Jan
08
2019
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Managed by Q ends 2018 with a fresh $25 million in funding

Managed by Q, the office management platform that launched back in 2013, has today revealed that it raised an additional $25 million as a part of its Series C, led by existing investors RRE and Google Ventures, with participation from new investors DivCo West, Oxford Properties and others. The fresh capital brings the total round to $55 million.

Managed by Q launched as an all-encompassing platform for office management, offering IT support, supply inventory management, cleaning and equipment repair. Since, the company has added a full-fledged marketplace, allowing office managers to choose vendors for various needs around the office.

But for 2019, the company is focused on tools and services.

“We want to spend 2019 putting even greater focus on the tools used by our vendors and workplace management teams, like task management tools,” said co-founder and CEO Dan Teran . “We want to build the first set of collaboration tools for the workplace team, the same way that designers use InVision and engineers use GitHub and salespeople use Salesforce. Something purposely built for the workplace team.”

Teran described tools that would allow for employee requests, work orders, task management, inventory management and budgeting to all live on the same platform.

The company hasn’t shared much by way of revenue or customer growth, but Teran told TechCrunch that the marketplace business has been doubling since it launched and is on track to continue on that trajectory. He also wrote in a company blog post that Managed by Q’s top five vendor partners have done more than $1 million in business on the Managed by Q platform, and more than 30 partners will have earned over $100,000 on the platform in 2018.

The NY-based startup also brokered a partnership with Staples to provide office supplies to clients, and acquired Hivy and NVS to further fill out their office management suite of products.

Managed by Q has raised a total of $128.25 million, according to Crunchbase.

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
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
12
2018
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Tigera raises $30M Series B for its Kubernetes security and compliance platform

Tigera, a startup that offers security and compliance solutions for Kubernetes container deployments, today announced that it has raised a $30 million Series B round led by Insight Venture Partners. Existing investors Madrona, NEA and Wing also participated in this round.

Like everybody in the Kubernetes ecosystem, Tigera is exhibiting at KubeCon this week, so I caught up with the team to talk about the state of the company and its plans for this new raise.

“We are in a very exciting position,” Tigera president and CEO Ratan Tipirneni told me. “All the four public cloud players [AWS, Microsoft Azure, Google Cloud and IBM Cloud] have adopted us for their public Kubernetes service. The large Kubernetes distros like Red Hat and Docker are using us.” In addition, the team has signed up other enterprises, often in the healthcare and financial industry, and SaaS players (all of which it isn’t allowed to name) that use its service directly.

The company says that it didn’t need to raise right now. “We didn’t need the money right now, but we had a lot of incoming interest,” Tipirneni said. The company will use the funding to expand its engineering, marketing and customer success teams. In total, it plans to quadruple its sales force. In addition, it plans to set up a large office in Vancouver, Canada, mostly because of the availability of talent there.

In the legacy IT world, security and compliance solutions could rely on the knowledge that the underlying infrastructure was relatively stable. Now, though, with the advent of containers and DevOps, workloads are highly dynamic, but that also makes the challenge of securing them and ensuring compliance with regulations like HIPAA or standards like PCI more complex, too. The promise of Tigera’s solution is that it allows enterprises to ensure compliance by using a zero-trust model that authorizes each service on the network, encrypts all the traffic and enforces the policies the admins have set for their company and needs. All of this data is logged in detail and, if necessary, enterprises can pull it for incident management or forensic analysis. 

Dec
12
2018
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AtScale lands $50 million investment led by Morgan Stanley

AtScale, the startup that helps companies move massive amounts of data into business intelligence and analytics tools, announced a $50 million Series D round today.

Morgan Stanley led the round, with previous investors Storm Ventures and Atlantic Bridge joining in. New investor Wells Fargo also participated. The funding comes almost exactly a year after the company announced its $25 million Series C. Today’s funding brings the total amount raised to $120 million.

Bringing on an institutional investor like Morgan Stanley is often a signal that the company has reached the stage where it is at least beginning to think about the possibility of going public at some point in the future. AtScale CEO Chris Lynch acknowledged such a connection without making any broad commitment (as you would expect). “We are not close to being IPO-ready, but that was a future consideration in selecting Morgan Stanley,” Lynch told TechCrunch.

What the company does is help take big data and move it into tools where customers can make better use of it. AtScale co-founder Dave Mariani used to be at Yahoo where he helped pioneer the use of big data in the 2009/2010 timeframe. Unfortunately, systems at the time couldn’t deal with the volume of data — and that is still a problem, one that AtScale says it is designed to solve. “We take a bunch of data silos and put a semantic layer across the data platforms and expose them in a consistent way,” Mariani told TechCrunch last year at the time of the Series C round. This allows a company to get a big picture view of their data, rather than consuming it in smaller chunks.

AtScale reported a banner year, bringing on 50 new customers across their target verticals of retail, financial services, advertising and digital sales. These include Rakuten, Dell Technologies, TD Bank and Toyota. What’s more, the company stretched out this year, taking advantage of the last funding round to expand more into international markets in Europe and Asia.

The company was founded in 2013 and is based in San Mateo, California.

Dec
11
2018
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TechSee nabs $16M for its customer support solution built on computer vision and AR

Chatbots and other AI-based tools have firmly found footing in the world of customer service, used either to augment or completely replace the role of a human responding to questions and complaints, or (sometimes, annoyingly, at the same time as the previous two functions) sell more products to users.

Today, an Israeli startup called TechSee is announcing $16 million in funding to help build out its own twist on that innovation: an AI-based video service, which uses computer vision, augmented reality and a customer’s own smartphone camera to provide tech support to customers, either alongside assistance from live agents, or as part of a standalone customer service “bot.”

Led by Scale Venture Partners — the storied investor that has been behind some of the bigger enterprise plays of the last several years (including Box, Chef, Cloudhealth, DataStax, Demandbase, DocuSign, ExactTarget, HubSpot, JFrog and fellow Israeli AI assistance startup WalkMe), the Series B also includes participation from Planven Investments, OurCrowd, Comdata Group and Salesforce Ventures. (Salesforce was actually announced as a backer in October.)

The funding will be used both to expand the company’s current business as well as move into new product areas like sales.

Eitan Cohen, the CEO and co-founder, said that the company today provides tools to some 15,000 customer service agents and counts companies like Samsung and Vodafone among its customers across verticals like financial services, tech, telecoms and insurance.

The potential opportunity is big: Cohen estimates there are about 2 million customer service agents in the U.S., and about 14 million globally.

TechSee is not disclosing its valuation. It has raised around $23 million to date.

While TechSee provides support for software and apps, its sweet spot up to now has been providing video-based assistance to customers calling with questions about the long tail of hardware out in the world, used for example in a broadband home Wi-Fi service.

In fact, Cohen said he came up with the idea for the service when his parents phoned him up to help them get their cable service back up, and he found himself challenged to do it without being able to see the set-top box to talk them through what to do.

So he thought about all the how-to videos that are on platforms like YouTube and decided there was an opportunity to harness that in a more organised way for the companies providing an increasing array of kit that may never get the vlogger treatment.

“We are trying to bring that YouTube experience for all hardware,” he said in an interview.

The thinking is that this will become a bigger opportunity over time as more services get digitised, the cost of components continues to come down and everything becomes “hardware.”

“Tech may become more of a commodity, but customer service does not,” he added. “Solutions like ours allow companies to provide low-cost technology without having to hire more people to solve issues [that might arise with it.]”

The product today is sold along two main trajectories: assisting customer reps; and providing unmanned video assistance to replace some of the easier and more common questions that get asked.

In cases where live video support is provided, the customer opts in for the service, similar to how she or he might for a support service that “takes over” the device in question to diagnose and try to fix an issue. Here, the camera for the service becomes a customer’s own phone.

Over time, that live assistance is used in two ways that are directly linked to TechSee’s artificial intelligence play. First, it helps to build up TechSee’s larger back catalogue of videos, where all identifying characteristics are removed with the focus solely on the device or problem in question. Second, the experience in the video is also used to build TechSee’s algorithms for future interactions. Cohen said there are now “millions” of media files — images and videos — in the company’s catalogue.

The effectiveness of its system so far has been pretty impressive. TechSee’s customers — the companies running the customer support — say they have on average seen a 40 percent increase in customer satisfaction (NPS scores), a 17 percent decrease in technician dispatches and between 20 and 30 percent increase in first-call resolutions, depending on the industry.

TechSee is not the only company that has built a video-based customer engagement platform: others include Stryng, CallVU and Vee24. And you could imagine companies like Amazon — which is already dabbling in providing advice to customers based on what its Echo Look can see — might be interested in providing such services to users across the millions of products that it sells, as well as provide that as a service to third parties.

According to Cohen, what TechSee has going for it compared to those startups, and also the potential entry of companies like Microsoft or Amazon into the mix, is a head start on raw data and a vision of how it will be used by the startup’s AI to build the business.

“We believe that anyone who wants to build this would have a challenge making it from scratch,” he said. “This is where we have strong content, millions of images, down to specific model numbers, where we can provide assistance and instructions on the spot.”

Salesforce’s interest in the company, he said, is a natural progression of where that data and customer relationship can take a business beyond responsive support into areas like quick warranty verification (for all those times people have neglected to do a product registration), snapping fender benders for insurance claims and of course upselling to other products and services.

“Salesforce sees the synergies between the sales cloud and the service cloud,” Cohen said.

“TechSee recognized the great potential for combining computer vision AI with augmented reality in customer engagement,” said Andy Vitus, partner at Scale Venture Partners, who joins the board with this round. “Electronic devices become more complex with every generation, making their adoption a perennial challenge. TechSee is solving a massive problem for brands with a technology solution that simplifies the customer experience via visual and interactive guidance.”

Dec
06
2018
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Contentful raises $33.5M for its headless CMS platform

Contentful, a Berlin- and San Francisco-based startup that provides content management infrastructure for companies like Spotify, Nike, Lyft and others, today announced that it has raised a $33.5 million Series D funding round led by Sapphire Ventures, with participation from OMERS Ventures and Salesforce Ventures, as well as existing investors General Catalyst, Benchmark, Balderton Capital and Hercules. In total, the company has now raised $78.3 million.

It’s been less than a year since the company raised its Series C round and, as Contentful co-founder and CEO Sascha Konietzke told me, the company didn’t really need to raise right now. “We had just raised our last round about a year ago. We still had plenty of cash in our bank account and we didn’t need to raise as of now,” said Konietzke. “But we saw a lot of economic uncertainty, so we thought it might be a good moment in time to recharge. And at the same time, we already had some interesting conversations ongoing with Sapphire [formerly SAP Ventures] and Salesforce. So we saw the opportunity to add more funding and also start getting into a tight relationship with both of these players.”

The original plan for Contentful was to focus almost explicitly on mobile. As it turns out, though, the company’s customers also wanted to use the service to handle its web-based applications and these days, Contentful happily supports both. “What we’re seeing is that everything is becoming an application,” he told me. “We started with native mobile application, but even the websites nowadays are often an application.”

In its early days, Contentful focused only on developers. Now, however, that’s changing, and having these connections to large enterprise players like SAP and Salesforce surely isn’t going to hurt the company as it looks to bring on larger enterprise accounts.

Currently, the company’s focus is very much on Europe and North America, which account for about 80 percent of its customers. For now, Contentful plans to continue to focus on these regions, though it obviously supports customers anywhere in the world.

Contentful only exists as a hosted platform. As of now, the company doesn’t have any plans for offering a self-hosted version, though Konietzke noted that he does occasionally get requests for this.

What the company is planning to do in the near future, though, is to enable more integrations with existing enterprise tools. “Customers are asking for deeper integrations into their enterprise stack,” Konietzke said. “And that’s what we’re beginning to focus on and where we’re building a lot of capabilities around that.” In addition, support for GraphQL and an expanded rich text editing experience is coming up. The company also recently launched a new editing experience.

Dec
05
2018
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Workato raises $25M for its integration platform

Workato, a startup that offers an integration and automation platform for businesses that competes with the likes of MuleSoft, SnapLogic and Microsoft’s Logic Apps, today announced that it has raised a $25 million Series B funding round from Battery Ventures, Storm Ventures, ServiceNow and Workday Ventures. Combined with its previous rounds, the company has now received investments from some of the largest SaaS players, including Salesforce, which participated in an earlier round.

At its core, Workato’s service isn’t that different from other integration services (you can think of them as IFTTT for the enterprise), in that it helps you to connect disparate systems and services, set up triggers to kick off certain actions (if somebody signs a contract on DocuSign, send a message to Slack and create an invoice). Like its competitors, it connects to virtually any SaaS tool that a company would use, no matter whether that’s Marketo and Salesforce, or Slack and Twitter. And like some of its competitors, all of this can be done with a drag-and-drop interface.

What’s different, Workato founder and CEO Vijay Tella tells me, is that the service was built for business users, not IT admins. “Other enterprise integration platforms require people who are technical to build and manage them,” he said. “With the explosion in SaaS with lines of business buying them — the IT team gets backlogged with the various integration needs. Further, they are not able to handle all the workflow automation needs that businesses require to streamline and innovate on the operations.”

Battery Ventures’ general partner Neeraj Agrawal also echoed this. “As we’ve all seen, the number of SaaS applications run by companies is growing at a very rapid clip,” he said. “This has created a huge need to engage team members with less technical skill-sets in integrating all these applications. These types of users are closer to the actual business workflows that are ripe for automation, and we found Workato’s ability to empower everyday business users super compelling.”

Tella also stressed that Workato makes extensive use of AI/ML to make building integrations and automations easier. The company calls this Recipe Q. “Leveraging the tens of billions of events processed, hundreds of millions of metadata elements inspected and hundreds of thousands of automations that people have built on our platform — we leverage ML to guide users to build the most effective integration/automation by recommending next steps as they build these automations,” he explained. “It recommends the next set of actions to take, fields to map, auto-validates mappings, etc. The great thing with this is that as people build more automations — it learns from them and continues to make the automation smarter.”

The AI/ML system also handles errors and offers features like sentiment analysis to analyze emails and detect their intent, with the ability to route them depending on the results of that analysis.

As part of today’s announcement, the company is also launching a new AI-enabled feature: Automation Editions for sales, marketing and HR (with editions for finance and support coming in the future). The idea here is to give those departments a kit with pre-built workflows that helps them to get started with the service without having to bring in IT.

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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WeWork picks up ANOTHER $3B from SoftBank

WeWork has picked up another $3 billion in financing from SoftBank Corp, not to be confused with SoftBank Vision Fund. The deal comes in the form of a warrant, allowing SoftBank to pay $3 billion for the opportunity to buy shares before September 2019 at a price of $110 or higher, ultimately valuing WeWork at $42 billion minimum.

In August, SoftBank Corp invested $1 billion in WeWork in the form of a convertible note.

According to the Financial Times, SoftBank will pay WeWork $1.5 billion on January 15, 2019 and another $1.5 billion on April 15.

SoftBank is far and away WeWork’s biggest investor, with SoftBank Vision Fund having poured $4.4 billion into the company just last year.

The real estate play out of WeWork is just one facet of the company’s strategy.

More than physical land, WeWork wants to be the central connective tissue for work in general. The company often strikes deals with major service providers at “whole sale” prices by negotiating on behalf of its 300,000 members. Plus, WeWork has developed enterprise products for large corporations, such as Microsoft, who tend to sign longer, more lucrative leases. In fact, these types of deals make up 29 percent of WeWork’s revenue.

The biggest issue is whether or not WeWork can sustain its outrageous growth, which seems to have been the key to its soaring valuation. After all, WeWork hasn’t yet achieved profitability.

Can the vision become a reality? SoftBank seems willing to bet on it.

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