SetSail raises raises $7M to change how sales teams are compensated

Most sales teams earn a commission after a sale closes, but nothing prior to that. Yet there are a variety of signals along the way that indicate the sales process is progressing, and SetSail, a startup from some former Google engineers, is using machine learning to figure out what those signals are, and how to compensate salespeople as they move along the path to a sale, not just after they close the deal.

Today, the startup announced a $7 million investment led by Wing Venture Capital with help from Operator Collective and Team8. Under the terms of the deal, Leyla Seka from Operator will be joining the board. Today’s investment brings the total raised to $11 million, according to the company.

CEO and co-founder Haggai Levi says his company is based on the idea that commission alone is not a good way to measure sales success, and that it is in fact a lagging indicator. “We came up with a different approach. We use machine learning to create progress-based incentives,” Levi explained

To do that they rely on machine learning to discover the signals that are coming from the customer that indicate that the deal is moving forward, and using a points system, companies can begin compensating reps on hitting these milestones, even before the sale closes.

The seeds for the idea behind SetSail were planted years ago when the three founders were working at Google tinkering with ways to motivate sales reps beyond pure commission. From a behavioral perspective, Levi and his co-founders found that reps were taking fewer risks with a pure commission approach and they wanted to find a way to change that. The incremental compensation system achieves that.

“If I’m closing the deal, I’m getting my commission. If I’m not closing the deal, I’m getting nothing. That means from a behavioral point of view, I would take the shortest path to win a deal, and I would take the minimum risk possible. So if there’s a competitive situation I will try to avoid that,” he said.

They look at things like appointments, emails and call transcripts. The signals will vary by customer. One may find an appointment with CIO is a good signal a deal is on the right trajectory, but to avoid having reps gaming the system by filling the CRM with the kinds of positive signals the company is looking for, they only rely on objective data, rather than any kind of self-reporting information from reps themselves.

The team eventually built a system like this inside Google, and in 2018, left to build a solution for the rest of the world that does something similar.

As the company grows, Levi says he is building a diverse team, not only because it’s the right thing to do, but because it simply makes good business sense. “The reality is that we’re building a product for a diverse audience, and if we don’t have a diverse team we would never be able to build the right product,” he explained.

The company’s unique approach to sales compensation is resonating with customers like Dropbox, Lyft and Pendo, who are looking for new ways to motivate sales teams, especially during a pandemic when there may be a longer sales cycle. This kind of system provides a way to compensate sales teams more incrementally and reward positive approaches that have proven to result in sales.


By Ron Miller

SUSE acquires Kubernetes management platform Rancher Labs

SUSE, which describes itself as ‘the world’s largest independent open source company,’ today announced that it has acquired Rancher Labs, a company that has long focused on making it easier for enterprises to make their container clusters.

The two companies did not disclose the price of the acquisition, but Rancher was well funded, with a total of $95 million in investments. It’s also worth mentioning that it’s only been a few months since the company announced its $40 million Series D round led by Telstra Ventures. Other investors include the likes of Mayfield and Nexus Venture Partners, GRC SinoGreen and F&G Ventures.

Like similar companies, Rancher’s original focus was first on Docker infrastructure before it pivoted to putting its emphasis on Kubernetes once that became the de facto standard for container orchestration. Unsurprisingly, this is also why SUSE is now acquiring this company. After a number of ups and downs — and various ownership changes — SUSE has now found its footing again and today’s acquisition shows that its aiming to capitalize on its current strengths.

Just last month, the company reported that the annual contract value of its booking increased by 30% year over year and that it saw a 63% increase in customer deals worth more than $1 million in the last quarter, with its cloud revenue growing 70%. While it is still in the Linux distribution business that the company was founded on, today’s SUSE is a very different company, offering various enterprise platforms (including its Cloud Foundry-based Cloud Application Platform), solutions and services. And while it already offered a Kubernetes-based container platform, Rancher’s expertise will only help it to build out this business.

“This is an incredible moment for our industry, as two open source leaders are joining forces. The merger of a leader in Enterprise Linux, Edge Computing and AI with a leader in Enterprise Kubernetes Management will disrupt the market to help customers accelerate their digital transformation journeys,” said SUSE CEO Melissa Di Donato in today’s announcement. “Only the combination of SUSE and Rancher will have the depth of a globally supported and 100% true open source portfolio, including cloud native technologies, to help our customers seamlessly innovate across their business from the edge to the core to the cloud.”

The company describes today’s acquisition as the first step in its ‘inorganic growth strategy’ and Di Donato notes that this acquisition will allow the company to “play an even more strategic role with cloud service providers, independent hardware vendors, systems integrators and value-added resellers who are eager to provide greater customer experiences.”


By Frederic Lardinois

Nvidia’s Ampere GPUs come to Google Cloud

Nvidia today announced that its new Ampere-based data center GPUs, the A100 Tensor Core GPUs, are now available in alpha on Google Cloud. As the name implies, these GPUs were designed for AI workloads, as well as data analytics and high-performance computing solutions.

The A100 promises a significant performance improvement over previous generations. Nvidia says the A100 can boost training and inference performance by over 20x compared to its predecessors (though you’ll mostly see 6x or 7x improvements in most benchmarks) and tops out at about 19.5 TFLOPs in single-precision performance and 156 TFLOPs for Tensor Float 32 workloads.

Image Credits: Nvidia

“Google Cloud customers often look to us to provide the latest hardware and software services to help them drive innovation on AI and scientific computing workloads,” said Manish Sainani, Director of Product Management at Google Cloud, in today’s announcement. “With our new A2 VM family, we are proud to be the first major cloud provider to market Nvidia A100 GPUs, just as we were with Nvidia’s T4 GPUs. We are excited to see what our customers will do with these new capabilities.”

Google Cloud users can get access to instances with up to 16 of these A100 GPUs, for a total of 640GB of GPU memory and 1.3TB of system memory.


By Frederic Lardinois

Vendia raises $5.1M for its multi-cloud serverless platform

When the inventor of AWS Lambda, Tim Wagner, and the former head of blockchain at AWS, Shruthi Rao, co-found a startup, it’s probably worth paying attention. Vendia, as the new venture is called, combines the best of serverless and blockchain to help build a truly multi-cloud serverless platform for better data and code sharing.

Today, the Vendia team announced that it has raised a $5.1 million seed funding round, led by Neotribe’s Swaroop ‘Kittu’ Kolluri. Correlation Ventures, WestWave Capital, HWVP, Firebolt Ventures, Floodgate and Future\Perfect Ventures also participated in this oversubscribed round.

(Image Credits: Vendia)

Seeing Wagner at the helm of a blockchain-centric startup isn’t exactly a surprise. After building Lambda at AWS, he spent some time as VP of engineering at Coinbase, where he left about a year ago to build Vendia.

“One day, Coinbase approached me and said, ‘hey, maybe we could do for the financial system what you’ve been doing over there for the cloud system,’ ” he told me. “And so I got interested in that. We had some conversations. I ended up going to Coinbase and spent a little over a year there as the VP of Engineering, helping them to set the stage for some of that platform work and tripling the size of the team.” He noted that Coinbase may be one of the few companies where distributed ledgers are actually mission-critical to their business, yet even Coinbase had a hard time scaling its Ethereum fleet, for example, and there was no cloud-based service available to help it do so.

Tim Wagner, Vendia co-founder and CEO (Image Credits: Vendia)

“The thing that came to me as I was working there was why don’t we bring these two things together? Nobody’s thinking about how would you build a distributed ledger or blockchain as if it were a cloud service, with all the things that we’ve learned over the course of the last 10 years building out the public cloud and learning how to do it at scale,” he said.

Wagner then joined forces with Rao, who spent a lot of time in her role at AWS talking to blockchain customers. One thing she noticed was that while it makes a lot of sense to use blockchain to establish trust in a public setting, that’s really not an issue for enterprise.

“After the 500th customers, it started to make sense,” she said. “These customers had made quite a bit of investment in IoT and edge devices. And they were gathering massive amounts of data. And they also made investments on the other side, with AI and ML and analytics. And they said, ‘well, there’s a lot of data and I want to push all of this data through these intelligent systems. And I need a mechanism to get this data.’ ” But the majority of that data often comes from third-party services. At the same time, most blockchain proof of concepts weren’t moving into any real production usage because the process was often far too complex, especially enterprises that maybe wanted to connect their systems to those of their partners.

Shruthi Rao, Vendia co-founder and CBO (Image Credits: Vendia)

“We are asking these partners to spin up Kubernetes clusters and install blockchain nodes. Why is that? That’s because for blockchain to bring trust into a system to ensure trust, you have to own your own data. And to own your own data, you need your own node. So we’re solving fundamentally the wrong problem,” she explained.

The first product Vendia is bringing to market is Vendia Share, a way for businesses to share data with partners (and across clouds) in real time, all without giving up control over that data. As Wagner noted, businesses often want to share large data sets but they also want to ensure they can control who has access to that data. For those users, Vendia is essentially a virtual data lake with provenance tracking and tamper-proofing built-in.

The company, which mostly raised this round after the coronavirus pandemic took hold in the U.S., is already working with a couple of design partners in multiple industries to test out its ideas, and plans to use the new funding to expand its engineering team to build out its tools.

“At Neotribe Ventures, we invest in breakthrough technologies that stretch the imagination and partner with companies that have category creation potential built upon a deep-tech platform,” said Neotribe founder and managing director Kolluri. “When we heard the Vendia story, it was a no-brainer for us. The size of the market for multi-party, multi-cloud data and code aggregation is enormous and only grows larger as companies capture every last bit of data. Vendia’s Serverless -based technology offers benefits such as ease of experimentation, no operational heavy lifting and a pay-as-you-go pricing model, making it both very consumable and highly disruptive. Given both Tim and Shruthi’s backgrounds, we know we’ve found an ideal ‘Founder fit’ to solve this problem! We are very excited to be the lead investors and be a part of their journey.”


By Frederic Lardinois

CodeGuru, AWS’s AI code reviewer and performance profiler, is now generally available

AWS today announced that CodeGuru, a set of tools that use machine learning to automatically review code for bugs and suggest potential optimizations, is now generally available. The tool launched into preview at AWS re:Invent last December.

CodeGuru consists of two tools, Reviewer and Profiler, and those names pretty much describe exactly what they do. To build Reviewer, the AWS team actually trained its algorithm with the help of code from more than 10,000 open source projects on GitHub, as well as reviews from Amazon’s own internal codebase.

“Even for a large organization like Amazon, it’s challenging to have enough experienced developers with enough free time to do code reviews, given the amount of code that gets written every day,” the company notes in today’s announcement. “And even the most experienced reviewers miss problems before they impact customer-facing applications, resulting in bugs and performance issues.”

To use CodeGuru, developers continue to commit their code to their repository of choice, no matter whether that’s GitHub, Bitbucket Cloud, AWS’s own CodeCommit or another service. CodeGuru Reviewer then analyzes that code, tries to find bugs and, if it does, it will also offer potential fixes. All of this is done within the context of the code repository, so CodeGuru will create a GitHub pull request, for example, and add a comment to that pull request with some more info about the bug and potential fixes.

To train the machine learning model, users can also provide CodeGuru with some basic feedback, though we’re mostly talking “thumbs up” and “thumbs down” here.

The CodeGuru Application Profiler has a somewhat different mission. It is meant to help developers figure out where there might be some inefficiencies in their code and identify the most expensive lines of code. This includes support for serverless platforms like AWS Lambda and Fargate.

One feature the team added since it first announced CodeGuru is that Profiler now attaches an estimated dollar amount to the lines of unoptimized code.

“Our customers develop and run a lot of applications that include millions and millions of lines of code. Ensuring the quality and efficiency of that code is incredibly important, as bugs and inefficiencies in even a few lines of code can be very costly. Today, the methods for identifying code quality issues are time-consuming, manual, and error-prone, especially at scale,” said Swami Sivasubramanian, vice president, Amazon Machine Learning, in today’s announcement. “CodeGuru combines Amazon’s decades of experience developing and deploying applications at scale with considerable machine learning expertise to give customers a service that improves software quality, delights their customers with better application performance, and eliminates their most expensive lines of code.”

AWS says a number of companies started using CodeGuru during the preview period. These include the likes of Atlassian, EagleDream and DevFactory.

“While code reviews from our development team do a great job of preventing bugs from reaching production, it’s not always possible to predict how systems will behave under stress or manage complex data shapes, especially as we have multiple deployments per day,” said Zak Islam, head of Engineering, Tech Teams, at Atlassian. “When we detect anomalies in production, we have been able to reduce the investigation time from days to hours and sometimes minutes thanks to Amazon CodeGuru’s continuous profiling feature. Our developers now focus more of their energy on delivering differentiated capabilities and less time investigating problems in our production environment.”

Image Credits: AWS


By Frederic Lardinois

Cape Privacy launches data science collaboration platform with $5.06M seed investment

Cape Privacy emerged from stealth today after spending two years building a platform for data scientists to privately share encrypted data. The startup also announced $2.95 million in new funding and $2.11 million in funding it got when the business launched in 2018 for a total of $5.06 million raised.

Boldstart Ventures and Version One led the round with participation from Haystack, Radical Ventures and Faktory Ventures.

Company CEO Ché Wijesinghe says that data science teams often have to deal with data sets that contain sensitive data and share data internally or externally for collaboration purposes. It creates a legal and regulatory data privacy conundrum that Cape Privacy is trying to solve.

“Cape Privacy is a collaboration platform designed to help focus on data privacy for data scientists. So the biggest challenge that people have today from a business perspective is managing privacy policies for machine learning and data science,” Wijesinghe told TechCrunch.

The product breaks down that problem into a couple of key areas. First of all it can take language from lawyers and compliance teams and convert that into code that automatically generates policies about who can see the different types of data in a given data set. What’s more, it has machine learning underpinnings so it also learns about company rules and preferences over time.

It also has a cryptographic privacy component. By wrapping the data with a cryptographic cypher, it lets teams share sensitive data in a safe way without exposing the data to people who shouldn’t be seeing it because of legal or regulatory compliance reasons.

“You can send something to a competitor as an example that’s encrypted, and they’re able  to process that encrypted data without decrypting it, so they can train their model on encrypted data,” company co-founder and CTO Gavin Uhma explained.

The company closed the new round in April, which means they were raising in the middle of a pandemic, but it didn’t hurt that they had built the product already and were ready to go to market, and that Uhma and his co-founders had already built a successful startup, GoInstant that was acquired by Salesforce in 2012. (It’s worth noting that GoInstant debuted at TechCrunch Disrupt in 2011.)

Uhma and his team brought Wijesinghe on board to build the sales and marketing team because as a technical team, they wanted someone with go to market experience running the company, so they could concentrate on building product.

The company has 14 employees and are already an all remote team, so that the team didn’t have to adjust at all when the pandemic hit. While it plans to keep hiring fairly limited for the foreseeable future, the company has had a diversity and inclusion plan from the start.

“You have to be intentional about about seeking diversity, so it’s something that when we sit down and map out our hiring and work with recruiters in terms of our pipeline, we really make sure that that diversity is one of our objectives. You just have you have it as a goal, as part of your culture, and it’s something that when we see the picture of the team, we want to see diversity,” he said.

Wijesinghe adds, “As a person of color myself, I’m very sensitive to making sure that we have a very diverse team, not just from a color perspective, but a gender perspective as well,” he said.

The company is gearing up to sell the product  and has paid pilots starting in the coming weeks.


By Ron Miller

Outreach nabs $50M at a $1.33B valuation for software that helps with sales engagement

CRM software has become a critical piece of IT when it comes to getting business done, and today a startup focusing on one specific aspect of that stack — sales automation — is announcing a growth round of funding underscoring its own momentum. Outreach, which has built a popular suite of tools used by salespeople to help identify and reach out to prospects and improve their relationships en route to closing deals, has raised $50 million in a Series F round of funding that values the company at $1.33 billion. 

The funding will be used to continue expanding geographically — headquartered in Seattle, Outreach also has an office in London and wants to do more in Europe and eventually Asia — as well as to invest in product development.

The platform today essentially integrates with a company’s existing CRM, be it Salesforce, or Microsoft’s, or Kustomer, or something else — and provides an SaaS-based set of tools for helping to source and track meetings, have to-hand information on sales targets, and a communications manager that helps with outreach calls and other communication in real-time. It will be investing in more AI around the product, such as its newest product Kaia (an acronym for “knowledge AI assistant”), and it has also hired a new CFO, Melissa Fisher, from Qualys, possibly a sign of where it hopes to go next as a business.

Sands Capital — an investor out of Virginia that also backs the likes of UiPath and DoorDash — is leading the round, Outreach noted, with “strong participation” also from strategic backer Salesforce Ventures. Other investors include Operator Collective (a new backer that launched last year and focuses on B2B) and previous backers Lone Pine Capital, Spark Capital, Meritech Capital Partners, Trinity Ventures, Mayfield, and Sapphire Ventures.

Outreach has raised $289 million to date, and for some more context, this is definitely an upround: the startup was last valued at $1.1 billion when it raised a Series E in April 2019.

The funding comes on the heels of strong growth for the company: more than 4,000 businesses now use its tools, including Adobe, Tableau, DoorDash, Splunk, DocuSign, and SAP, making Outreach the biggest player in a field that also includes Salesloft (which also raised a significant round last year on the heels of Outreach’s), ClariChorus.aiGongConversica, and Afiniti. Its sweet spot has been working with technology-led businesses and that sector continues to expand its sales operations, even as much of the economy has contracted in recent months. 

“You are seeing a cambric explosion of B2B startups happening everywhere,” Manny Medina, CEO and co-founder of Outreach, said in a phone interview this week. “It means that sales roles are being created as we speak.” And that translates to a growing pool of potential customers for Outreach.

It wasn’t always this way.

When Outreach was first founded in 2011 in Seattle, it wasn’t a sales automation company. It was a recruitment startup called GroupTalent working on software to help source and hire talent, aimed at tech companies. That business was rolling along, until it wasn’t: in 2015, the startup found itself with only two months of runway left, with little hope of raising more. 

“We were not hitting our stride, and growth was hard. We didn’t make the numbers in 2014 and then had two months of cash left and no prospects of raising more,” Medina recalled. “So I sat down with my co-founders,” — Gordon Hempton, Andrew Kinzer and Wes Hather, none of whom are at the company anymore — “and we decided to sell our way out of it. We thought that if we generated more meetings we could gain more opportunities to try to sell our recruitment software.

“So we built the engine to do that, and we saw that we were getting 40% reply rates to our own outreaching emails. It was so successful we had a 10x increase in productivity. But we ran out of sales capacity, so we started selling the meetings we had managed to secure with potential talent directly to the tech companies themselves, who would have become their employers.”

That quickly tipped over into a business opportunity of its own. “Companies were saying to us, ‘I don’t want to buy the recruitment software. I need that sales engine!” The company never looked back, and changed its name to work for the pivot.

Fast forward to 2020, and times are challenging in a completely different way, defined as we are by a global health pandemic that affects what we do every day, where we go, how we work, how we interact with people, and much more. 

Medina says that impact of the novel coronavirus has been a significant one for the company and its customers, in part because it fits well with two main types of usage cases that have emerged in the world of sales in the time of COVID-19.

“Older sellers now working from home are accomplished and don’t need to be babysat,” he said, but added but they can’t rely on their traditional touchpoints “like meetings, dinners, and bar mitzvahs” anymore to seal deals. “They don’t have the tools to get over the line. So our product is being called in to help them.”

Another group at the other end of the spectrum, he said, are “younger and less experienced salespeople who don’t have the physical environment [many live in smaller places with roommates] nor experience to sell well alone. For them it’s been challenging not to come into an office because especially in smaller companies, they rely on each other to train, to listen to others on calls to learn how to sell.”

That’s the other scenario where Outreach is finding some traction: they’re using Outreach’s tools as a proxy for physically sitting alongside and learning from more experienced colleagues, and using it as a supplement to learning the ropes in the old way .

Like a lot of sales tools that are powered by AI, Outbrain in part is taking on some of the more mundane jobs of salespeople. But Medina doesn’t believe that this will play out in the “man versus machine” scenario we often ponder when we think about human obsolescence in the face of technological efficiency. In other words, he doesn’t think we’re close to replacing the humans in the mix, even at a time when we’re seeing so many layoffs.

“We are at the early innings,” he said. “There are 6.8 million sales people and we only have north of 100,000 users, not even 2% of the market. There may be a redefinition of the role, but not a reduction.”


By Ingrid Lunden

Searchable.ai nabs additional $4M seed to continue building AI-driven search

Searchable.ai is an early-stage startup in the alpha phase of testing its initial product, but it has an idea compelling enough to attract investment, even during a pandemic. Today the company announced an additional $4 million in seed capital to continue building its AI-driven search solution.

Susquehanna International Group and Omicron Media co-led the round with participation by Defy Partners, NextView Ventures and a group of unnamed angel investors. Today’s investment comes on top of the $2 million in seed money the startup announced in October.

Company co-founder and CEO Brian Shin said that when he presented to his investors in early March at the last event he attended before everything shut down, they approached him about additional money, and given the economic uncertainty he decided to take it.

“Honestly we probably would not have taken additional money if it was not for the uncertainty around the macro environment right now,” he told TechCrunch.

The company is trying to solve enterprise search and being pre-revenue, Shin recognized that having additional capital would give them more room to build the product and get it to market.

“We are trying to solve this problem where people just can’t find information that they need in order to do their jobs. When you look within the workplace, this problem is just getting worse and worse with the proliferation of different formats and people storing their information in many different places, local networks, cloud repositories, email and Slack,” he explained.

They have a few thousand people in the alpha program right now testing a personal desktop version of the application that helps individual users find their content wherever it happens to be. The plan is to open that up to a wider group soon.

The road map calls for a teams version where groups of employees can search among their different individual repositories, a developer version to build the search technology into other operations and eventually an enterprise tool. They also want to add voice search starting with an Alexa skill with the general belief that we need to move beyond keyword searches to more natural language approaches.

“We believe that there’ll be a whole new category of search, search companies and search products that are more conversational. […] Being able to interact with your information more naturally, more and more conversationally, that’s where we think the markets is going,” he said.

The company now has more money in the bank to help achieve that vision.


By Ron Miller

Nanox, maker of a low-cost scanning service to replace X-rays, expands Series B to $51M

A lot of the attention in medical technology today has been focused on tools and innovations that might help the world better fight the COVID-19 global health pandemic. Today comes news of another startup that is taking on some funding for a disruptive innovation that has the potential to make both COVID-19 as well as other kinds of clinical assessments more accessible.

Nanox, a startup out of Israel that has developed a small, low-cost scanning system and “medical screening as a service” to replace the costly and large machines and corresponding software typically used for X-rays, CAT scans, PET scans and other body imaging services, is today announcing that it has raised $20 million from a strategic investor, South Korean carrier SK Telecom.

SK Telecom in turn plans to help distribute physical scanners equipped with Nanox technology as well as resell the pay-per-scan imaging service, branded Nanox.Cloud, and corresponding 5G wireless network capacity to operate them. Nanox currently licenses its tech to big names in the imaging space like FujiFilm, and Foxconn is also manufacturing its donut-shaped Nanox.Arc scanners.

The funding is technically an extension of Nanox’s previous round, which was announced earlier this year at $26 million with backing from Foxconn, FujiFilm and more. Nanox says that the full round is now closed off at $51 million, with the company having raised $80 million since launching almost a decade ago, in 2011.

Nanox’s valuation is not being publicly disclosed, a but a news report in the Israeli press from December said that one option the startup was considering was an IPO at a $500 million valuation. We understand from sources that the valuation is about $100 million higher now.

The Nanox system is based around proprietary technology related to digital X-rays. Digital radiography is a relatively new area in the world of imaging that relies on digital scans rather than X-ray plates to capture and process images.

Nanox says the ARC comes in at 70 kg versus 2,000 kg for the average CT scanner, and production costs are around $10,000 compared to $1-3 million for the CT scanner.

But in addition to being smaller (and thus cheaper) machines with much of the processing of images done in the cloud, the Nanox system, according to CEO and founder Ran Poliakine, can make its images in a tiny fraction of a second, making them significantly safer in terms of radiation exposure compared to existing methods.

Imaging has been in the news a lot of late because it has so far been one of the most accurate methods for detecting the progress of COVID-19 in patients or would-be patients in terms of how it is affecting patients’ lungs and other organs. While the dissemination of equipment like Nanox’s definitely could play a role in handling those cases better, the ultimate goal of the startup is much wider than that.

Ultimately, the company hopes to make its devices and cloud-based scanning service ubiquitous enough that it would be possible to run early detection, preventative scans for a much wider proportion of the population.

“What is the best way to fight cancer today? Early detection. But with two-thirds of the world without access to imaging, you may need to wait weeks and months for those scans today,” said Poliakine.

The startup’s mission is to distribute some 15,000 of its machines over the next several years to bridge that gap, and it’s getting there through partnerships. In addition to the SK Telecom deal it’s announcing today, last March, Nanox inked a $174 million deal to distribute 1,000 machines across Australia, New Zealand and Norway in partnership with a company called the Gateway Group.

The SK Telecom investment is an interesting development that underscores how carriers see 5G as an opportunity to revisit what kinds of services they resell and offer to businesses and individuals, and SK Telecom specifically has singled out healthcare as one obvious and big opportunity.

“Telecoms carriers are looking for opportunities around how to sell 5G,” said Ilung Kim, SK Telecom’s president, in an interview. “Now you can imagine a scanner of this size being used in an ambulance, using 5G data. It’s a game changer for the industry.”

Looking ahead, Nanox will continue to ink partnerships for distributing its hardware and reselling its cloud-based services for processing the scans, but Poliakine said it does not plan to develop its own  technology beyond that to gain insights from the raw data. For that, it’s working with third parties — currently three AI companies – that plug into its APIs, and it plans to add more to the ecosystem over time.


By Ingrid Lunden

11 VCs share their thoughts on enterprise startup trends and opportunities

Compared to other tech firms, enterprise companies have held up well during the pandemic.

If anything, the problems enterprises were facing prior to the economic downturn have become even more pronounced; if you were thinking about moving to the cloud or just dabbling in it, you’re probably accelerating that motion. If you were trying to move off of legacy systems, that has become even more imperative. And if you were attempting to modernize processes and workflows, whether engineer- and developer-related, or across other parts of the organization, chances are good that you are giving that a much closer look.

We won’t be locked down forever and employees will eventually return to offices, but it’s likely that many companies will take the lessons they learned during this era and put them to work inside their organizations. Startups are uniquely positioned to help companies solve these new modern kinds of problems, much more so than a legacy vendor (which could be itself trying to update its approach).

Venture capitalists certainly understand all of these dynamics and are always dutifully searching for startups that could help companies shift to a digital future more quickly.

We spoke to 11 of them to take their pulse and learn more about the trends that are exciting them, what they look for in an investment opportunity and which parts of the enterprise are ripe for startups to impact:

  • Max Gazor, CRV
  • Navin Chadda, Mayfield
  • Matt Murphy, Menlo Venture Capital
  • Soma Somasagar, Madrona Ventures
  • Jon Lehr, Work-Bench
  • Steve Herrod, General Catalyst
  • Jai Das, Sapphire Ventures
  • Max Gazor,  CRV
  • Ed Sim, Boldstart Ventures
  • Martin Cassado, Andreessen Horowitz
  • Vassant Natarajan, Accel

Max Gazor, CRV

What trends are you most excited about in the enterprise from an investing perspective?

It’s abundantly clear that cloud software markets are bigger than most people anticipated. We continue to invest heavily there as we have been doing for the last decade.

Specifically, the most exciting trend right now in enterprise is low-code software development. I’m on the board of Airtable, where I led the Series A and co-led the Series B investments, so I see first hand how this will play out. We are heading toward a future where hundreds of millions of people will be empowered to compose software that fits their own needs. Imagine the productivity and transformation that will unlock in the world! It may be one of the largest market opportunities we have seen since cloud computing.


By Ron Miller

Microsoft launches Project Bonsai, its new machine teaching service for building autonomous systems

At its Build developer conference, Microsoft today announced that Project Bonsai, its new machine teaching service, is now in public preview.

If that name sounds familiar, it’s probably because you remember that Microsoft acquired Bonsai, a company that focuses on machine teaching, back in 2018. Bonsai combined simulation tools with different machine learning techniques to build a general-purpose deep reinforcement learning platform, with a focus on industrial control systems.

It’s maybe no surprise then that Project Bonsai, too, has a similar focus on helping businesses teach and manage their autonomous machines. “With Project Bonsai, subject-matter experts can add state-of-the-art intelligence to their most dynamic physical systems and processes without needing a background in AI,” the company notes in its press materials.

“The public preview of Project Bonsai builds on top of the Bonsai acquisition and the autonomous systems private preview announcements made at Build and Ignite of last year,” a Microsoft spokesperson told me.

Interestingly, Microsoft notes that project Bonsai is only the first block of a larger vision to help its customers build these autonomous systems. The company also stresses the advantages of machine teaching over other machine learning approach, especially the fact that it’s less of a black box approach than other methods, which makes it easier for developers and engineers to debug systems that don’t work as expected.

In addition to Bonsai, Microsoft also today announced Project Moab, an open-source balancing robot that is meant to help engineers and developers learn the basics of how to build a real-world control system. The idea here is to teach the robot to keep a ball balanced on top of a platform that is held by three arms.

Potential users will be able to either 3D print the robot themselves or buy one when it goes on sale later this year. There is also a simulation, developed by MathWorks, that developers can try out immediately.

“You can very quickly take it into areas where doing it in traditional ways would not be easy, such as balancing an egg instead,” said Mark Hammond, Microsoft General Manager
for Autonomous Systems. “The point of the Project Moab system is to provide that
playground where engineers tackling various problems can learn how to use the tooling and simulation models. Once they understand the concepts, they can apply it to their novel use case.”


By Frederic Lardinois

Adobe announces AI toolbox for Experience Platform

Most companies don’t have the personnel to do AI well, so they turn to platform vendors like Adobe for help. Like other platforms, it has been building AI into its product set for several years now, but wanted to give marketers a set of tools that take advantage of some advanced AI capabilities out of the box.

Today, the company announced five pre-packaged AI solutions specifically designed to give marketers more intelligent insight. Amit Ahuja, VP of ecosystem development at Adobe, says even before the pandemic, customers were struggling to deal with the onslaught of data and how they could use it to understand their customers better.

“There is so much data coming in, and customers are struggling to leverage this data — and not just for the purpose of analytics and insights, which is a huge part of it, but also to do predictive optimization,” Ahuja explained.

What’s more, we’ve known for some time that when there is so much data, it becomes impossible to make sense of it manually. Given that AI deals best with tons of data, Adobe wanted to take advantage of that, while packaging some popular data scenarios in a way that makes it easy for marketers to get insights.

That data comes from the Adobe Experience Platform, which the is designed to pull data not only from Adobe products, but from a variety of enterprise sources to help marketers build a more complete picture of their customers and get answers to key questions.

Customer Insights AI helps users understand their customers better. Image Credit: Adobe

The company is announcing a total of five AI tools today, two of which are generally available with the remainder in Beta for now. For starters, Customer AI helps marketers understand why their customers do what they do. For instance, why they keep coming back or why they stopped. Attribution AI helps marketers understand how effective their strategies are, something that’s always important, but especially in this economy where effectively deploying spend is more important than ever.

The first of the Beta tools is Journey AI, which helps marketers decide the best channel to engage customers. Content and Commerce AI looks at the most effective way to deliver content and finally Leads AI looks at the visitors most likely to convert to customers.

These five are just a start, and the company plans to add new tools to the toolbox as customers look for additional insights from the data to help them improve their marketing outcomes.


By Ron Miller

Kustomer acquires Reply.ai to enhance chatbots on its CRM platform

Last December, when CRM startup Kustomer was announcing its latest round of funding — a $60 million round led by Coatue — its co-founder and CEO Brad Birnbaum said it would use some of the money to build more RPA-style automations into its platform to expand KustomerIQ, its AI-based product that helps understand and respond to customer enquiries to take some of the more repetitive load off of agents. Today, Kustomer is announcing some M&A that will help in that strategy: it is acquiring Reply.ai, a startup originally founded in Madrid that has built a code-free platform for companies to create customised chatbots to handle customer service enquires that use machine learning to, over time, become better at responding to those inbound contacts.

Kustomer, which has raised more than $170 million and is now valued at $710 million (per PitchBook), said it is not disclosing the financial terms of the deal.

Reply.ai — whose customers include Coca Cola, Starbucks, Samsung, and a number of retailers and major ad and marketing agencies working on behalf of clients — had by comparison raised a modest $4 million in funding (with the last round back in 2018). Its list of investors included strategic backers like Aflac and Westfield (the shopping mall giant), as well as Seedcamp, Madrid’s JME Ventures, and Y Combinator, where Reply.ai was a part of its Startup School cohort in 2017.

Birnbaum said that the conversation for acquiring Reply.ai started before the global health pandemic — the two already worked together, as part of Reply.ai’s integrations with a number of CRM platforms. But active discussions, due diligence, and the closing of the deal were all done over Zoom. “We were fortunate that we got to meet before Corona, but for the most part we did this remotely,” he said.

Reply.ai was founded back in 2016 — the year when chatbots suddenly became all the rage — and it managed to make it through that and then the subsequent the trough of disillusionment, when a lot of the early novelty wore off after they were discovered to be not quite as effective as many had hoped or assumed they would be. One of the reasons for Reply.ai’s survival was that it had proven to be a builder of effective applications in one of the only segments of the market became a willing customer and user of chatbots: customer service.

While a large part of the CRM industry — estimated to be worth some $40 billion in 2019 —  is still based around human interactions, there has been a growing push to leverage advances in AI, cloud services, and use of the Internet as a point of interaction to bring more automation into the process, both to help those who are agents deal with more tricky issues, and to help bring overall costs down for those who rely on customer support as part of their service proposition.

That trend, if anything, is only getting a boost right now. In some cases, agents are unable to work because of social distancing rules in cases where customer queries cannot be handled by remote workers. In others, companies are seeing a lot of financial pressure and are looking to reduce expenses. But at the same time, with more people at home and unable to my physical queries to stores and more, the whole medium of customer support is seeing new levels of usage.

Kustomer has been taking on the bigger names in CRM, including Salesforce (where Birnbaum and his cofounder Jeremy Suriel previously worked), Zendesk and Oracle, by providing a platform that makes it easier for human agents to handle inbound “omni-channel” customer requests — another big trend, leveraging the rise of multiple messaging and communications platforms as potential routes to both speaking to customers and seeing them complain for all the world to see. So moving deeper into chatbots and other AI-powered tools is a natural progression.

Birnbaum said that one of its key interests with Reply.ai was its focus on “deflection” — the term for using non-human tools and services to help resolve inbound requests before needing to call in a human agent. Reply.ai’s tools have been shown to help deflect 40% of initial inbound queries, he noted.

“Some companies have been dealing with a significant increase in inbound volume, and it’s been hard to scale their teams of agents, especially when they are remote,” he said. “So those companies are looking for ways to respond more rapidly. So anything they can do to help with that deflection and let their agents be more productive to drive higher levels of satisfaction, anything that can enable self service, is what this is about.”

Other tools in the Reply toolkit, in addition to its chatbot-building platform and deflection capabilities, include agent assistant tools for suggesting relevant answers, as well as suggestions for tagging (for analytics) and re-routing.

“We are excited for Reply to join Kustomer and share its mission to make customer service more efficient, effective and personalized,” said said Omar Pera, one of Reply.ai’s founders, in a statement. “As a long-time partner of Kustomer, we are able to seamlessly integrate our deflection and chatbots technologies into Kustomer’s platform and help brands more cost-effectively increase efficiency. We look forward to working with Brad and the entire team.”


By Ingrid Lunden