Personal blog of Yzmir Ramirez

Uptycs, a Boston-area startup that uses data to help understand and prevent security attacks, announced a $50 million Series C today, 11 months after announcing a $30 million Series B. Norwest Venture Partners led the round with participation from Sapphire Ventures and ServiceNow Ventures.

Company co-founder and CEO Ganesh Pai says that he was still well capitalized from last year’s investment, and wasn’t actually looking to raise funds, but the investors came looking for him and he saw a way to speed up some aspects of the company’s roadmap.

“It was one of those things where the round came in primarily as a function of execution and success to date, and we decided to capitalize on that because we know the partners and raised the capital so that we could use it meaningfully for a couple of different things, primarily sales and marketing acceleration,” Pai said.

He said that part of the reason for the company’s success over the last year was that the pandemic generated more customer interest as people moved to work from home, the SolarWinds hack happened and companies were moving to the cloud faster. “We provided a solution which was telemetric powered and very insightful when it came to solving their security problems and that’s what led to triple digit growth over the last year,” he said.

But Pai says that the company has not been sitting still in terms of the platform. While last year, he described it primarily as a forensic security data solution, helping customers figure out what happened after a security issue has happened, he says that the company has begun expanding on that vision to include all four main areas of security, including being proactive, reactive, predictive and protective.

The company started primarily in being reactive by figuring what happened in the past, but has begun to expand into these other areas over the last year, and the plan is to continue to build out that functionality.

“In the context of SolarWinds, what everyone is trying to figure out is how soon into the supply chain can you figure out what could be potentially wrong by looking at indications of behavior or indications of compromise, and our ability to ingest telemetry from a diverse set of sources, not as a bolt-on solution, but something which is built from the ground up, resonated really well,” Pai explained.

The company had 65 employees when we spoke last year for the Series B. Today, Pai says that number is approaching 140 and he is adding new people every week, with a goal to get to around 200 people by the end of the year. He says as the company grows, he keeps diversity top of mind.

“As we grow and as we raise capital diversity has been something which has been a high priority and very critical for us,” he said. In fact, he reports that more than 50% of his employees come from under-represented groups whether it’s Latinx, Black or Asian heritage.

Pai says that one of the reasons he has been able to build a diverse workforce is his commitment to a remote workplace, which means he can hire from anywhere, something he will continue to do even after the pandemic ends.

DataRobot, the Boston-based automated machine learning startup, had a bushel of announcements this morning as it expanded its platform to give technical and nontechnical users alike something new. It also announced it has acquired Zepl, giving it an advanced development environment where data scientists can bring their own code to DataRobot. The two companies did not share the acquisition price.

Nenshad Bardoliwalla, SVP of Product at DataRobot says that his company aspires to be the leader in this market and it believes the path to doing that is appealing to a broad spectrum of user requirements, from those who have little data science understanding to those who can do their own machine learning coding in Python and R.

“While people love automation, they also want it to be [flexible]. They don’t want just automation, but then you can’t do anything with it. They also want the ability to turn the knobs and pull the levers,” Bardoliwalla explained.

To resolve that problem, rather than building a coding environment from scratch, it chose to buy Zepl and incorporate its coding notebook into the platform in a new tool called Composable ML. “With Composable ML and with the Zepl acquisition, we are now providing a really first-class environment for people who want to code,” he said.

Zepl was founded in 2016 and raised $13 million along the way, according to Crunchbase data. The company didn’t want to reveal the number of employees or the purchase price, but the acquisition gives it advanced capabilities, especially a notebook environment to call its own to attract those more advanced users to the platform. The company plans to incorporate the Zepl functionality into the platform, while also leaving the standalone product in place.

Bardoliwalla said that they see the Zepl acquisition as an extension of the automated side of the house, where these tools can work in conjunction with one another with machines and humans working together to generate the best models. “This [generates an] organic mixture of the best of what a system can generate using DataRobot AutoML and the best of what human beings can do and kind of trying to compose those together into something really interesting […],” Bardoliwalla said.

The company is also introducing a no-code AI app builder that enables nontechnical users to create apps from the data set with drag and drop components. In addition, it’s adding a tool to monitor the accuracy of the model over time. Sometimes, after a model is in production for a time, the accuracy can begin to break down as the data on which the model is based is no longer valid. This tool monitors the model data for accuracy and warns the team when it’s starting to fall out of compliance.

Finally, the company is announcing a model bias monitoring tool to help root out model bias that could introduce racist, sexist or other assumptions into the model. To avoid this, the company has built a tool to identify when it sees this happening both in the model-building phase and in production. It warns the team of potential bias, while providing them with suggestions to tweak the model to remove it.

DataRobot is based in Boston and was founded in 2012. It has raised more than $750 million and has a valuation of over $2.8 billion, according to PitchBook.

Taking on Amazon S3 in the cloud storage game would seem to be a fool-hearty proposition, but Wasabi has found a way to build storage cheaply and pass the savings onto customers. Today the Boston-based startup announced a $112 million Series C investment on a $700 million valuation.

Fidelity Management & Research Company led the round with participation from previous investors. It reports that it has now raised $219 million in equity so far, along with additional debt financing, but it takes a lot of money to build a storage business.

CEO David Friend says that business is booming and he needed the money to keep it going. “The business has just been exploding. We achieved a roughly $700 million valuation on this round, so  you can imagine that business is doing well. We’ve tripled in each of the last three years and we’re ahead of plan for this year,” Friend told me.

He says that demand continues to grow and he’s been getting requests internationally. That was one of the primary reasons he went looking for more capital. What’s more, data sovereignty laws require that certain types of sensitive data like financial and healthcare be stored in-country, so the company needs to build more capacity where it’s needed.

He says they have nailed down the process of building storage, typically inside co-location facilities, and during the pandemic they actually became more efficient as they hired a firm to put together the hardware for them onsite. They also put channel partners like managed service providers (MSPs) and value added resellers (VARs) to work by incentivizing them to sell Wasabi to their customers.

Wasabi storage starts at $5.99 per terabyte per month. That’s a heck of a lot cheaper than Amazon S3, which starts at 0.23 per gigabyte for the first 50 terabytes or $23.00 a terabyte, considerably more than Wasabi’s offering.

But Friend admits that Wasabi still faces headwinds as a startup. No matter how cheap it is, companies want to be sure it’s going to be there for the long haul and a round this size from an investor with the pedigree of Fidelity will give the company more credibility with large enterprise buyers without the same demands of venture capital firms.

“Fidelity to me was the ideal investor. […] They don’t want a board seat. They don’t want to come in and tell us how to run the company. They are obviously looking toward an IPO or something like that, and they are just interested in being an investor in this business because cloud storage is a virtually unlimited market opportunity,” he said.

He sees his company as the typical kind of market irritant. He says that his company has run away from competitors in his part of the market and the hyperscalers are out there not paying attention because his business remains a fraction of theirs for the time being. While an IPO is far off, he took on an institutional investor this early because he believes it’s possible eventually.

“I think this is a big enough market we’re in, and we were lucky to get in at just the right time with the right kind of technology. There’s no doubt in my mind that Wasabi could grow to be a fairly substantial public company doing cloud infrastructure. I think we have a nice niche cut out for ourselves, and I don’t see any reason why we can’t continue to grow,” he said.

Today Juniper Networks announced it was acquiring smart wide area networking startup 128 Technology for $450 million.

This marks the second AI-fueled networking company Juniper has acquired in the last year and a half after purchasing Mist Systems in March 2019 for $405 million. With 128 Technology, the company gets more AI SD-WAN technology. SD-WAN is short for software-defined wide area networks, which means networks that cover a wide geographical area such as satellite offices, rather than a network in a defined space.

Today, instead of having simply software-defined networking, the newer systems use artificial intelligence to help automate session and policy details as needed, rather than dealing with static policies, which might not fit every situation perfectly.

Writing in a company blog post announcing the deal, executive vice president and chief product officer Manoj Leelanivas sees 128 Technology adding great flexibility to the portfolio as it tries to transition from legacy networking approaches to modern ones driven by AI, especially in conjunction with the Mist purchase.

“Combining 128 Technology’s groundbreaking software with Juniper SD-WAN, WAN Assurance and Marvis Virtual Network Assistant (driven by Mist AI) gives customers the clearest and quickest path to full AI-driven WAN operations — from initial configuration to ongoing AIOps, including customizable service levels (down to the individual user), simple policy enforcement, proactive anomaly detection, fault isolation with recommended corrective actions, self-driving network operations and AI-driven support,” Leelanivas wrote in the blog post.

128 Technologies was founded in 2014 and raised over $96 million, according to Crunchbase data. Its most recent round was a $30 million Series D investment in September 2019 led by G20 Ventures and The Perkins Fund.

In addition to the $450 million, Juniper has asked 128 Technology to issue retention stock bonuses to encourage the startup’s employees to stay on during the transition to the new owners. Juniper has promised to honor this stock under the terms of the deal. The deal is expected to close in Juniper’s fiscal fourth quarter, subject to normal regulatory review.

Every company today is struggling to deal with security and understanding what is happening on their systems. This is even more pronounced as companies have had to move their employees to work from home. Uptycs, a Boston-area security analytics startup, announced a $30 million Series B today to help companies detect and understand breaches when they happen.

Sapphire Ventures led the round with help from Comcast Ventures and ForgePoint Capital. The startup has now raised a total of $43 million, according to the company. Under the terms of today’s deal, Sapphire Ventures’ president and managing director Jai Das will be joining the company’s board.

Company co-founder and CEO Ganesh Pai says he and his co-founders previously worked at Akamai, where they observed Akamai’s debugging and diagnostic tools, which were designed to work at massive scale. The founders believed they could use a similar approach to building a security analytics platform, and in 2016 the group launched Uptycs .

“We help people to solve intrusion detection, compliance and audit and incident investigation. These are table stakes requirements [for security solutions] that most large scale organizations have, and of course with their scale the challenges vary. What we at Uptycs do is provide a solution for that,” Pai told TechCrunch.

The company uses a flight recorder approach to security, giving security operations teams the ability to sift through the data and review exactly how a detection happened and how the intruder got through the company’s defenses.

He recognizes his company is fortunate to get a round this large right now, but he says the solution has attracted a number of customers signing seven-digit contracts and this in turn got the attention of investors. “That customer engagement, their experience and this commitment from our customers led to this substantial round of funding,” he said.

The company currently has 65 employees spread across offices in Waltham, a Boston suburb, as well as two offices in India. Pai says the plan is to double that number in the next 12 months. “Between the cash flow from our existing customers and the pipeline for us and the funding, we are planning to grow in a meaningful way. If everything aligns with our expectation we will double our team size in the next 12 months,” he said.

As he grows his company in this way, Pai says they are talking to their investors about how to build a diverse workforce. “We’ve thought long and hard about it, both in terms of diversity and inclusion. It is a lot harder to execute because at the end of the day, there is a finite talent pool, but we are having conversations with our investors, who have seen patterns of success in terms of implementing such plans from growth stage ventures,” he said.

He added, “And of course we are a very early-stage company, but we are extremely cognizant, and given the current circumstances are acutely aware that we need to do our very best and make a difference.”

As the company has moved to work from home across its operations, he says it has benefited from working in the cloud from the start. “As an organization we are very fortunate that we built our organization so that everything runs in the cloud and everyone has been able to remain very productive,” he said.

We may be in the thick of a pandemic with all of the economic fallout that comes from that, but certain aspects of technology don’t change, no matter the external factors. Storage is one of them. In fact, we are generating more digital stuff than ever, and Wasabi, a Boston-based startup that has figured out a way to drive down the cost of cloud storage, is benefiting from that.

Today it announced a $30 million Series B led led by Forestay Capital, the technology innovation arm of Waypoint Capital, with help from previous investors. As with the previous round, Wasabi is going with home office investors, rather than traditional venture capital firms. Today’s round brings the total raised to $110 million, according to the company.

While founder and CEO David Friend wouldn’t discuss the specific valuation, he did say it was in the hundreds of millions of dollars.

Friend says the company needs the funds to keep up with the rapid growth. “We’ve got about 15,000 customers today, hundreds of petabytes of storage, 2,500 channel partners, 250 technology partners — so we’ve been busy,” he said.

He says that revenue continues to grow in spite of the impact of COVID-19 on other parts of the economy. “Revenue grew 5x last year. It’ll probably grow 3.5x this year. We haven’t seen any real slowdown from the coronavirus. Quarter over quarter growth will be in excess of 40% — this quarter over Q1 — so it’s just continuing on a torrid pace,” he said.

The challenge for a company like Wasabi, which is looking to capture a large chunk of the growing cloud storage market, is the infrastructure piece. It needs to keep building more to meet increasing demand, while keeping costs down, which remains its primary value proposition with customers.

The money will be used mostly to continue to expand its growing infrastructure requirements. The more they store, the more data centers they need, and that takes money. It will also help the company expand into new markets where countries have data sovereignty laws that require data to be stored in-country.

The company launched in 2015. It previously raised $68 million in 2018.

Note: This article originally stated this was a debt financing round. The company has clarified that it is an equity round.

Artificial intelligence is playing an increasingly large role in enterprise software, and Boston’s DataRobot has been helping companies build, manage and deploy machine learning models for some time now. Today, the company announced a $206 million Series E investment led by Sapphire Ventures.

Other participants in this round included new investors Tiger Global Management, World Innovation Lab, Alliance Bernstein PCI, and EDBI along with existing investors DFJ Growth, Geodesic Capital, Intel Capital, Sands Capital, NEA and Meritech.

Today’s investment brings the total raised to $431 million, according to the company. It has a pre-money valuation of $1 billion, according to PitchBook. DataRobot would not confirm this number.

The company has been catching the attention of these investors by offering a machine learning platform aimed at analysts, developers and data scientists to help build predictive models much more quickly than it typically takes using traditional methodologies. Once built, the company provides a way to deliver the model in the form of an API, simplifying deployment.

The late-stage startup plans to use the money to continue building out its product line, while looking for acquisition opportunities where it makes sense. The company also announced the availability of a new product today, DataRobot MLOps, a tool to manage, monitor and deploy machine learning models across a large organization.

The company, which was founded in 2012, claims it has had triple-digit recurring revenue growth dating back to 2015, as well as one billion models built on the platform to-date. Customers contributing to that number include a broad range of companies such as Humana, United Airlines, Harvard Business School and Deloitte.

Imagine coating an expensive part with a layer of diamond dust the width of a human hair, capturing its light pattern as a unique identifier, then storing that identifier in a traditional database or on the blockchain. That’s precisely what Dust Identity, a Boston-based startup is trying to do, and today it got $2.3 million in seed money led by Kleiner Perkins with participation from New Science Ventures, Angular Ventures, and Castle Island Ventures.

The science behind Dust Identity was nurtured inside MIT, but the company has been at work for two years trying to build a solution based on that idea after receiving early support from DARPA. What these folks do is manufacture extremely tiny diamonds. They dust an object such as a circuit board with a coating of this and capture the diamonds in a polymer, company CEO and co-founder Ophir Gaathon explained.

“Once the diamonds fall on the surface of a polymer epoxy, and that polymer cures, the diamonds are fixed in their position, fixed in their orientation, and it’s actually the orientation of those diamonds that we developed a technology that allows us to read those angles very quickly,” Gaathon told TechCrunch.

For all the advanced technology at play here, Dust Identity is truly an identity company, but instead of identifying an individual, its purpose is to provide a trusted identity for an object using a physical anchor — in this case, diamond dust. You may be thinking that diamonds are kind of an expensive way to achieve this, but as it turns out, the company is actually creating the coating materials from low-cost diamond industrial waste.

“We start with diamond waste (for example, [from] the abrasive industry), but we developed a proprietary process (that’s of course highly scalable and economical) to purify and engineer the diamond waste into dust,” a company spokesperson explained.

The idea behind all of this is to prove that an object is valid and hasn’t been tampered with. The dust is applied at some point during the manufacturing process. The unique identifier is captured with some kind of commercial scanner and stored in the database. It provides a physical anchor for blockchain supply chain solutions that’s currently lacking. When the part makes its way to the buyer, they can run the part under a scanner and make sure it matches. If the dust pattern has been disturbed, there’s a good chance the piece was tampered with.

Finding a way to create uncopyable tags for physical objects is a kind of supply chain holy grail. Ilya Fushman, a partner at Kleiner Perkins says his firm recognized the potential of this solution. “We have a pretty strong hard tech practice. We understand the value of supply chain and supply chain integrity,” he said.

The company is not alone in trying to find a way to attach a physical anchor to items in the supply chain. In fact, you can go back to RFID tags and QR codes, but Gaathon says the security of these approaches has degraded over time as hackers figure out how to copy them. IBM and others are working on tiny chips to attach to objects, but the diamond dust approach could be the most secure if it can scale because it works with an entirely random light pattern that can never be reproduced.

The startup intends to take the money and try to prove this idea can be commercialized for government and manufacturing use cases. It certainly gets points for creativity here and it could be onto something that could transform how we track the integrity of items as they move through a supply chain.

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