Posted by Richy George on 2 July, 2018

Dell, which went private in one of the the largest leveraged buyouts in tech circa 2013, announced today that it will once again be going public through a relatively complex mechanism that will once again bring the company back onto the public markets with founder Michael Dell and Silver Lake Partners largely in control.

Dell’s leveraged buyout largely marked the final page in the company’s storied history as a PC provider, going back to the old “dude, you’re getting a Dell” commercials. The company rode that wave to dominance, but as computing shifted to laptops, mobile phones, and complex operations were offloaded into cloud services like Amazon Web Services, Azure and Google Cloud, Dell found itself navigating a complex environment while having to make a significant business transition beyond the PC era. That meant Dell would be beholden to the whims of public markets, perhaps laden with short-term pessimism over the company’s urgent need to find a transition.

The transaction is actually an offer to buy shares that track the company’s involvement in VMWare, converting that tracking stock into Dell Technologies stock that would mark its return as a publicly-traded company. Those shares will end up traded on the NYSE, around five years later after its founder took the company private with Silver Lake Partners in a deal worth roughly $25 billion. Silver Lake Partners owns around 24% of the company, while Dell owns 72% and will continue to serve as the chairman and CEO of the company. This move helps the company bypass the IPO process, which would remove the whole time period of potential investors scrutinizing the company (which has taken on a substantial debt load).

Dell said in its most recent quarter it recorded revenue of $21.4 billion, up 19% year-over-year, and over the past 12 months the company generated $82.4 billion of revenue with a net loss of $2.3 billion. The company said it has also paid down $13 billion of gross debt since its combination with EMC back in 2016. All this has been part of the company’s transition to find new businesses beyond just selling computers, though there’s clearly still demand for those computers in offices around the world. As it has expanded into a broader provider of IT services, it’s potentially positioned itself as a modern enterprise tools provider, which would allow it to more securely navigate public markets while offering investors a way to correctly calibrate its value.

Posted by Richy George on 29 June, 2018

Say you have a job with a large company and you want to know how much vacation time you have left, or how to add your new baby to your healthcare. This usually involves emailing or calling HR and waiting for an answer, or it could even involve crossing multiple systems to get what you need.

Leena AI, a member of the Y Combinator Summer 2018 class, wants to change that by building HR bots to answer question for employees instantly.

The bots can be integrated into Slack or Workplace by Facebook and they are built and trained using information in policy documents and by pulling data from various back-end systems like Oracle and SAP.

Adit Jain, co-founder at Leena AI says the company has its roots in another startup called Chatteron, that the founders started after they got out of college in India in 2015. That product helped people build their own chatbots. Jain says along the way, they discovered while doing their market research, a particularly strong need in HR. They started Leena AI last year to address that specific requirement.

Jain says when building bots, the team learned through its experience with Chatteron, that it’s better to concentrate on a single subject because the underlying machine learning model gets better the more it’s used. “Once you create a bot, for it to really to add value and be [extremely] accurate, and for it to really go deep, it takes a lot of time and effort and that can only happen through verticalization,” Jain explained.

Photo: Leena AI

What’s more, as the founders have become more knowledgeable about the needs of HR, they have learned that 80 percent of the questions cover similar topics like vacation, sick time and expense reporting. They have also seen companies using similar back-end systems, so they can now build standard integrators for common applications like SAP, Oracle and Netsuite.

Of course, even though people may ask similar questions, the company may have unique terminology or people may ask the question in an unusual way. Jain says that’s where the natural language processing (NLP) comes in. The system can learn these variations over time as they build a larger database of possible queries.

The company just launched in 2017 and already has a dozen paying customers. They hope to double that number in just 60 days. Jain believes being part of Y Combinator should help in that regard. The partners are helping the team refine its pitch and making introductions to companies that could make use of this tool.

Their ultimate goal is nothing less than to be ubiquitous, to help bridge multiple legacy systems to provide answers seamlessly for employees to all their questions. If they can achieve that, they should be a successful company.

Posted by Richy George on 28 June, 2018

Regardless of what you may think of Facebook as a platform, they run a massive operation and when you reach their level of scale you have to get more creative in how you handle every aspect of your computing environment.

Engineers quickly reach the limits of human ability to track information to the point that checking logs and analytics becomes impractical and unwieldy on a system running thousands of services. This is a perfect scenario to implement machine learning and that is precisely what Facebook has done.

The company published a blog post today about a self-tuning system they have dubbed Sprial. This is pretty nifty and what it does is essentially flip the idea of system tuning on its head. Instead of looking at some data and coding what you want the system to do, you teach the system the right way to do it and it does it for you, using the massive stream of data to continually teach the machine learning models how to push the systems to be ever better.

In the blog post, the Spiral team described it this way: “Instead of looking at charts and logs produced by the system to verify correct and efficient operation, engineers now express what it means for a system to operate correctly and efficiently in code. Today, rather than specify how to compute correct responses to requests, our engineers encode the means of providing feedback to a self-tuning system.”

They say that coding in this way is akin to declarative code, like using SQL statements to tell the database what you want it to do with the data, but the act of applying that concept to systems is not a simple matter.

“Spiral uses machine learning to create data-driven and reactive heuristics for resource-constrained real-time services. The system allows for much faster development and hands-free maintenance of those services, compared with the hand-coded alternative,” the Spiral team wrote in the blog post.

If you consider the sheer number of services running on Facebook, and the number of users trying to interact with those services at any given time, it required sophisticated automation, and that is what Spiral is providing.

The system takes the log data, processes it through Spiral, which is connected with just a few lines of code. It then sends commands back to the server based on the declarative coding statements written by the team. To ensure those commands are always being fine tuned, at the same time, the data gets sent from the server to a model  for further adjustment in a lovely virtuous cycle. This process can applied locally or globally.

The tool was developed by the team operating in Boston, and is only available internally inside Facebook. It took lots of engineering to make it happen, the kind of scope that only Facebook could apply to a problem like this (mostly because Facebook is one of the few companies that would actually have a problem like this).

Posted by Richy George on 27 June, 2018

Microsoft today launched two new Azure regions in China. These new regions, China North 2 in Beijing and China East 2 in Shanghai, are now generally available and will complement the existing two regions Microsoft operates in the country (with the help of its local partner, 21Vianet).

As the first international cloud provider in China when it launched its first region there in 2014, Microsoft has seen rapid growth in the region and there is clearly demand for its services there. Unsurprisingly, many of Microsoft’s customers in China are other multinationals that are already betting on Azure for their cloud strategy. These include the likes of Adobe, Coke, Costco, Daimler, Ford, Nuance, P&G, Toyota and BMW.

In addition to the new China regions, Microsoft also today launched a new availability zone for its region in the Netherlands. While availability zones have long been standard among the big cloud providers, Azure only launched this feature — which divides a region into multiple independent zones — into general availability earlier this year. The regions in the Netherlands, Paris and Iowa now offer this additional safeguard against downtime, with others to follow soon.

In other Azure news, Microsoft also today announced that Azure IoT Edge is now generally available. In addition, Microsoft announced the second generation of its Azure Data Lake Storage service, which is now in preview, and some updates to the Azure Data Factory, which now includes a web-based user interface for building and managing data pipelines.

Posted by Richy George on 27 June, 2018

For any company built on top of machine learning operations, the more data they have, the better they are off — as long as they can keep it all under control. But as more and more information pours in from disparate sources, gets logged in obscure databases and is generally hard (or slow) to query, the process of getting that all into one neat place where a data scientist can actually start running the statistics is quickly running into one of machine learning’s biggest bottlenecks.

That’s a problem Intermix.io and its founders, Paul Lappas and Lars Kamp, hope to solve. Engineers get a granular look at all of the different nuances behind what’s happening with some specific function, from the query all the way through all of the paths it’s taking to get to its end result. The end product is one that helps data engineers monitor the flow of information going through their systems, regardless of the source, to isolate bottlenecks early and see where processes are breaking down. The company also said it has raised seed funding from Uncork Capital, S28 Capital, PAUA Ventures along with Bastian Lehman, CEO of Postmates, and Hasso Plattner, Founder of SAP.

“Companies realize being data driven is a key to success,” Kamp said. “The cloud makes it cheap and easy to store your data forever, machine learning libraries are making things easy to digest. But a company that wants to be data driven wants to hire a data scientist. This is the wrong first hire. To do that they need access to all the relevant data, and have it be complete and clean. That falls to data engineers who need to build data assembly lines where they are creating meaningful types to get data usable to the data scientist. That’s who we serve.”

Intermix.io works in a couple of ways: first, it tags all of that data, giving the service a meta-layer of understanding what does what, and where it goes; second, it taps every input in order to gather metrics on performance and help identify those potential bottlenecks; and lastly, it’s able to track that performance all the way from the query to the thing that ends up on a dashboard somewhere. The idea here is that if, say, some server is about to run out of space somewhere or is showing some performance degradation, that’s going to start showing up in the performance of the actual operations pretty quickly — and needs to be addressed.

All of this is an efficiency play that might not seem to make sense at a smaller scale. the waterfall of new devices that come online every day, as well as more and more ways of understanding how people use tools online, even the smallest companies can quickly start building massive data sets. And if that company’s business depends on some machine learning happening in the background, that means it’s dependent on all that training and tracking happening as quickly and smoothly as possible, with any hiccups leading to real-term repercussions for its own business.

Intermix.io isn’t the first company to try to create some application performance management software. There are others like Data Dog and New Relic, though Lappas says that the primary competition from them comes in the form of traditional APM software with some additional scripts tacked on. However, data flows are a different layer altogether, which means they require a more unique and custom approach to addressing that problem.

Posted by Richy George on 27 June, 2018

The rapid pace of technology innovation and applications in recent decades — you could argue that just about every kind of business is a “tech” business these days — has spawned a sea of tech startups and larger businesses that are focused on serving that market, and equally demanding consumers, on a daily basis. Today, a venture capital firm in the UK is announcing a fund aimed at helping to grow the technologies that will underpin a lot of those daily applications.

Cambridge-based IQ Capital is raising £125 million ($165 million) that it will use specifically to back UK startups that are building “deep tech” — the layer of research and development, and potentially commercialised technology, that is considered foundational to how a lot of technology will work in the years and decades to come. So far, some £92 million has been secured, and partner Kerry Baldwin said that the rest is coming “without question” — pointing to strong demand.

There was a time when it was more challenging to raise money for very early stage companies working at the cusp of new technologies, even more so in smaller tech ecosystems like the UK’s. As Ed Stacey, another partner in the firm acknowledges, there is often a very high risk of failure at even more stages of the process, with the tech in some cases not even fully developed, let alone rolled out to see what kind of commercial interest there might be in the product.

However, there has been a clear shift in the last several years.

There a lot more money floating around in tech these days — so much so that it’s created a stronger demand for projects to invest in. (Another consequence of that is that when you do get a promising startup, funds are potentially giving them hundreds of millions and causing other disruptions in how they grow and exit, which is another story…)

And while there are definitely a lot of startups out there in the world today, a lot of them are what you might describe as “me too”, or at least making something that is easily replicated by another startup, making the returns and the wins harder to find among them.

A new focus that we are seeing on “deep tech” is a consequence of both of those trends.

“The low-hanging fruit has been discovered… Shallow tech is a solved problem,” Stacey said, in reference to areas like the basics of e-commerce services and mobile apps. “These are easy to build with open source components, for example. It’s shallow when it can be copied very quickly.”

In contrast, deep tech is “by definition is something that can’t easily be copied,” he continued. “The underlying algorithm is deep, with computational complexity.”

But the challenges run deep in deep tech: not only might a product or technology never come together, or find a customer, but it might face problems scaling if it does take off. IQ Capital’s focus on deep tech is coupled with the company trying to  determine which ideas will scale, not just work or find a customer. As we see more deep tech companies emerging and growing, I’m guessing scalability will become an ever more prominent factor in deciding whether a startup gets backing.

IQ Capital’s investments to date span areas like security (Privitar), marketing tech (Grapeshot, which was acquired by Oracle earlier this year), AI (such as speech recognition API developer Speechmatics) and biotechnology (Fluidic Analytics, which measures protein concentrations), all areas that will be the focus of this fund, along with IoT and other emerging technologies and gaps in the current market.

IQ Capital is not the only fund starting to focus on deep tech, nor is its portfolio the only range of startups focusing on this (Allegro.AI and deep-learning chipmaker Hailo are others, to name just two).

LPs in this latest fund include family offices, wealth managers, tech entrepreneurs and CEOs from IQ’s previous investments, as well as British Business Investments, the commercial arm of the British Business Bank, the firm said.

Posted by Richy George on 26 June, 2018

Google is giving developers a new storage option in its cloud. Cloud Filestore, which will launch into beta next month, essentially offers a fully managed network attached storage (NAS) service in the cloud. This means that companies can now easily run applications that need a traditional file system interface on the Google Cloud Platform.

Traditionally, developers who wanted access to a standard file system over the kind of object storage and database options that Google already offered had to rig up a file server with a persistent disk. Filestore does away with all of this and simply allows Google Cloud users to spin up storage as needed.

The promise of Filestore is that it offers high throughput, low latency and high IOPS. The service will come in two tiers: premium and standard. The premium tier will cost $0.30 per GB and month and promises a throughput speed of 700 MB/s and 30,000 IOPS, no matter the storage capacity. Standard tier Filestore storage will cost $0.20 per GB and month, but performance scales with capacity and doesn’t hit peak performance until you store more than 10TB of data in Filestore.

Google launched Filestore at an event in Los Angeles that mostly focused on the entertainment and media industry. There are plenty of enterprise applications in those verticals that need a shared filesystem, but the same can be said for many other industries that rely on similar enterprise applications.

The Filestore beta will launch next month. Since it’s still in beta, Google isn’t making any uptime promises right now and there is no ETA for when the service will come out of beta.

Posted by Richy George on 26 June, 2018

ZenProspect, a startup that emerged from the Y Combinator Winter 2016 class to help companies use data and intelligence to increase sales, announced today that it was rebranding as Apollo. It also announced a $7 million Series A investment.

The round was led by Nexus Venture Partners. Social Capital and Y Combinator also participated. Apparently Y Combinator liked what they saw enough to continue to invest in the company.

Apollo helps customers connect their sales people with the right person at the right time. That is typically a customer that is most likely to buy the product. It does this by combining a number of tools including a rules engine to automate prospect routing, a lead scoring tool and analytics to measure results at a granular level, among others.

The company also uses data they have collected from 200 million contacts at 10 million companies to match sellers to buyers along with the information in the user’s own CRM tools — typically Salesforce. Apollo is making this vast database of company and contact data available for customers to use themselves for free starting today.

Apollo CEO and founder Tim Zheng says the company was born out of a need at a previous venture. He was working at a startup that was floundering and sales had flatlined. When they couldn’t find a product on the market to help them, they decided to build it and saw the number of users increase from 5000 to 150,000 users in just five weeks. That eventually reached a million users.  As he spoke to friends at other Bay area companies about what his company had done, he heard a lot of interest, and decided to turn that sales tool into a company.

The company launched as ZenProspect in 2015 and went through Y Combinator in 2016. They were the third fastest growing company in that YC batch, generating $1 million in annual recurring revenue (ARR) during their tenure. In fact, they were profitable out of the gate, using their own software to sell the product.

Zheng points out that there are thousands of sales tools out there, but he said, even if you bought every one of them and stitched them together you still wouldn’t have a great sales process. Zheng says his company has figured out how to solve that problem and provide that structure to deliver the best prospects to sales people to close deals.

The company works closely with Salesforce as 80 percent of its customers are using data inside of Salesforce in conjunction with the Apollo tool. It’s worth noting, however, that Apollo is not built on top of Salesforce platform. It just integrates with it.

They target both early stage startups looking to increase sales and established enterprise customers with huge sales teams. So far it’s been working. Today, Apollo has 500 customers and 50 employees. With the current influx of money, they expect to get to 120 in the next 12 -18 months.

Posted by Richy George on 26 June, 2018

If a coffee fanatic decides they want to open up a coffee shop somewhere, odds are they’ll have to end up Googling “liability insurance” at some point — and trying to navigate the complex legal web to get all of that nailed down before they even sell their first iced latte.

Inaki Berenguer instead hopes they’ll stumble upon CoverWallet in that Google search, which streamlines the process of setting up commercial insurance for a small business. The company is trying to take another step now by saying it will create an open-ended tool that allows third parties to plug directly into its services, giving small businesses a way to pick up commercial insurance while they are going through the flow of another set of SMB management software. All of this is geared toward ensuring that more and more users are able to start tapping the service, which allows it to pick up additional business — and data — even if it means partially handing off the branding and user experience to another service.

“When we had three employees and we moved to New York, we were told, if you want to sign a lease you have to buy insurance.” Berenguer said. “I wanted to go to a website, and input my square footage, and my revenue, and get a quote, and do everything else in five to ten minutes — but I was told that didn’t exist for business insurance. I had to go to a general provider, complete a 20-page PDF, which the broker sends it to the insurance company, and then they’ll come back with a quote. This process is analog and time consuming and opaque. I know this process can be reinvented. There are 25m small businesses in the U.S., and they all need to buy insurance.”

CoverWallet is much like what Berenguer explained in his dream scenario when he was moving his last company into an office. The insurance policies are personalized for restaurants, startups, retail stores, contractors, or various other types of commercial insurance products. Users input their business information, and then are able to pay for the policies — up front or in monthly installments — and get their policy set up in short order. If that doesn’t work, CoverWallet also has a team of agents to cover the rest of the questions they have, and users can modify any of those policies whenever they want.

But in the end, it may be that users are looking to keep things simple – especially if it’s a small- to medium-sized business that isn’t the kind of technically savvy ones you’ll often find in a major metropolitan area like New York or San Francisco. While CoverWallet looks to simplify the whole process of getting commercial insurance, which can be a major roadblock to getting something as simple as a coffee shop off the ground, integrating into other tools and making the whole process more and more seamless ensures that it’ll be able to keep that flow of businesses coming in — and those businesses may eventually start to spread the word on their own.

“Businesses might already be using accounting software or payroll,” Berenguer said. “Those systems have all the company info. Why do they need to come to a platform, and type everything, when that info is somewhere else. It’s like white labeling your solution. But if you want to be customer centric, the less they have to type the better.”

There likely isn’t much stopping the larger insurance carriers from offering a similar sort of plug-and-play API. But Berenguer said building a whole aggregation across all of those insurance providers, and then giving that pipeline to customers as they look to pick up insurance through another SMB tool like Gusto (though Gusto isn’t one of the clients, Berenguer said), gives them enough of a compelling argument for those employment suites to bring them in. Certain providers may only offer certain kinds of policies, or cover certain geographic regions, and CoverWallet hopes it will make a good enough case that it can cover all those gaps.

Posted by Richy George on 26 June, 2018

In the age of digital transformation, it’s important to understand your business processes and find improvements quickly, but it’s not always easy to do without bringing in expensive consultants to help. Celonis, a New York City enterprise startup, created a sophisticated software solution to help solve this problem, and today it announced a $50 million Series B investment from Accel and 83North on a $1 billion valuation.

It’s not typical for an enterprise startup to have such a lofty valuation so early in its funding cycle, but Celonis is not a typical enterprise startup. It launched in 2011 in Munich with this idea of helping companies understand their processes, which they call process mining.

“Celonis is an intelligent system using logs created by IT systems such as SAP, Salesforce, Oracle and Netsuite, and automatically understands how these processes work and then recommends intelligently how they can be improved,” Celonis CEO and co-founder Alexander Rinke explained.

The software isn’t magic, but helps customers visualize each business process, and then looks at different ways of shifting how and where humans interact with the process or bringing in technology like robotics process automation (RPA) when it makes sense.

Celonis process flow. Photo: Celonis

Rinke says the software doesn’t simply find a solution and that’s the end of the story. It’s a continuous process loop of searching for ways to help customers operate more efficiently. This doesn’t have to be a big change, but often involves lots incremental ones.

“We tell them there are lots of answers. We don’t think there is one solution. All these little things don’t execute well. We point out these things. Typically we find it’s easy to implement, ” he said.

Screenshot: Celonis

It seems to be working. Customers include the likes of Exxon-Mobile, 3M, Merck, Lockheed-Martin and Uber. Rinke reports deals are often seven figures. The company has grown an astonishing 5,000 percent in the past 4 years and 300 percent in the past year alone. What’s more, it has been profitable every year since it started. (How many enterprise startups can say that?)

The company currently has 400 employees, but unlike most Series B investments, they aren’t looking at this money to grow operationally. They wanted to have the money for strategic purposes, so if the opportunity came along to make an acquisition or expand into a new market, they would be in a position to do that.

“I see the funding as a confirmation and commitment, a sign from our investors and an indicator about what we’ve built and the traction we have. But for us it’s more important, and our investors share this, what they really invested in was the future of the company,” Rinke said. He’s sees an on-going commitment to help his customers as far more important than a billion valuation.

But that doesn’t hurt either as it moves rapidly forward.