a building’s use, location, dimensions or otherwise. The software runs the inputs through a set of parametric energy models before spitting out the optimal resource combination under the set parameters.

For example, if a project is located on a site with heavy wind flow in a cold city, the platform might tell you to increase window size and spend on energy efficient wall installations, while reducing spending on HVAC systems. Along with its recommendations, Cove.Tool provides in-depth but fairly easy-to-understand graphical analyses that illustrate various aspects of a building’s energy performance under different scenarios and sensitivities.

Cove.Tool users can input granular project-specifics, such as shading from particular beams and facades, to get precise analyses around a building’s energy performance under different scenarios and sensitivities.

Democratizing building energy modeling

Traditionally, the design process for a building’s energy system can be quite painful for architecture and engineering firms.

An architect would send initial building designs to engineers, who then test out a variety of energy system scenarios over the course a few weeks. By the time the engineers are able to come back with an analysis, the architects have often made significant design changes, which then gets sent back to the engineers, forcing the energy plan to constantly be 1-to-3 months behind the rest of the building. This process can not only lead to less-efficient and more-expensive energy infrastructure, but the hectic back-and-forth can lead to longer project timelines, unexpected construction issues, delays and budget overruns.

Cove.Tool effectively looks to automate the process of “energy modeling.” The energy modeling looks to ease the pains of energy design in the same ways Building Information Modeling (BIM) has transformed architectural design and construction. Just as BIM creates predictive digital simulations that test all the design attributes of a project, energy modeling uses building specs, environmental conditions, and various other parameters to simulate a building’s energy efficiency, costs and footprint.

By using energy modeling, developers can optimize the design of the building’s energy system, adjust plans in real-time, and more effectively manage the construction of a building’s energy infrastructure. However, the expertise needed for energy modeling falls outside the comfort zones of many firms, who often have to outsource the task to expensive consultants.

The frustrations of energy system design and the complexities of energy modeling are ones the Cove.Tool team knows well. Patrick Chopson and Sandeep Ajuha, two of the company’s three co-founders, are former architects that worked as energy modeling consultants when they first began building out the Cove.Tool software.

After seeing their clients’ initial excitement over the ability to quickly analyze millions of combinations and instantly identify the ones that produce cost and energy savings, Patrick and Sandeep teamed up with CTO Daniel Chopson and focused full-time on building out a comprehensive automated solution that would allow firms to run energy modeling analysis without costly consultants, more quickly, and through an interface that would be easy enough for an architectural intern to use.

So far there seems to be serious demand for the product, with the company already boasting an impressive roster of customers that includes several of the country’s largest architecture firms, such as HGA, HKS and Cooper Carry. And the platform has delivered compelling results – for example, one residential developer was able to identify energy solutions that cost $2 million less than the building’s original model. With the funds from its seed round, Cove.Tool plans further enhance its sales effort while continuing to develop additional features for the platform.

Changing decision-making and fighting climate change

The value proposition Cove.Tool hopes to offer is clear – the company wants to make it easier, faster and cheaper for firms to use innovative design processes that help identify the most cost-effective and energy-efficient solutions for their buildings, all while reducing the risks of redesign, delay and budget overruns.

Longer-term, the company hopes that it can help the building industry move towards more innovative project processes and more informed decision-making while making a serious dent in the fight against emissions.

“We want to change the way decisions are made. We want decisions to move away from being just intuition to become more data-driven.” The co-founders told TechCrunch.

“Ultimately we want to help stop climate change one building at a time. Stopping climate change is such a huge undertaking but if we can change the behavior of buildings it can be a bit easier. Architects and engineers are working hard but they need help and we need to change.”

Nov
13
2018
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Rent tech-focused RET closes first fund; pours $5M into management platform SmartRent

Today, Real Estate Technology Ventures (RET Ventures) announced the final close of $108 million for its first fund. RET focuses on early-stage investments in companies that are primarily looking to disrupt the North American multifamily rental industry, with the firm boasting a roster of LPs made up of some of the largest property owners and operators in the multifamily space.

RET is one of the latest in a rising number of venture firms focused on the real estate sector, which by many accounts has yet to experience significant innovation or technological disruption. 

The firm was founded in 2017 by managing director John Helm, who possesses an extensive background as an operator and investor in both real estate and real estate technology. Helm’s real estate journey began with a position right out of college and eventually led him to the commercial brokerage giant Marcus & Millichap, where he worked as CFO before leaving to build two venture-backed real estate technology companies.  After successfully selling both companies, Helm worked as a venture partner at Germany-based DN Capital, where he invested in companies such as PurpleBricks and Auto1. 

Speaking with investors and past customers, John realized there was a need for a venture fund specifically focused on the multifamily rental sector. RET points out that while multifamily properties have traditionally fallen under the commercial real estate umbrella, operators are forced to deal with a wide set of idiosyncratic dynamics unique to the vertical. In fact, outside of a select group, most of the companies and real estate investment trusts that invest in multifamily tend to invest strictly within the sector.

Now, RET has partnered with leading multifamily owners to help identify innovative startups that can help the LPs better run their portfolios, which account for nearly a million units across the country in aggregate. With its deep sector expertise and its impressive LP list, RET believes it can bring tremendous value to entrepreneurs by providing access to some of the largest property owners in the U.S., effectively shortening a notoriously lengthy sales cycle and making it much easier to scale.

Photo: Alexander Kirch/Shutterstock

One of the first companies reaping the benefits of RET’s deep ties to the real estate industry is SmartRent, the startup providing a property analytics and automation platform for multifamily property managers and renters. Today, SmartRent announced it had closed $5 million in series A financing, with seed investor RET providing the entire round. 

SmartRent essentially provides property managers with many of the smart home capabilities that have primarily been offered to consumers to date, making it easier for them to monitor units remotely, avoid costly damages and streamline operations, all while hopefully enhancing the resident experience through all-in-one home controls.

By combining connected devices with its web and mobile platform, SmartRent hopes to provide tools that can help identify leaks or faulty equipment, eliminate energy waste and provide remote access control for door locks. The functions provided by SmartRent are particularly valuable when managing vacant units, in which leaks or unnecessary energy consumption can often go unnoticed, leading to multimillion-dollar damage claims or inflated utility bills. SmartRent also attempts to enhance the leasing process for vacant units by pre-screening potential renters that apply online and allowing qualified applicants to view the unit on their own without a third-party sales agent.

Just like RET, SmartRent is the brainchild of accomplished real estate industry vets. Founder and CEO Lucas Haldeman was still the CTO of Colony Starwood’s single-family portfolio when he first rolled out an early version of the platform in around 26,000 homes. Haldeman quickly realized how powerful the software was for property managers and decided to leave his C-suite position at the publicly traded REIT to found SmartRent.

According to RET, the strong industry pedigree of the founding team was one of the main drivers behind its initial investment in SmartRent and is one of the main differentiators between the company and its competitors.

With RET providing access to its leading multifamily owner LPs, SmartRent has been able to execute on a strong growth trajectory so far, with the company on pace to complete 15,000 installations by the end of the year and an additional 35,000 apartments committed for 2019. And SmartRent seems to have a long runway ahead. The platform can be implemented in any type of rental property, from retrofit homes to high rises, and has only penetrated a small portion of the nearly one million units owned by RET’s LPs alone.

SmartRent has now raised $10 million to date and hopes to use this latest round of funding to ramp growth by broadening its sales and marketing efforts. Longer-term, SmartRent hopes to permeate throughout the entire multifamily industry while continuing to improve and iterate on its platform.

“We’re so early on and we’ve made great progress, but we want to make deep penetration into this industry,” said Haldeman. “There are millions of apartment units and we want to be over 100,000 by year one, and over a million units by year three. At the same time, we’re continuing to enhance our offering and we’re focused on growing and expanding.”

As for RET Ventures, the firm hopes the compelling value proposition of its deep LP and industry network can help RET become the go-to venture firm startups looking to disrupt the real estate rental sector.

Nov
12
2018
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Vista snaps up Apptio for $1.94B, as enterprise companies remain hot

It seems that Sunday has become a popular day to announce large deals involving enterprise companies. IBM announced the $34 billion Red Hat deal two weeks ago. SAP announced its intent to buy Qualtrics for $8 billion last night, and Vista Equity Partners got into the act too, announcing a deal to buy Apptio for $1.94 billion, representing a 53 percent premium for stockholders.

Vista paid $38 per share for Apptio, a Seattle company that helps companies manage and understand their cloud spending inside a hybrid IT environment that has assets on-prem and in the cloud. The company was founded in 2007 right as the cloud was beginning to take off, and grew as the cloud did. It recognized that companies would have trouble understanding their cloud assets along side on-prem ones. It turned out to be a company in the right place at the right time with the right idea.

Investors like Andreessen Horowitz, Greylock and Madrona certainly liked the concept, showering the company with $261 million before it went public in 2016. The stock price has been up and down since, peaking in August at $41.23 a share before dropping down to $24.85 on Friday. The $38 a share Vista paid comes close to the high water mark for the stock.

Stock Chart: Google

Sunny Gupta, co-founder and CEO at Apptio liked the idea of giving his shareholders a good return while providing a good landing spot to take his company private. Vista has a reputation for continuing to invest in the companies it acquires and that prospect clearly excited him. “Vista’s investment and deep expertise in growing world-class SaaS businesses and the flexibility we will have as a private company will help us accelerate our growth…,” Gupta said in a statement.

The deal was approved by Apptio’s board of directors, which will recommend shareholders accept it. With such a high premium, it’s hard to imagine them turning it down. If it passes all of the regulatory hurdles, the acquisition is expected to close in Q1 2019.

It’s worth noting that the company has a 30-day “go shop” provision, which would allow it to look for a better price. Given how hot the enterprise market is right now and how popular hybrid cloud tools are, it is possible it could find another buyer, but it could be hard to find one willing to pay such a high premium.

Vista clearly likes to buy enterprise tech companies having snagged Ping Identity for $600 million and Marketo for $1.8 billion in 2016. It grabbed Jamf, an Apple enterprise device management company and Datto, a disaster recovery company last year. It turned Marketo around for $4.75 billion in a deal with Adobe just two months ago.

Oct
18
2018
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Seva snares $2.4M seed investment to find info across cloud services

Seva, a New York City startup, that wants to help customers find content wherever it lives across SaaS products, announced a $2.4 million seed round today. Avalon Ventures led the round with participation from Studio VC and Datadog founder and CEO Olivier Pomel.

Company founder and CEO Sanjay Jain says that he started this company because he felt the frustration personally of having to hunt across different cloud services to find the information he was looking for. When he began researching the idea for the company, he found others who also complained about this fragmentation.

“Our fundamental vision is to change the way that knowledge workers acquire the information they need to do their jobs from one where they have to spend a ton of time actually seeking it out to one where the Seva platform can prescribe the right information at the right time when and where the knowledge worker actually needs it, regardless of where it lives.”

Seva, which is currently in Beta, certainly isn’t the first company to try to solve this issue. Jain believes that with a modern application of AI and machine learning and single sign-on, Seva can provide a much more user-centric approach than past solutions simply because the technology wasn’t there yet.

The way they do this is by looking across the different information types. Today they support a range of products including Gmail, Google Calendar, Google Drive,, Box, Dropbox, Slack and JIRA, Confluence. Jain says they will be adding additional services over time.

Screenshot: Seva

Customers can link Seva to these products by simply selecting one and entering the user credentials. Seva inherits all of the security and permissioning applied to each of the services, so when it begins pulling information from different sources, it doesn’t violate any internal permissioning in the process.

Jain says once connected to these services, Seva can then start making logical connections between information wherever it lives. A salesperson might have an appointment with a customer in his or her calendar, information about the customer in a CRM and a training video related to the customer visit. It can deliver all of this information as a package, which users can share with one another within the platform, giving it a collaborative element.

Seva currently has 6 employees, but with the new funding is looking to hire a couple of more engineers to add to the team. Jain hopes the money will be a bridge to a Series A round at the end of next year by which time the product will be generally available.

Oct
15
2018
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Celonis brings intelligent process automation software to cloud

Celonis has been helping companies analyze and improve their internal processes using machine learning. Today the company announced it was providing that same solution as a cloud service with a few nifty improvements you won’t find on prem.

The new approach, called Celonis Intelligent Business Cloud, allows customers to analyze a workflow, find inefficiencies and offer improvements very quickly. Companies typically follow a workflow that has developed over time and very rarely think about why it developed the way it did, or how to fix it. If they do, it usually involves bringing in consultants to help. Celonis puts software and machine learning to bear on the problem.

Co-founder and CEO Alexander Rinke says that his company deals with massive volumes of data and moving all of that to the cloud makes sense. “With Intelligent Business Cloud, we will unlock that [on prem data], bring it to the cloud in a very efficient infrastructure and provide much more value on top of it,” he told TechCrunch.

The idea is to speed up the whole ingestion process, allowing a company to see the inefficiencies in their business processes very quickly. Rinke says it starts with ingesting data from sources such as Salesforce or SAP and then creating a visual view of the process flow. There may be hundreds of variants from the main process workflow, but you can see which ones would give you the most value to change, based on the number of times the variation occurs.

Screenshot: Celonis

By packaging the Celonis tools as a cloud service, they are reducing the complexity of running and managing it. They are also introducing an app store with over 300 pre-packaged options for popular products like Salesforce and ServiceNow and popular process like order to cash. This should also help get customers up and running much more quickly.

New Celonis App Store. Screenshot: Celonis

The cloud service also includes an Action Engine, which Rinke describes as a big step toward moving Celonis from being purely analytical to operational. “Action Engine focuses on changing and improving processes. It gives workers concrete info on what to do next. For example in process analysis, it would notice on time delivery isn’t great because order to cash is to slow. It helps accelerate changes in system configuration,” he explained.

Celonis Action Engine. Screenshot: Celonis

The new cloud service is available today. Celonis was founded in 2011. It has raised over $77 million. The most recent round was a $50 million Series B on a valuation over $1 billion.

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