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
11
2018
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InVision, valued at $1.9 billion, picks up $115 million Series F

“The screen is becoming the most important place in the world,” says InVision CEO and founder Clark Valberg . In fact, it’s hard to get through a conversation with him without hearing it. And, considering that his company has grown to $100 million in annual recurring revenue, he has reason to believe his own affirmation.

InVision, the startup looking to be the Salesforce of design, has officially achieved unicorn status with the close of a $115 million Series F round, bringing the company’s total funding to $350 million. This deal values InVision at $1.9 billion, which is nearly double its valuation as of mid-2017 on the heels of its $100 million Series E financing.

Spark Capital led the round, with participation from Goldman Sachs, as well as existing investors Battery Ventures, ICONIQ Capital, Tiger Global Management, FirstMark and Geodesic Capital. Atlassian also participated in the round. Earlier this year, Atlassian and InVision built out much deeper integrations, allowing Jira, Confluence and Trello users to instantly collaborate via InVision.

As part of the deal, Spark Capital’s Megan Quinn will be joining the board alongside existing board members and observers Amish Jani, Lee Fixel, Matthew Jacobson, Mike Kourey, Neeraj Agrawal, Vas Natarajan and Daniel Wolfson.

InVision started in 2011 as a simple prototyping tool. It let designers build out their experience without asking the engineering/dev team to actually build it, to then send to the engineering and product and marketing and executive teams for collaboration and/or approval.

Over the years, the company has stretched its efforts both up and downstream in the process, building out a full collaboration suite called InVision Cloud, so that every member of the organization can be involved in the design process; Studio, a design platform meant to take on the likes of Adobe and Sketch; and InVision Design System Manager, where design teams can manage their assets and best practices from one place.

But perhaps more impressive than InVision’s ability to build design products for designers is its ability to attract users that aren’t designers.

“Originally, I don’t think we appreciated how much the freemium model acted as a flywheel internally within an organization,” said Quinn. “Those designers weren’t just inviting designers from their own team or other teams, but PMs and Marketing and Customer Service and executives to collaborate and approve the designs. From the outside, InVision looks like a design company. But really, they start with the designer as a core customer and spread virally within an organization to serve a multitude.”

InVision has simply dominated prototyping and collaboration, today announcing it has surpassed 5 million users. What’s more, InVision has a wide variety of customers. The startup has a long and impressive list of digital-first customers — including Netflix, Uber, Airbnb and Twitter — but also serves 97 percent of the Fortune 100, with customers like Adidas, General Electric, NASA, IKEA, Starbucks and Toyota.

Part of that can be attributed to the quality of the products, but the fundamental shift to digital (as predicted by Valberg) is most certainly under way. Whether brands like it or not, customers are interacting with them more and more from behind a screen, and digital customer experience is becoming more and more important to all companies.

In fact, a McKinsey study showed that companies that are in the top quartile scores of the McKinsey Design Index outperformed their counterparts in both revenues and total returns to shareholders by as much as a factor of two.

But as with any transition, some folks are averse to change. Valberg identifies industry education and evangelism as two big challenges for InVision.

“Organizations are not quick to change on things like design, which is why we’ve built out a Design Transformation Team,” said Valberg. “The team goes in and gets hands on with brands to help them with new practices and to achieve design maturity within the organization.”

With a fresh $115 million and 5 million users, InVision has just about everything it needs to step into a new tier of competition. Even amongst behemoths like Adobe, which pulled in $2.29 billion in revenue in Q3 alone, InVision has provided products that can both complement and compete.

But Quinn believes the future of InVision rests on execution.

“As with most companies, the biggest challenge will be continued excellence in execution,” said Quinn. “InVision has all the right tail winds with the right team, a great product and excellent customers. It’s all about building and executing ahead of where the pack is going.”

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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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
06
2018
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LeanIX, the SaaS that lets enterprises map out their software architecture, closes $30M Series C

LeanIX, the Software-as-a-Service for “Enterprise Architecture Management,” has closed $30 million in Series C funding.

The round is led by Insight Venture Partners, with participation from previous investors Deutsche Telekom Capital Partners (DTCP), Capnamic Ventures and Iris Capital. It brings LeanIX’s total funding to nearly $40 million since the German company was founded in 2012.

Operating in the enterprise architecture space, previously the domain of a company’s IT team only, LeanIX’s SaaS might well be described as a “Google Maps for IT architectures.”

The software lets enterprises map out all of the legacy software or modern SaaS that the organisation is run on, including creating meta data on things like what business process it is used for or capable of supporting, what tech (and version) powers it, what teams are using or have access to it, who is responsible for it, as well as how the different architecture fits together.

From this vantage point, enterprises can not only keep a better handle on all of the software from different vendors they are buying in, including how that differs or might be better utilised across distributed teams, but also act in a more nimble way in terms of how they adopt new solutions or decommission legacy ones.

In a call with André Christ, co-founder and CEO, he described LeanIX as providing a “single source of truth” for an enterprise’s architecture. He also explained that the SaaS takes a semi-automatic approach to how it maps out that data. A lot of the initial data entry will need to be done manually, but this is designed to be done collaboratively across an organisation and supported by an “easy-to-use UX,” while LeanIX also extracts some data automatically via integrations with ServiceNow (e.g. scanning software on servers) or Signavio (e.g. how IT Systems are used in Business Processes).

More broadly, Christ tells me that the need for a solution like LeanIX is only increasing, as enterprise architecture has shifted away from monolithic vendors and software to the use of a sprawling array of cloud or on-premise software where each typically does one job or business process really well, rather than many.

“With the rising adoption of SaaS, multi-cloud and microservices, an agile management of the Enterprise Architecture is harder to achieve but more important than ever before,” he says. “Any company in any industry using more than a hundred applications is facing this challenge. That’s why the opportunity is huge for LeanIX to define and own this category.”

To that end, LeanIX says the investment will be used to accelerate growth in the U.S. and for continued product innovation. Meanwhile, the company says that in 2018 it achieved several major milestones, including doubling its global customer base, launching operations in Boston and expanding its global headcount with the appointment of several senior-level executives. Enterprises using LeanIX include Adidas, DHL, Merck and Santander, with strategic partnerships with Deloitte, ServiceNow and PwC, among others.

“For businesses today, effective enterprise architecture management is critical for driving digital transformation, and requires robust tools that enable collaboration and agility,” said Teddie Wardi, principal at Insight Venture Partners, in a statement. “LeanIX is a pioneer in the space of next-generation EA tools, achieved staggering growth over the last year, and is the trusted partner for some of today’s largest and most complex organizations. We look forward to supporting its continued growth and success as one of the world’s leading software solutions for the modernization of IT architectures.”

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.”

Dec
04
2018
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FortressIQ raises $12M to bring new AI twist to process automation

FortressIQ, a startup that wants to bring a new kind of artificial intelligence to process automation called imitation learning, emerged from stealth this morning and announced it has raised $12 million.

The Series A investment came entirely from a single venture capital firm, Light Speed Venture Partners. Today’s funding comes on top of $4 million in seed capital the company raised previously from Boldstart Ventures, Comcast Ventures and Eniac Ventures.

Pankaj Chowdhry, founder and CEO of FortressIQ, says that his company basically replaces high-cost consultants who are paid to do time and motion studies and automates that process in a fairly creative way. It’s a bit like Robotics Process Automation (RPA), a space that is attracting a lot of investment right now, but instead of simply recording what’s happening on the desktop, and reproducing that digitally, it takes it a step further in a process called “imitation learning.”

“We want to be able to replicate human behavior through observation. We’re targeting this idea of how can we help people understand their processes. But imitation learning is I think the most interesting area of artificial intelligence because it focuses not on what AI can do, but how can AI learn and adapt,” he explained

They start by capturing a low-bandwidth movie of the process. “So we build virtual processors. And basically the idea is we have an agent that gets deployed by your enterprise IT group, and it integrates into the video card,” Chowdhry explained.

He points out that it’s not actually using a camera, but it captures everything going on, as a person interacts with a Windows desktop. In that regard it’s similar to RPA. “The next component is our AI models and computer vision. And we build these models that can literally watch the movie and transcribe the movie into what we call a series of software interactions,” he said.

Another key differentiator here is that they have built a data mining component on top of this, so if the person in the movie is doing something like booking an invoice, and stops to check email or Slack, FortressIQ can understand when an activity isn’t part of the process and filters that out automatically.

The product will be offered as a cloud service. Chowdhry’s previous company, Third Pillar Systems, was acquired by Genpact in 2013.

Dec
04
2018
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Fivetran announces $15M Series A to build automated data pipelines

Fivetran, a startup that builds automated data pipelines between data repositories and cloud data warehouses and analytics tools, announced a $15 million Series A investment led by Matrix Partners.

Fivetran helps move data from source repositories like Salesforce and NetSuite to data warehouses like Snowflake or analytics tools like Looker. Company CEO and co-founder George Fraser says the automation is the key differentiator here between his company and competitors like Informatica and SnapLogic.

“What makes Fivetran different is that it’s an automated data pipeline to basically connect all your sources. You can access your data warehouse, and all of the data just appears and gets kept updated automatically,” Fraser explained. While he acknowledges that there is a great deal of complexity behind the scenes to drive that automation, he stresses that his company is hiding that complexity from the customer.

The company launched out of Y Combinator in 2012, and other than $4 million in seed funding along the way, it has relied solely on revenue up until now. That’s a rather refreshing approach to running an enterprise startup, which typically requires piles of cash to build out sales and marketing organizations to compete with the big guys they are trying to unseat.

One of the key reasons they’ve been able to take this approach has been the company’s partner strategy. Having the ability to get data into another company’s solution with a minimum of fuss and expense has attracted data-hungry applications. In addition to the previously mentioned Snowflake and Looker, the company counts Google BigQuery, Microsoft Azure, Amazon Redshift, Tableau, Periscope Data, Salesforce, NetSuite and PostgreSQL as partners.

Ilya Sukhar, general partner at Matrix Partners, who will be joining the Fivetran board under the terms of deal sees a lot of potential here. “We’ve gone from companies talking about the move to the cloud to preparing to execute their plans, and the most sophisticated are making Fivetran, along with cloud data warehouses and modern analysis tools, the backbone of their analytical infrastructure,” Sukhar said in a statement.

They currently have 100 employees spread out across four offices in Oakland, Denver, Bangalore and Dublin. They boast 500 customers using their product including Square, WeWork, Vice Media and Lime Scooters, among others.

Dec
04
2018
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Forethought scores $9M Series A in wake of Battlefield win

It’s been a whirlwind few months for Forethought, a startup with a new way of looking at enterprise search that relies on artificial intelligence. In September, the company took home the TechCrunch Disrupt Battlefield trophy in San Francisco, and today it announced a $9 million Series A investment.

It’s pretty easy to connect the dots between the two events. CEO and co-founder Deon Nicholas said they’ve seen a strong uptick in interest since the win. “Thanks to TechCrunch Disrupt, we have had a lot of things going on including a bunch of new customer interest, but the biggest news is that we’ve raised our $9 million Series A round,” he told TechCrunch.

The investment was led by NEA with K9 Ventures, Village Global and several angel investors also participating. The angel crew includes Front CEO Mathilde Collin, Robinhood CEO Vlad Tenev and Learnvest CEO Alexa von Tobel.

Forethought aims to change conventional enterprise search by shifting from the old keyword kind of approach to using artificial intelligence underpinnings to retrieve the correct information from a corpus of documents.

“We don’t work on keywords. You can ask questions without keywords and using synonyms to help understand what you actually mean, we can actually pull out the correct answer [from the content] and deliver it to you,” Nicholas told TechCrunch in September.

He points out that it’s still early days for the company. It had been in stealth for a year before launching at TechCrunch Disrupt in September. Since the event, the three co-founders have brought on six additional employees and they will be looking to hire more in the next year, especially around machine learning and product and UX design.

At launch, they could be embedded in Salesforce and Zendesk, but are looking to expand beyond that.

The company is concentrating on customer service for starters, but with the new money in hand, it intends to begin looking at other areas in the enterprise that could benefit from a smart information retrieval system. “We believe that this can expand beyond customer support to general information retrieval in the enterprise,” Nicholas said.

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