Private equity firms can offer enterprise startups a viable exit option

Four years ago, Ping Identity was at a crossroads. A venerable player in the single sign-on market, its product was not a market leader, and after 14 years and $128 million in venture capital, it needed to find a new path.

While the company had once discussed an IPO, by 2016 it began putting out feelers for buyers. Vista Equity Partners made a $600 million offer and promised to keep building the company, something that corporate buyers wouldn’t guarantee. Ping CEO and co-founder Andre Durand accepted Vista’s offer, seeing it as a way to pay off his investors and employees and exit the right way. Even better, his company wasn’t subsumed into a large entity as likely would have happened with a typical M&A transaction.

As it turned out, the IPO-or-acquisition question wasn’t an either/or proposition. Vista continued to invest in the company, using small acquisitions like UnboundID and Elastic Beam to fill in its roadmap, and Ping went public last year. The company’s experience shows that private equity offers a reasonable way for mature enterprise startups with decent but not exceptional growth — like the 100% or more venture firms tend to favor — to exit, pay off investors, reward employees and still keep building the company.

But not everyone that goes this route has a tidy outcome like Ping’s. Some companies get brought into the P/E universe where they replace the executive team, endure big layoffs or sell off profitable pieces and stop investing in the product. But the three private equity firms we spoke to — Vista Equity, Thoma Bravo and Scaleworks — all wanted to see their acquisitions succeed, even if they each go about it differently.

Viable companies with good numbers


By Ron Miller

Application security platform NeuraLegion raises $4.7 million seed led by DNX Ventures

A video call group photo of NeuraLegion's team working remotely around the world

A video call group photo of NeuraLegion’s team working remotely around the world

Application security platform NeuraLegion announced today it has raised a $4.7 million seed round led by DNX Ventures, an enterprise-focused investment firm. The funding included participation from Fusion Fund, J-Ventures and Incubate Fund. The startup also announced the launch of a new self-serve, community version that allows developers to sign up on their own for the platform and start performing scans within a few minutes.

Based in Tel Aviv, Israel, NeuraLegion also has offices in San Francisco, London, and Mostar, Bosnia. It currently offers NexDAST for dynamic application security testing, and NexPLOIT to integrate application security into SDLC (software development life-cycle). It was launched last year by a founding team that includes chief executive Shoham Cohen, chief technology officer Bar Hofesh, chief scientist Art Linkov, and president and chief commercial officer Gadi Bashvitz.

When asked who NeuraLegion views as its closest competitors, Bashvitz said Invicti Security and WhiteHat Security. Both are known primarily for their static application security testing (SAST) solutions, which Bashvitz said complements DAST products like NeuraLegion’s.

“These are complementary solutions and in fact we have some information partnerships with some of these companies,” he said.

Where NeuraLegion differentiates from other application security solutions, however, is that it was created for specifically for developers, quality assurance and DevOps workers, so even though it can also be used by security professionals, it allows scans to be run much earlier in the development process than usual while lowering costs.

Bashvitz added that NeuraLegion is now used by thousands of developers through their organizations, but it is releasing its self-serve, community product to make its solutions more accessible to developers, who can sign up on their own, run their first scans and get results within fifteen minutes.

In a statement about the funding, DNX Ventures managing partner Hiro Rio Maeda said, “The DAST market has been long stalled without any innovative approaches. NeuraLegion’s next-generation platform introduces a new way of conducting robust testing in today’s modern CI/CD environment.”


By Catherine Shu

Atlassian Smarts adds machine learning layer across the company’s platform of services

Atlassian has been offering collaboration tools, often favored by developers and IT for some time with such stalwarts as Jira for help desk tickets, Confluence to organize your work and BitBucket to organize your development deliverables, but what it lacked was machine learning layer across the platform to help users work smarter within and across the applications in the Atlassian family.

That changed today, when Atlassian announced it has been building that machine learning layer called Atlassian Smarts, and is releasing several tools that take advantage of it. It’s worth noting that unlike Salesforce, which calls its intelligence layer Einstein or Adobe, which calls its Sensei; Atlassian chose to forgo the cutesy marketing terms and just let the technology stand on its own.

Shihab Hamid, the founder of the Smarts and Machine Learning Team at Atlassian, who has been with the company 14 years, says that they avoided a marketing name by design. “I think one of the things that we’re trying to focus on is actually the user experience and so rather than packaging or branding the technology, we’re really about optimizing teamwork,” Hamid told TechCrunch.

Hamid says that the goal of the machine learning layer is to remove the complexity involved with organizing people and information across the platform.

“Simple tasks like finding the right person or the right document becomes a challenge, or at least they slow down productivity and take time away from the creative high-value work that everyone wants to be doing, and teamwork itself is super messy and collaboration is complicated. These are human challenges that don’t really have one right solution,” he said.

He says that Atlassian has decided to solve these problems using machine learning with the goal of speeding up repetitive, time-intensive tasks. Much like Adobe or Salesforce, Atlassian has built this underlying layer of machine smarts, for lack of a better term, that can be distributed across their platform to deliver this kind of machine learning-based functionality wherever it makes sense for the particular product or service.

“We’ve invested in building this functionality directly into the Atlassian platform to bring together IT and development teams to unify work, so the Atlassian flagship products like JIRA and Confluence sit on top of this common platform and benefit from that common functionality across products. And so the idea is if we can build that common predictive capability at the platform layer we can actually proliferate smarts and benefit from the data that we gather across our products,” Hamid said.

The first pieces fit into this vision. For starters, Atlassian is offering a smart search tool that helps users find content across Atlassian tools faster by understanding who you are and how you work. “So by knowing where users work and what they work on, we’re able to proactively provide access to the right documents and accelerate work,” he said.

The second piece is more about collaboration and building teams with the best personnel for a given task. A new tool called predictive user mentions helps Jira and Confluence users find the right people for the job.

“What we’ve done with the Atlassian platform is actually baked in that intelligence, because we know what you work on and who you collaborate with, so we can predict who should be involved and brought into the conversation,” Hamid explained.

Finally, the company announced a tool specifically for Jira users, which bundles together similar sets of help requests and that should lead to faster resolution over doing them manually one at a time.

“We’re soon launching a feature in JIRA Service Desk that allows users to cluster similar tickets together, and operate on them to accelerate IT workflows, and this is done in the background using ML techniques to calculate the similarity of tickets, based on the summary and description, and so on.”

All of this was made possible by the company’s previous shift  from mostly on-premises to the cloud and the flexibility that gave them to build new tooling that crosses the entire platform.

Today’s announcements are just the start of what Atlassian hopes will be a slew of new machine learning-fueled features being added to the platform in the coming months and years.


By Ron Miller

Dataloop raises $11M Series A round for its AI data management platform

Dataloop, a Tel Aviv-based startup that specializes in helping businesses manage the entire data lifecycle for their AI projects, including helping them annotate their datasets, today announced that it has now raised a total of $16 million. This includes a $5 seed round that was previously unreported, as well as an $11 million Series A round that recently closed.

The Series A round was led by Amiti Ventures with participation from F2 Venture Capital, crowdfunding platform OurCrowd, NextLeap Ventures and SeedIL Ventures.

“Many organizations continue to struggle with moving their AI and ML projects into production as a result of data labeling limitations and a lack of real time validation that can only be achieved with human input into the system,” said Dataloop CEO Eran Shlomo. “With this investment, we are committed, along with our partners, to overcoming these roadblocks and providing next generation data management tools that will transform the AI industry and meet the rising demand for innovation in global markets.”

Image Credits: Dataloop

For the most part, Dataloop specializes in helping businesses manage and annotate their visual data. It’s agnostic to the vertical its customers are in, but we’re talking about anything from robotics and drones to retail and autonomous driving.

The platform itself centers around the ‘humans in the loop’ model that complements the automated systems with the ability for humans to train and correct the model as needed. It combines the hosted annotation platform with a Python SDK and REST API for developers, as well as a serverless Functions-as-a-Service environment that runs on top of a Kubernetes cluster for automating dataflows.

Image Credits: Dataloop

The company was founded in 2017. It’ll use the new funding to grow its presence in the U.S. and European markets, something that’s pretty standard for Israeli startups, and build out its engineering team as well.


By Frederic Lardinois

Vivun announces $18M Series A to keep growing pre-sales platform

Vivun’s co-founder and CEO, Matt Darrow used to run pre-sales at Zuora and he saw that pre-sales team members had a lot of insight into customers. He believed if he could capture that insight, it would turn into valuable data to be shared across the company. He launched Vivun to build upon that idea in 2018, and today the company announced an $18 million Series A.

Accel led the round with participation from existing investor Unusual Ventures. With today’s investment, Vivun has raised a total of $21 million, according to the company.

Darrow says that the company has caught the attention of investors because this is a unique product category and there has been a lot of demand for it. “It turns out that businesses of all sizes, startups and enterprises, are really craving a solution like Vivun, which is dedicated to pre-sales. It’s a big, expensive department, and there’s never been software for it before,” Darrow told TechCrunch.

He says that a couple of numbers stand out in the company’s first year in business. First of all, the startup grew annual recurring revenue (ARR) six fold (although he wouldn’t share specific numbers) and tripled the workforce growing from 10 to 30, all while doing business as an early stage startup in the midst of a pandemic.

Darrow said while the business has grown this year, he found smaller businesses in the pipeline were cutting back due to the impact of COVID’s, but larger businesses like Okta, Autodesk and Dell Secureworks have filled in nicely, and he says the product actually fits well in larger enterprise organizations.

“If we look at our value proposition and what we do, it increases exponentially with the size of the company. So the larger the team, the larger the silos are, the larger the organization is, the bigger the value of solving the problem for pre-sales becomes,” he said.

After going from a team of 10 to 30 employees in the last year, Darrow wants to double the head count to reach around 60 employees in the next year, fueled in part by the new investment dollars. As he builds the company, the founding team, which is made up of two men and two women, is focused on building a diverse and inclusive employee base.

“It is something that’s really important to us, and we’ve been working at it. Even as we went from 10 to 30, we’ve worked to pay close attention to [diversity and inclusion], and we continue to do so just as part of the culture of how we build the business,” he said.

He’s been having to build that workforce in the middle of COVID, but he says that even before the pandemic shut down offices, he and his founding partners were big on flexibility in terms of time spent in the office versus working from home. “We knew that for mental health strength and stability, that being in the office nine to five, five days a week wasn’t really a modern model that would cut it,” he said.

Even pre-COVID the company was offering two quiet periods a year to let people refresh their batteries. In the midst of COVID, he’s trying to give people Friday afternoons off to go out and exercise and relax their minds.

As the startup grows, those types of things may be harder to do, but it’s the kind of culture Darrow and his founding partners hope to continue to foster as they build the company.


By Ron Miller

Armory nabs $40M Series C as commercial biz on top of open source Spinnaker project takes off

As companies continue to shift more quickly to the cloud, pushed by the pandemic, startups like Armory that work in the cloud native space are seeing an uptick in interest. Armory is a company built to be commercial layer on top of the open source continuous delivery project Spinnaker. Today, it announced a $40 million Series C.

B Capital led the round with help from new investors Lead Edge Capital and Marc Benioff along with previous investors Insight Partners, Crosslink Capital, Bain Capital Ventures, Mango Capital, Y Combinator and Javelin Venture Partners. Today’s investment brings the total raised to more than $82 million.

“Spinnaker is an open source project that came out of Netflix and Google, and it is a very sophisticated multi-cloud and software delivery platform,” company co-founder and CEO Daniel R. Odio told TechCrunch.

Odio points out that this project has the backing of industry leaders including the three leading public cloud infrastructure vendors Amazon, Microsoft and Google, as well as other cloud players like CloudFoundry and HashiCorp. “The fact that there is a lot of open source community support for this project means that it is becoming the new standard for cloud native software delivery,” he said.

In the days before the notion of continuous delivery, companies moved forward slowly, releasing large updates over months or years. As software moved to the cloud, this approach no longer made sense and companies began delivering updates more incrementally adding features when they were ready. Adding a continuous delivery layer helped facilitate this move.

As Odio describes it, Armory extends the Spinnaker project to help implement complex use cases at large organizations including around compliance and governance and security. It is also in the early stages of implementing a SaaS version of the solution, which should be available next year.

While he didn’t want to discuss customer numbers, he mentioned JPMorgan Chase and Autodesk as customers along with less specific allusions to a “a Fortune Five technology company, a Fortune 20 Bank, a Fortune 50 retailer and a Fortune 100 technology company.

The company currently has 75 employees, but Odio says business has been booming and he plans to double the team in the next year. As he does, he says that he is deeply committed to diversity and inclusion.

“There’s actually a really big difference between diversity and inclusion, and there’s a great Vernā Myers quote that diversity is being asked to the party and inclusion is being asked to dance, and so it’s actually important for us not only to focus on diversity, but also focus on inclusion because that’s how we win. By having a heterogeneous company, we will outperform a homogeneous company,” he said.

While the company has moved to remote work during COVID, Odio says they intend to remain that way, even after the current crisis is over. “Now obviously COVID been a real challenge for the world including us. We’ve gone to a fully remote-first model, and we are going to stay remote first even after COVID. And it’s really important for us to be taking care of our people, so there’s a lot of human empathy here,” he said.

But at the same time, he sees COVID opening up businesses to move to the cloud and that represents an opportunity for his business, one that he will focus on with new capital at his disposal. “In terms of the business opportunity, we exist to help power the transformation that these enterprises are undergoing right now, and there’s a lot of urgency for us to execute on our vision and mission because there is a lot of demand for this right now,” he said.


By Ron Miller

Twilio is buying customer data startup Segment for between $3B and $4B

Sources have told TechCrunch that Twilio intends to acquire customer data startup Segment for between $3 and $4 billion. Forbes broke the story on Friday night, reporting a price tag of $3.2 billion.

We have heard from a couple of industry sources that the deal is in the works and could be announced as early as Monday.

Twilio and Segment are both API companies. That means they create an easy way for developers to tap into a specific type of functionality without writing a lot of code. As I wrote in a 2017 article on Segment, it provides a set of APIs to pull together customer data from a variety of sources:

Segment has made a name for itself by providing a set of APIs that enable it to gather data about a customer from a variety of sources like your CRM tool, customer service application and website and pull that all together into a single view of the customer, something that is the goal of every company in the customer information business.

While Twilio’s main focus since it launched in 2008 has been on making it easy to embed communications functionality into any app, it signaled a switch in direction when it released the Flex customer service API in March 2018. Later that same year, it bought SendGrid, an email marketing API company for $2 billion.

Twilio’s market cap as of Friday was an impressive $45 billion. You could see how it can afford to flex its financial muscles to combine Twilio’s core API mission, especially Flex, with the ability to pull customer data with Segment and create customized email or ads with SendGrid.

This could enable Twilio to expand beyond pure core communications capabilities and it could come at the cost of around $5 billion for the two companies, a good deal for what could turn out to be a substantial business as more and more companies look for ways to understand and communicate with their customers in more relevant ways across multiple channels.

As Semil Shah from early stage VC firm Haystack wrote in the company blog yesterday, Segment saw a different way to gather customer data, and Twilio was wise to swoop in and buy it.

Segment’s belief was that a traditional CRM wasn’t robust enough for the enterprise to properly manage its pipe. Segment entered to provide customer data infrastructure to offer a more unified experience. Now under the Twilio umbrella, Segment can continue to build key integrations (like they have for Twilio data), which is being used globally inside Fortune 500 companies already.

Segment was founded in 2011 and raised over $283 million, according to Crunchbase data. Its most recent raise was $175 million in April on a $1.5 billion valuation.

Twilio stock closed at $306.24 per share on Friday up $2.39%.

Segment declined to comment on this story. We also sent a request for comment to Twilio, but hadn’t heard back by the time we published.  If that changes, we will update the story.


By Ron Miller

How Roblox completely transformed its tech stack

Picture yourself in the role of CIO at Roblox in 2017.

At that point, the gaming platform and publishing system that launched in 2005 was growing fast, but its underlying technology was aging, consisting of a single data center in Chicago and a bunch of third-party partners, including AWS, all running bare metal (nonvirtualized) servers. At a time when users have precious little patience for outages, your uptime was just two nines, or less than 99% (five nines is considered optimal).

Unbelievably, Roblox was popular in spite of this, but the company’s leadership knew it couldn’t continue with performance like that, especially as it was rapidly gaining in popularity. The company needed to call in the technology cavalry, which is essentially what it did when it hired Dan Williams in 2017.

Williams has a history of solving these kinds of intractable infrastructure issues, with a background that includes a gig at Facebook between 2007 and 2011, where he worked on the technology to help the young social network scale to millions of users. Later, he worked at Dropbox, where he helped build a new internal network, leading the company’s move away from AWS, a major undertaking involving moving more than 500 petabytes of data.

When Roblox approached him in mid-2017, he jumped at the chance to take on another major infrastructure challenge. While they are still in the midst of the transition to a new modern tech stack today, we sat down with Williams to learn how he put the company on the road to a cloud-native, microservices-focused system with its own network of worldwide edge data centers.

Scoping the problem


By Ron Miller

Headroom, which uses AI to supercharge videoconferencing, raises $5M

Videoconferencing has become a cornerstone of how many of us work these days — so much so that one leading service, Zoom, has graduated into verb status because of how much it’s getting used.

But does that mean videoconferencing works as well as it should? Today, a new startup called Headroom is coming out of stealth, tapping into a battery of AI tools — computer vision, natural language processing and more — on the belief that the answer to that question is a clear — no bad WiFi interruption here — “no.”

Headroom not only hosts videoconferences, but then provides transcripts, summaries with highlights, gesture recognition, optimised video quality, and more, and today it’s announcing that it has raised a seed round of $5 million as it gears up to launch its freemium service into the world.

You can sign up to the waitlist to pilot it, and get other updates here.

The funding is coming from Anna Patterson of Gradient Ventures (Google’s AI venture fund); Evan Nisselson of LDV Capital (a specialist VC backing companies buidling visual technologies); Yahoo founder Jerry Yang, now of AME Cloud Ventures; Ash Patel of Morado Ventures; Anthony Goldbloom, the cofounder and CEO of Kaggle.com; and Serge Belongie, Cornell Tech associate dean and Professor of Computer Vision and Machine Learning.

It’s an interesting group of backers, but that might be because the founders themselves have a pretty illustrious background with years of experience using some of the most cutting-edge visual technologies to build other consumer and enterprise services.

Julian Green — a British transplant — was most recently at Google, where he ran the company’s computer vision products, including the Cloud Vision API that was launched under his watch. He came to Google by way of its acquisition of his previous startup Jetpac, which used deep learning and other AI tools to analyze photos to make travel recommendations. In a previous life, he was one of the co-founders of Houzz, another kind of platform that hinges on visual interactivity.

Russian-born Andrew Rabinovich, meanwhile, spent the last five years at Magic Leap, where he was the head of AI, and before that, the director of deep learning and the head of engineering. Before that, he too was at Google, as a software engineer specializing in computer vision and machine learning.

You might think that leaving their jobs to build an improved videoconferencing service was an opportunistic move, given the huge surge of use that the medium has had this year. Green, however, tells me that they came up with the idea and started building it at the end of 2019, when the term “Covid-19” didn’t even exist.

“But it certainly has made this a more interesting area,” he quipped, adding that it did make raising money significantly easier, too. (The round closed in July, he said.)

Given that Magic Leap had long been in limbo — AR and VR have proven to be incredibly tough to build businesses around, especially in the short- to medium-term, even for a startup with hundreds of millions of dollars in VC backing — and could have probably used some more interesting ideas to pivot to; and that Google is Google, with everything tech having an endpoint in Mountain View, it’s also curious that the pair decided to strike out on their own to build Headroom rather than pitch building the tech at their respective previous employers.

Green said the reasons were two-fold. The first has to do with the efficiency of building something when you are small. “I enjoy moving at startup speed,” he said.

And the second has to do with the challenges of building things on legacy platforms versus fresh, from the ground up.

“Google can do anything it wants,” he replied when I asked why he didn’t think of bringing these ideas to the team working on Meet (or Hangouts if you’re a non-business user). “But to run real-time AI on video conferencing, you need to build for that from the start. We started with that assumption,” he said.

All the same, the reasons why Headroom are interesting are also likely going to be the ones that will pose big challenges for it. The new ubiquity (and our present lives working at home) might make us more open to using video calling, but for better or worse, we’re all also now pretty used to what we already use. And for many companies, they’ve now paid up as premium users to one service or another, so they may be reluctant to try out new and less-tested platforms.

But as we’ve seen in tech so many times, sometimes it pays to be a late mover, and the early movers are not always the winners.

The first iteration of Headroom will include features that will automatically take transcripts of the whole conversation, with the ability to use the video replay to edit the transcript if something has gone awry; offer a summary of the key points that are made during the call; and identify gestures to help shift the conversation.

And Green tells me that they are already also working on features that will be added into future iterations. When the videoconference uses supplementary presentation materials, those can also be processed by the engine for highlights and transcription too.

And another feature will optimize the pixels that you see for much better video quality, which should come in especially handy when you or the person/people you are talking to are on poor connections.

“You can understand where and what the pixels are in a video conference and send the right ones,” he explained. “Most of what you see of me and my background is not changing, so those don’t need to be sent all the time.”

All of this taps into some of the more interesting aspects of sophisticated computer vision and natural language algorithms. Creating a summary, for example, relies on technology that is able to suss out not just what you are saying, but what are the most important parts of what you or someone else is saying.

And if you’ve ever been on a videocall and found it hard to make it clear you’ve wanted to say something, without straight-out interrupting the speaker, you’ll understand why gestures might be very useful.

But they can also come in handy if a speaker wants to know if he or she is losing the attention of the audience: the same tech that Headroom is using to detect gestures for people keen to speak up can also be used to detect when they are getting bored or annoyed and pass that information on to the person doing the talking.

“It’s about helping with EQ,” he said, with what I’m sure was a little bit of his tongue in his cheek, but then again we were on a Google Meet, and I may have misread that.

And that brings us to why Headroom is tapping into an interesting opportunity. At their best, when they work, tools like these not only supercharge videoconferences, but they have the potential to solve some of the problems you may have come up against in face-to-face meetings, too. Building software that actually might be better than the “real thing” is one way of making sure that it can have staying power beyond the demands of our current circumstances (which hopefully won’t be permanent circumstances).


By Ingrid Lunden

As IBM spins out legacy infrastructure management biz, CEO goes all in on the cloud

When IBM announced this morning that it was spinning out its legacy infrastructure services business, it was a clear signal that new CEO Arvand Krishna, who took the reins in April, was ready to fully commit his company to the cloud.

The move was a continuation of the strategy the company began to put in place when it bought Red Hat in 2018 for the princely sum of $34 billion. That purchase signaled a shift to a hybrid-cloud vision, where some of your infrastructure lives on-premises and some in the cloud — with Red Hat helping to manage it all.

Even as IBM moved deeper into the hybrid cloud strategy, Krishna saw the financial results like everyone else and recognized the need to focus more keenly on that approach. In its most recent earnings report overall IBM revenue was $18.1 billion, down 5.4% compared to the year-ago period. But if you broke out just IBM’s cloud and Red Hat revenue, you saw some more promising results: cloud revenue was up 30 percent to $6.3 billion, while Red Hat-derived revenue was up 17%.

Even more, cloud revenue for the trailing 12 months was $23.5 billion, up 20%.

You don’t need to be a financial genius to see where the company is headed. Krishna clearly saw that it was time to start moving on from the legacy side of IBM’s business, even if there would be some short-term pain involved in doing so. So the executive put his resources into (as they say) where the puck is going. Today’s news is a continuation of that effort.

The managed infrastructure services segment of IBM is a substantial business in its own right, but Krishna was promoted to CEO to clean house, taking over from Ginni Rometti to make hard decisions like this.

While its cloud business is growing, Synergy Research data has IBM public cloud market share mired in single digits with perhaps 4 or 5%. In fact, Alibaba has passed its market share, though both are small compared to the market leaders Amazon, Microsoft and Google.

Like Oracle, another legacy company trying to shift more to the cloud infrastructure business, IBM has a ways to go in its cloud evolution.

As with Oracle, IBM has been chasing the market leaders — Google at 9%, Microsoft 18% and AWS with 33% share of public cloud revenue (according to Synergy) — for years now without much change in its market share. What’s more, IBM competes directly with Microsoft and Google, which are also going after that hybrid cloud business with more success.

While IBM’s cloud revenue is growing, its market share needle is stuck and Krishna understands the need to focus. So, rather than continue to pour resources into the legacy side of IBM’s business, he has decided to spin out that part of the company, allowing more attention for the favored child, the hybrid cloud business.

It’s a sound strategy on paper, but it remains to be seen if it will have a material impact on IBM’s growth profile in the long run. He is betting that it will, but then what choice does he have?


By Ron Miller

Grid AI raises $18.6M Series A to help AI researchers and engineers bring their models to production

Grid AI, a startup founded by the inventor of the popular open-source PyTorch Lightning project, William Falcon, that aims to help machine learning engineers more efficiently, today announced that it has raised an $18.6 million Series A funding round, which closed earlier this summer. The round was led by Index Ventures, with participation from Bain Capital Ventures and firstminute. 

Falcon co-founded the company with Luis Capelo, who was previously the head of machine learning at Glossier. Unsurprisingly, the idea here is to take PyTorch Lightning, which launched about a year ago, and turn that into the core of Grid’s service. The main idea behind Lightning is to decouple the data science from the engineering.

The time argues that a few years ago, when data scientists tried to get started with deep learning, they didn’t always have the right expertise and it was hard for them to get everything right.

“Now the industry has an unhealthy aversion to deep learning because of this,” Falcon noted. “Lightning and Grid embed all those tricks into the workflow so you no longer need to be a PhD in AI nor [have] the resources of the major AI companies to get these things to work. This makes the opportunity cost of putting a simple model against a sophisticated neural network a few hours’ worth of effort instead of the months it used to take. When you use Lightning and Grid it’s hard to make mistakes. It’s like if you take a bad photo with your phone but we are the phone and make that photo look super professional AND teach you how to get there on your own.”

As Falcon noted, Grid is meant to help data scientists and other ML professionals “scale to match the workloads required for enterprise use cases.” Lightning itself can get them partially there, but Grid is meant to provide all of the services its users need to scale up their models to solve real-world problems.

What exactly that looks like isn’t quite clear yet, though. “Imagine you can find any GitHub repository out there. You get a local copy on your laptop and without making any code changes you spin up 400 GPUs on AWS — all from your laptop using either a web app or command-line-interface. That’s the Lightning “magic” applied to training and building models at scale,” Falcon said. “It is what we are already known for and has proven to be such a successful paradigm shift that all the other frameworks like Keras or TensorFlow, and companies have taken notice and have started to modify what they do to try to match what we do.”

The service is now in private beta.

With this new funding, Grid, which currently has 25 employees, plans to expand its team and strengthen its corporate offering via both Grid AI and through the open-source project. Falcon tells me that he aims to build a diverse team, not in the least because he himself is an immigrant, born in Venezuela, and a U.S. military veteran.

“I have first-hand knowledge of the extent that unethical AI can have,” he said. “As a result, we have approached hiring our current 25 employees across many backgrounds and experiences. We might be the first AI company that is not all the same Silicon Valley prototype tech-bro.”

“Lightning’s open-source traction piqued my interest when I first learned about it a year ago,” Index Ventures’ Sarah Cannon told me. “So intrigued in fact I remember rushing into a closet in Helsinki while at a conference to have the privacy needed to hear exactly what Will and Luis had built. I promptly called my colleague Bryan Offutt who met Will and Luis in SF and was impressed by the ‘elegance’ of their code. We swiftly decided to participate in their seed round, days later. We feel very privileged to be part of Grid’s journey. After investing in seed, we spent a significant amount with the team, and the more time we spent with them the more conviction we developed. Less than a year later and pre-launch, we knew we wanted to lead their Series A.”


By Frederic Lardinois

YC grad DigitalBrain snags $3.4M seed to streamline customer service tasks

Most startup founders have a tough road to their first round of funding, but the founders of Digital Brain had it a bit tougher than most. The two young founders survived by entering and winning hackathons to pay their rent and put on food on the table. One of the ideas they came up with at those hackathons was DigitalBrain, a layer that sits on top of customer service software like Zendesk to streamline tasks and ease the job of customer service agents.

They ended up in Y Combinator in the Summer 2020 class, and today the company announced a $3.4 million seed investment. This total includes $3 million raised this round, which closed in August, and previously unannounced investments of $250,000 in March from Unshackled Ventures and $150,000 from Y Combinator in May.

The round was led by Moxxie Ventures with help from Caffeinated Capital, Unshackled Ventures, Shrug Capital, Weekend Fund, Underscore VC and Scribble Ventures along with a slew of individual investors.

Company co-founder Kesava Kirupa Dinakaran says that after he and his partner Dmitry Dolgopolov met at hackathon in May 2019, they moved into a community house in San Francisco full of startup founders. They kept hearing from their housemates about the issues their companies faced with customer service as they began scaling. Like any good entrepreneur, they decided to build something to solve that problem.

“DigitalBrain is an external layer that sits on top of existing help desk software to actually help the support agents get through their tickets twice as fast, and we’re doing that by automating a lot of internal workflows, and giving them all the context and information they need to respond to each ticket making the experience of responding to these tickets significantly faster,” Dinakaran told TechCrunch.

What this means in practice is that customer service reps work in DigitalBrain to process their tickets, and as they come upon a problem such as canceling an order or reporting a bug, instead of traversing several systems to fix it, they chose the appropriate action in DigitalBrain, enter the required information, and the problem is resolved for them automatically.  In the case of a bug, it would file a Jira ticket with engineering. In the case of canceling an order, it would take all of the actions and update all of the records required by this request.

As Dinakaran points out they aren’t typical Silicon Valley startup founders. They are 20 year old immigrants from India and Russia respectively, who came to the U.S. with coding skills and a dream of building a company. “We are both outsiders to Silicon Valley. We didn’t go to college. We don’t come from families of means. We wanted to come here and build our initial network from ground up,” he said.

Eventually they met some folks through their housemates, who suggested that they apply to Y Combinator. “As we started to meet people that we met through our community house here, some of them were YC founders and they kept saying I think you guys will love the YC community, not just in terms of your ethos, but also just purely from a perspective of meeting new people and where you are,” he said.

He said while he and his co-founder have trouble wrapping their arms around a number like the amount they have in the bank now, considering it wasn’t that long ago that they struggling to meet expenses every month, they recognize this money buys them an opportunity to help start building a more substantial company.

“What we’re trying to do is really accelerate the development and building of what we’re doing. And we think if we push the gas pedal with the resources we’ve gotten, we’ll be able to accelerate bringing on the next couple of customers, and start onboarding some of the larger companies we’re interested in,” he said.


By Ron Miller

Slack introduces new features to ease messaging between business partners

Slack is holding its Frontiers conference this week — virtually like everyone else in 2020 — and it’s introducing some new features to make it easier to message between partners. At the same time, it’s talking about some experimental features that could appear in the platform at some point (or not).

Let’s start with some features to help communicate with partners outside of your company in a secure way. This is always a tough nut to crack whether it’s collaboration or file sharing or any of the things that trusted partners do when they are working closely together.

To help solve that, the company is creating the notion of trusted partners, and this has a few components. The first is Slack Connect DMs (direct messages), which allows users inside an organization to collaborate with anyone outside their company simply by sending an invite.

“You can now direct message anyone in the Slack ecosystem. That means that anyone that has a Slack license can connect to one another,” Ilan Frank, VP of product at Slack told TechCrunch. While the company is introducing the new capability this week, it won’t be widely available until next year as the company wants to make sure this is used for business purposes only in a secure and non-spammy way.

“We’re going to be focused on, before we make this widely available, a lot of different information privacy and security [components] to make sure that we account for things like spam and phishing attacks and all that. This should not be a LinkedIn or Facebook Messenger where anyone can connect with you. This is [going to focus on] business for business work,” Frank explained.

Slack is introducing a couple of concepts to help ensure that happens. For starters, it’s adding Verified Organizations, which works a bit like verified users on Twitter, to help ensure you are dealing with someone from an organization you trust and work with before you start exchanging information on Slack.

“So if someone connects to you through direct message or through a channel, before you even make that connection, [you can ensure] if they are [from] a verified Slack organization versus someone who has just signed up on the internet, and you have not heard them, don’t have a relationship with them and don’t know who they are,” Frank said.

The last piece is called Managed Connections, which lets Slack admins control which organizations and individuals can connect with people inside your organization on Slack in a streamlined manner, which helps ensure that the other two new features are used in a responsible way.

“Organizations have told us that they want to go even deeper into the granularity of control, and they want to have different policies by external organizations that they’re connected to,” he said. Managed Connections lets admins set policies around different types of relationships with outside organizations.

All of these new tools are being introduced this week, but will be released later this year or early next year.

Among the other things the company working on in is enabling customers to embed video or audio in a Slack channel, extending it beyond a pure text messaging tool. The company was careful to point out that these features are just experiments for now and may or may not end up in the product  in the future.


By Ron Miller

Okta adds new no-code workflows that use identity to trigger sales and marketing tasks

It seems that no-code is the tech watchword of the year. It refers to the ability to create something that normally would require a developer to code, and replace it with dragging and dropping components instead, putting the task in reach of much less technical business users. Today Okta announced new no-code workflows that provide a way to use identity as a trigger to launch a customer-centric workflow.

Okta co-founder and CEO Todd McKinnon says that the company has created a series of connectors to make it easier to connect identity to a workflow that includes sales and marketing tooling. This comes on the heels of the identity lifecycle workflows, the company introduced at the Oktane customer conference in April.

“For this release we are introducing customer identity workflows which are focused on the connectors for all the customer-specific systems, things like Salesforce and Marketo and all the customer-centric [applications] that you’d want to do with your customer identities. And you can imagine over time that we’re going to expose this to more and more areas that will cover every kind of scenario a company would want to use,” McKinnon told TechCrunch.

McKinnon says that last year the company introduced Platform Services, which pulled apart the various pieces of the platform and exposed them as individual services, which bigger company customers could tap into as needed. He says that this is an extension of that idea, but instead of having to get engineering talent to write complex code to tie the Okta service into say Salesforce, you can simply drag the Salesforce connector to your workflow.

As McKinnon describes this using early adopter MLB as an example, say someone downloads the MLB app, creates a log-in and signs in. At that point, if MLB marketing personnel wanted to connect to any applications outside of Okta, it would normally require leveraging some programming help to make it happen.

But with the new workflow tools, a marketing person can set up a workflow that checks the log-in for fraud, then sends the person’s information automatically into Salesforce to create a customer record, and also triggers a welcome email in Marketo — and all of this could be done automatically triggered by the customer sign up.

Okta workflows showing what happens when a person downloads and app and creates an identiy.

Image Credits: Okta

This functionality was made possible by the $52.5 million acquisition of Azuqua last year. As COO and co-founder Frederic Kerrest wrote in a blog post at the time of the acquisition (and we quoted in the article):

“With Okta and Azuqua, IT teams will be able to use pre-built connectors and logic to create streamlined identity processes and increase operational speed. And, product teams will be able to embed this technology in their own applications alongside Okta’s core authentication and user management technology to build…integrated customer experiences.”

And that’s precisely the kind of approach the company is delivering this week. For now, it’s available as an early adopter program, but as Okta works out the kinks, you can expect them to build on this and add other enterprise workflow connectors to the mix as it expands this vision, giving the company a way to move beyond pure identity management and connect to other parts of the organization.


By Ron Miller

Salesforce Ventures launches $100M Impact Fund to invest in cloud startups with social mission

When Salesforce Ventures launched the first $50 million Impact Fund in 2017, it wanted to invest not only in promising cloud businesses, but startups with a socially positive mission. Today, the company launched the second Impact Fund, this time doubling its initial investment with a new $100 million fund

The latest fund is also designed to help bring more investment into areas that the company feels needs to be emphasized as a corporate citizen beyond pure business goals including education and reskilling, climate action, diversity, equity and inclusion, and providing tech for nonprofits and foundations.

Suzanne DiBianca, Chief Impact Officer and EVP of Corporate Relations at Salesforce says the money is being put to work on some of the world’s most pressing social issues. “Now more than ever, we believe business can be a powerful platform for change. We must leverage technology and invest in innovative ideas to drive the long-term health and wellness of all citizens, enable equal access to education and fuel impactful climate action,” DiBianca said in a statement.

Brent Leary, founder and principal analyst at CRM Essentials says that this investment is consistent with their commitment to social issues. “This fits right in with Salesforce’s efforts on making business a force for change. They talk it, they walk it, and they invest in it,” Leary told TechCrunch.

Claudine Emeott, Director of Impact Investing, in a Q&A on the company website, said that as with the first fund, the company is looking for cloud companies with some ties to Salesforce that address these core social components and can have a positive impact on the world. While there is a social aspect to each company, it still follows a particular investment thesis related to cloud computing. Her goal is to have a portfolio of cloud startups by next year that are addressing the set of social needs the firm has laid out.

“I hope that [by next year] we have made numerous investments in companies that are addressing today’s concurrent crises, and I hope that we can point to their measurable impact on those crises. I hope that we can point to exciting new integrations between our portfolio companies and Salesforce to tackle these challenges together,” Emeott said.

Paul Greenberg, president of the 56 Group and author of CRM at the Speed of Light, says that while he doesn’t always agree with Salesforce on every matter, he admires their social bent. “As an analyst, I might battle with them on some of their products, the things they do in the market and their messaging, but as a human being, I applaud them for their deep commitment to the common good,” he said.

Salesforce has always had a social component to its corporate goals including its 1-1-1 philanthropy model. While Salesforce isn’t always completely consistent as with its contract with ICE, it does put money and personnel toward helping in the communities where it operates, encouraging volunteerism and charitable giving from the top down and modeled across the organization.

This investment fund, while looking at the investments through a distinctly Salesforce lens, is designed to fund startups to help solve intractable social problems, while using its extensive financial resources for the betterment of the world.


By Ron Miller