DataFleets keeps private data useful, and useful data private, with federated learning and $4.5M seed

As you may already know, there’s a lot of data out there, and some of it could actually be pretty useful. But privacy and security considerations often put strict limitations on how it can be used or analyzed. DataFleets promises a new approach by which databases can be safely accessed and analyzed without the possibility of privacy breaches or abuse — and has raised a $4.5 million seed round to scale it up.

To work with data, you need to have access to it. If you’re a bank, that means transactions and accounts; if you’re a retailer, that means inventories and supply chains, and so on. There are lots of insights and actionable patterns buried in all that data, and it’s the job of data scientists and their ilk to draw them out.

But what if you can’t access the data? After all, there are many industries where it is not advised or even illegal to do so, such as in health care. You can’t exactly take a whole hospital’s medical records, give them to a data analysis firm, and say “sift through that and tell me if there’s anything good.” These, like many other data sets, are too private or sensitive to allow anyone unfettered access. The slightest mistake — let alone abuse — could have serious repercussions.

In recent years a few technologies have emerged that allow for something better, though: analyzing data without ever actually exposing it. It sounds impossible, but there are computational techniques for allowing data to be manipulated without the user ever actually having access to any of it. The most widely used one is called homomorphic encryption, which unfortunately produces an enormous, orders-of-magnitude reduction in efficiency — and big data is all about efficiency.

This is where DataFleets steps in. It hasn’t reinvented homomorphic encryption, but has sort of sidestepped it. It uses an approach called federated learning, where instead of bringing the data to the model, they bring the model to the data.

DataFleets integrates with both sides of a secure gap between a private database and people who want to access that data, acting as a trusted agent to shuttle information between them without ever disclosing a single byte of actual raw data.

Illustration showing how a model can be created without exposing data.

Image Credits: DataFleets

Here’s an example. Say a pharmaceutical company wants to develop a machine learning model that looks at a patient’s history and predicts whether they’ll have side effects with a new drug. A medical research facility’s private database of patient data is the perfect thing to train it. But access is highly restricted.

The pharma company’s analyst creates a machine learning training program and drops it into DataFleets, which contracts with both them and the facility. DataFleets translates the model to its own proprietary runtime and distributes it to the servers where the medical data resides; within that sandboxed environment, it runs grows into a strapping young ML agent, which when finished is translated back into the analyst’s preferred format or platform. The analyst never sees the actual data, but has all the benefits of it.

Screenshot of the DataFleets interface. Look, it’s the applications that are meant to be exciting.

It’s simple enough, right? DataFleets acts as a sort of trusted messenger between the platforms, undertaking the analysis on behalf of others and never retaining or transferring any sensitive data.

Plenty of folks are looking into federated learning; the hard part is building out the infrastructure for a wide-ranging enterprise-level service. You need to cover a huge amount of use cases and accept an enormous variety of languages, platforms, and techniques, and of course do it all totally securely.

“We pride ourselves on enterprise readiness, with policy management, identity access management, and our pending SOC 2 certification,” said DataFleets COO and co-founder Nick Elledge. “You can build anything on top of DataFleets and plug in your own tools, which banks and hospitals will tell you was not true of prior privacy software.”

But once federated learning is set up, all of a sudden the benefits are enormous. For instance, one of the big issues today in combating COVID-19 is that hospitals, health authorities, and other organizations around the world are having difficulty, despite their willingness, in securely sharing data relating to the virus.

Everyone wants to share, but who sends whom what, where is it kept, and under whose authority and liability? With old methods, it’s a confusing mess. With homomorphic encryption it’s useful but slow. With federated learning, theoretically, it’s as easy as toggling someone’s access.

Because the data never leaves its “home,” this approach is essentially anonoymous and thus highly compliant with regulations like HIPAA and GDPR, another big advantage. Elledge notes: “We’re being used by leading healthcare institutions who recognize that HIPAA doesn’t give them enough protection when they are making a data set available for third parties.”

Of course there are less noble, but no less viable, examples in other industries: wireless carriers could make subscriber metadata available without selling out individuals; banks could sell consumer data without violating anyone in particular’s privacy; bulky datasets like video can sit where they are instead of being duplicated and maintained at great expense.

The company’s $4.5M seed round is seemingly evidence of confidence from a variety of investors (as summarized by Elledge): AME Cloud Ventures (Jerry Yang of Yahoo!) and Morado Ventures, Lightspeed Venture Partners, Peterson Ventures, Mark Cuban, LG, Marty Chavez (President of the Board of Overseers of Harvard), Stanford-StartX fund, and three unicorn founders (Rappi, Quora, and Lucid).

With only 11 full time employees DataFleets appears to be doing a lot with very little, and the seed round should enable rapid scaling and maturation of its flagship product. “We’ve had to turn away or postpone new customer demand to focus on our work with our lighthouse customers,” Elledge said. They’ll be hiring engineers in the U.S. and Europe to help launch the planned self-service product next year.

“We’re moving from a data ownership to a data access economy, where information can be useful without transferring ownership,” said Elledge. If his company’s bet is on target, federated learning is likely to be a big part of that going forward.


By Devin Coldewey

Freshworks (re-)launches its CRM service

Freshworks, the customer and employee engagement company that offers a range of products, from call center and customer support software to HR tools and marketing automation services, today announced the launch of its newest product: Freshworks CRM. The new service, which the company built on top of its new Freshworks Neo platform, is meant to give sales and marketing teams all of the tools they need to get a better view of their customers — with a bit of machine learning thrown in for better predictions.

Freshworks CRM is essentially a rebrand of the company’s Freshsales service, combined with the company’s capabilities of its Freshmarketer marketing automation tool.

“Freshworks CRM unites Freshsales and Freshmarketer capabilities into one solution, which leverages an embedded customer data platform for an unprecedented and 360-degree view of the customer throughout their entire journey,” a company spokesperson told me.

The promise here is that this improved CRM solution is able to provide teams with a more complete view of their (potential) customers thanks to the unified view — and aggregated data — that the company’s Neo platform provides.

The company argues that the majority of CRM users quickly become disillusioned with their CRM service of choice — and the reason for that is because the data is poor. That’s where Freshworks thinks it can make a difference.

Freshworks CRM delivers upon the original promise of CRM: a single solution that combines AI-driven data, insights and intelligence and puts the customer front and center of business goals,” said Prakash Ramamurthy, the company’s chief product officer. “We built Freshworks CRM to harness the power of data and create immediate value, challenging legacy CRM solutions that have failed sales teams with clunky interfaces and incomplete data.”

The idea here is to provide teams with all of their marketing and sales data in a single dashboard and provide AI-assisted insights to them to help drive their decision making, which in turn should lead to a better customer experience — and more sales. The service offers predictive lead scoring and qualification, based on a host of signals users can customize to their needs, as well as Slack and Teams integrations, built-in telephony with call recording to reach out to prospects and more. A lot of these features were already available in Freshsales, too.

“The challenge for online education is the ‘completion rate’. To increase this, we need to understand the ‘Why’ aspect for a student to attend a course and design ‘What’ & ‘How’ to meet the personalized needs of our students so they can achieve their individual goals,” said Mamnoon Hadi Khan, the chief analytics officer at Shaw Academy. “With Freshworks CRM, Shaw Academy can track the entire student customer journey to better engage with them through our dedicated Student Success Managers and leverage AI to personalize their learning experience — meeting their objectives.”

Pricing for Freshworks CRM starts at $29 per user/month and goes up to $125 per user/month for the full enterprise plan with more advanced features.


By Frederic Lardinois

Wrike launches new AI tools to keep your projects on track

Project management service Wrike today announced a major update to its platform at its user conference that includes a lot of new AI smarts for keeping individual projects on track and on time, as well as new solutions for marketers and project management offices in large corporations. In addition, the company also launched a new budgeting feature and tweaks to the overall user experience.

The highlight of the launch, though, is, without doubt, the launch of the new AI and machine learning capabilities in Wrike . With more than 20,000 customers and over 2 million users on the platform, Wrike has collected a trove of data about projects that it can use to power these machine learning models.

Image Credits: Wrike

The way Wrike is now using AI falls into three categories: project risk prediction, task prioritization and tools for speeding up the overall project management workflow.

Figuring out the status of a project and knowing where delays could impact the overall project is often half the job. Wrike can now predict potential delays and alert project and team leaders when it sees events that signal potential issues. To do this, it uses basic information like start and end dates, but more importantly, it looks at the prior outcomes of similar projects to assess risks. Those predictions can then be fed into Wrike’s automation engine to trigger actions that could mitigate the risk to the project.

Task prioritization does what you would expect and helps you figure out what you should focus on right now to help a project move forward. No surprises there.

What is maybe more surprising is that the team is also launching voice commands (through Siri on iOS) and Gmail-like smart replies (in English for iOS and Android). Those aren’t exactly core features of a project management tools, but as the company notes, these features help remove the overall friction and reduce latencies. Another new feature that falls into this category is support for optical character recognition to allow you to scan printed and handwritten notes from your phones and attach them to tasks (iOS only).

“With more employees working from home, work and personal life are becoming intertwined,” the company argues. “As workers use AI in their personal lives, team managers and everyday users expect the smarts they’re accustomed to in consumer devices and apps to help them manage their work as well. Wrike Work Intelligence is the most comprehensive machine learning foundation that taps into tens of millions of work-related user engagements to power cross-functional collaboration to help organizations achieve operational efficiency, create new opportunities and accelerate digital transformation. Teams can focus on the work that matters most, predict and minimize delays, and cut communication latencies.”

Image Credits: Wrike

The other major new feature — at least if you’re in digital marketing — is Wrike’s new ability to pull in data about your campaigns from about 50 advertising, marketing automation and social media tools, which is then displayed inside the Wrike experience. In a fast-moving field, having all that data at your fingertips and right inside the tool where you think about how to manage these projects seems like a smart idea.

Image Credits: Wrike

Somewhat related, Wrike’s new budgeting feature also now makes it easier for teams to keep their projects within budget, using a new built-in rate card to manage project pricing and update their financials.

“We use Wrike for an extensive project management and performance metrics system,” said Shannon Buerk, the CEO of engage2learn, which tested this new budgeting tool. “We have tried other PM systems and have found Wrike to be the best of all worlds: easy to use for everyone and savvy enough to provide valuable reporting to inform our work. Converting all inefficiencies into productive time that moves your mission forward is one of the keys to a culture of engagement and ownership within an organization, even remotely. Wrike has helped us get there.”


By Frederic Lardinois

Egnyte introduces new features to help deal with security/governance during pandemic

The pandemic has put stress on companies dealing with a workforce that is mostly — and sometimes suddenly — working from home. That has led to rising needs for security and governance tooling, something that Egnyte is looking to meet with new features aimed at helping companies cope with file management during the pandemic.

Egnyte is an enterprise file storage and sharing (EFSS) company, though it has added security services and other tools over the years.

“It’s no surprise that there’s been a rapid shift to remote work, which has I believe led to mass adoption of multiple applications running on multiple clouds, and tied to that has been a nonlinear reaction of exponential growth in data security and governance concerns,” Vineet Jain, co-founder and CEO at Egnyte, explained.

There’s a lot of data at stake.

Egnyte’s announcements today are in part a reaction to the changes that COVID has brought, a mix of net-new features and capabilities that were on its road map, but accelerated to meet the needs of the changing technology landscape.

What’s new?

The company is introducing a new feature called Smart Cache to make sure that content (wherever it lives) that an individual user accesses most will be ready whenever they need it.

“Smart Cache uses machine learning to predict the content most likely to be accessed at any given site, so administrators don’t have to anticipate usage patterns. The elegance of the solution lies in that it is invisible to the end users,” Jain said. The end result of this capability could be lower storage and bandwidth costs, because the system can make this content available in an automated way only when it’s needed.

Another new feature is email scanning and governance. As Jain points out, email is often a company’s largest data store, but it’s also a conduit for phishing attacks and malware. So Egnyte is introducing an email governance tool that keeps an eye on this content, scanning it for known malware and ransomware and blocking files from being put into distribution when it identifies something that could be harmful.

As companies move more files around it’s important that security and governance policies travel with the document, so that policies can be enforced on the file wherever it goes. This was true before COVID-19, but has only become more true as more folks work from home.

Finally, Egnyte is using machine learning for auto-classification of documents to apply policies to documents without humans having to touch them. By identifying the document type automatically, whether it has personally identifying information or it’s a budget or planning document, Egnyte can help customers auto-classify and apply policies about viewing and sharing to protect sensitive materials.

Egnyte is reacting to the market needs as it makes changes to the platform. While the pandemic has pushed this along, these are features that companies with documents spread out across various locations can benefit from regardless of the times.

The company is over $100 million ARR today, and grew 22% in the first half of 2020. Whether the company can accelerate that growth rate in H2 2020 is not yet clear. Regardless, Egnyte is a budding IPO candidate for 2021 if market conditions hold.


By Ron Miller

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

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

Strike Graph raises $3.9M to help automate security audits

Compliance automation isn’t exactly the most exciting topic, but security audits are big business and companies that aim to get a SOC 2, ISO 207001 or FedRamp certification can often spend six figures to get through the process with the help of an auditing service. Seattle-based Strike Graph, which is launching today and announcing a $3.9 million seed funding round, wants to automate as much of this process as possible.

The company’s funding round was led by Madrona Venture Group, with participation from Amplify.LA, Revolution’s Rise of the Rest Seed Fund and Green D Ventures.

Strike Graph co-founder and CEO Justin Beals tells me that the idea for the company came to him during his time as CTO at machine learning startup Koru (which had a bit of an odd exit last year). To get enterprise adoption for that service, the company had to get a SOC 2 security certification. “It was a real challenge, especially for a small company. In talking to my colleagues, I just recognized how much of a challenge it was across the board. And so when it was time for the next startup, I was just really curious,” he told me.

Image Credits: Strike Graph

Together with his co-founder Brian Bero, he incubated the idea at Madrona Venture Labs, where he spent some time as Entrepreneur in Residence after  Koru.

Beals argues that today’s process tends to be slow, inefficient and expensive. The idea behind Strike Graph, unsurprisingly, is to remove as many of these inefficiencies as is currently possible. The company itself, it is worth noting, doesn’t provide the actual audit service. Businesses will still need to hire an auditing service for that. But Beals also argues that the bulk of what companies are paying for today is pre-audit preparation.

“We do all that preparation work and preparing you and then, after your first audit, you have to go and renew every year. So there’s an important maintenance of that information.”

Image Credits: Strike Graph

When customers come to Strike Graph, they fill out a risk assessment. The company takes that and can then provide them with controls for how to improve their security posture — both to pass the audit and to secure their data. Beals also noted that soon, Strike Graph will be able to help businesses automate the collection of evidence for the audit (say your encryption settings) and can pull that in regularly. Certifications like SOC 2, after all, require companies to have ongoing security practices in place and get re-audited every 12 months. Automated evidence collection will launch in early 2021, once the team has built out the first set of its integrations to collect that data.

That’s also where the company, which mostly targets mid-size businesses, plans to spend a lot of its new funding. In addition, the company plans to focus on its marketing efforts, mostly around content marketing and educating its potential customers.

“Every company, big or small, that sells a software solution must address a broad set of compliance requirements in regards to security and privacy.  Obtaining the certifications can be a burdensome, opaque and expensive process.  Strike Graph is applying intelligent technology to this problem – they help the company identify the appropriate risks, enable the audit to run smoothly, and then automate the compliance and testing going forward,” said Hope Cochran, Managing Director at Madrona Venture Group. “These audits were a necessary pain when I was a CFO, and Strike Graph’s elegant solution brings together teams across the company to move the business forward faster.”


By Frederic Lardinois

Datasaur snags $3.9M investment to build intelligent machine learning labeling platform

As machine learning has grown, one of the major bottlenecks remains labeling things so the machine learning application understands the data it’s working with. Datasaur, a member of the Y Combinator Winter 2020 batch, announced a $3.9 million investment today to help solve that problem with a platform designed for machine learning labeling teams.

The funding announcement, which includes a pre-seed amount of $1.1 million from last year and $2.8 million seed right after it graduated from Y Combinator in March, included investments from Initialized Capital, Y Combinator and OpenAI CTO Greg Brockman.

Company founder Ivan Lee says that he has been working in various capacities involving AI for seven years. First when his mobile gaming startup, Loki Studios was acquired by Yahoo! in 2013, and Lee was eventually moved to the AI team, and most recently at Apple. Regardless of the company, he consistently saw a problem around organizing machine learning labeling teams, one that he felt he was uniquely situated to solve because of his experience.

“I have spent millions of dollars [in budget over the years] and spent countless hours gathering labeled data for my engineers. I came to recognize that this was something that was a problem across all the companies that I’ve been at. And they were just consistently reinventing the wheel and the process. So instead of reinventing that for the third time at Apple, my most recent company, I decided to solve it once and for all for the industry. And that’s why we started Datasaur last year,” Lee told TechCrunch.

He built a platform to speed up human data labeling with a dose of AI, while keeping humans involved. The platform consists of three parts: a labeling interface, the intelligence component, which can recognize basic things, so the labeler isn’t identifying the same thing over and over, and finally a team organizing component.

He says the area is hot, but to this point has mostly involved labeling consulting solutions, which farm out labeling to contractors. He points to the sale of Figure Eight in March 2019 and to Scale, which snagged $100 million last year as examples of other startups trying to solve this problem in this way, but he believes his company is doing something different by building a fully software-based solution

The company currently offers a cloud and on-prem solution, depending on the customer’s requirements. It has 10 employees with plans to hire in the next year, although he didn’t share an exact number. As he does that, he says he has been working with a partner at investor Initialized on creating a positive and inclusive culture inside the organization, and that includes conversations about hiring a diverse workforce as he builds the company.

“I feel like this is just standard CEO speak but that is something that we absolutely value in our top of funnel for the hiring process,” he said.

As Lee builds out his platform, he has also worried about built-in bias in AI systems and the detrimental impact that could have on society. He says that he has spoken to clients about the role of labeling in bias and ways of combatting that.

“When I speak with our clients, I talk to them about the potential for bias from their labelers and built into our product itself is the ability to assign multiple people to the same project. And I explain to my clients that this can be more costly, but from personal experience I know that it can improve results dramatically to get multiple perspectives on the exact same data,” he said.

Lee believes humans will continue to be involved in the labeling process in some way, even as parts of the process become more automated. “The very nature of our existence [as a company] will always require humans in the loop, […] and moving forward I do think it’s really important that as we get into more and more of the long tail use cases of AI, we will need humans to continue to educate and inform AI, and that’s going to be a critical part of how this technology develops.”


By Ron Miller

Privacy data management innovations reduce risk, create new revenue channels

Privacy data mismanagement is a lurking liability within every commercial enterprise. The very definition of privacy data is evolving over time and has been broadened to include information concerning an individual’s health, wealth, college grades, geolocation and web surfing behaviors. Regulations are proliferating at state, national and international levels that seek to define privacy data and establish controls governing its maintenance and use.

Existing regulations are relatively new and are being translated into operational business practices through a series of judicial challenges that are currently in progress, adding to the confusion regarding proper data handling procedures. In this confusing and sometimes chaotic environment, the privacy risks faced by almost every corporation are frequently ambiguous, constantly changing and continually expanding.

Conventional information security (infosec) tools are designed to prevent the inadvertent loss or intentional theft of sensitive information. They are not sufficient to prevent the mismanagement of privacy data. Privacy safeguards not only need to prevent loss or theft but they must also prevent the inappropriate exposure or unauthorized usage of such data, even when no loss or breach has occurred. A new generation of infosec tools is needed to address the unique risks associated with the management of privacy data.

The first wave of innovation

A variety of privacy-focused security tools emerged over the past few years, triggered in part by the introduction of GDPR (General Data Protection Regulation) within the European Union in 2018. New capabilities introduced by this first wave of innovation were focused in the following three areas:

Data discovery, classification and cataloging. Modern enterprises collect a wide variety of personal information from customers, business partners and employees at different times for different purposes with different IT systems. This data is frequently disseminated throughout a company’s application portfolio via APIs, collaboration tools, automation bots and wholesale replication. Maintaining an accurate catalog of the location of such data is a major challenge and a perpetual activity. BigID, DataGuise and Integris Software have gained prominence as popular solutions for data discovery. Collibra and Alation are leaders in providing complementary capabilities for data cataloging.

Consent management. Individuals are commonly presented with privacy statements describing the intended use and safeguards that will be employed in handling the personal data they supply to corporations. They consent to these statements — either explicitly or implicitly — at the time such data is initially collected. Osano, Transcend.io and DataGrail.io specialize in the management of consent agreements and the enforcement of their terms. These tools enable individuals to exercise their consensual data rights, such as the right to view, edit or delete personal information they’ve provided in the past.


By Walter Thompson

WhyLabs brings more transparancy to ML ops

WhyLabs, a new machine learning startup that was spun out of the Allen Institute, is coming out of stealth today. Founded by a group of former Amazon machine learning engineers, Alessya Visnjic, Sam Gracie and Andy Dang, together with Madrona Venture Group principal Maria Karaivanova, WhyLabs’ focus is on ML operations after models have been trained — not on building those models from the ground up.

The team also today announced that it has raised a $4 million seed funding round from Madrona Venture Group, Bezos Expeditions, Defy Partners and Ascend VC.

Visnjic, the company’s CEO, used to work on Amazon’s demand forecasting model.

“The team was all research scientists, and I was the only engineer who had kind of tier-one operating experience,” she told me. “So it was like, ”Okay, how bad could it be?’ I carried the pager for the retail website before it can be bad. But it was one of the first AI deployments that we’d done at Amazon at scale. The pager duty was extra fun because there were no real tools. So when things would go wrong — like we’d order way too many black socks out of the blue — it was a lot of manual effort to figure out why was this happening.”

Image Credits: WhyLabs

But while large companies like Amazon have built their own internal tools to help their data scientists and AI practitioners operate their AI systems, most enterprises continue to struggle with this — and a lot of AI projects simply fail and never make it into production. “We believe that one of the big reasons that happens is because of the operating process that remains super manual,” Visnjic said. “So at WhyLabs, we’re building the tools to address that — specifically to monitor and track data quality and alert — you can think of it as Datadog for AI applications.”

The team has brought ambitions, but to get started, it is focusing on observability. The team is building — and open-sourcing — a new tool for continuously logging what’s happening in the AI system, using a low-overhead agent. That platform-agnostic system, dubbed WhyLogs, is meant to help practitioners understand the data that moves through the AI/ML pipeline.

For a lot of businesses, Visnjic noted, the amount of data that flows through these systems is so large that it doesn’t make sense for them to keep “lots of big haystacks with possibly some needles in there for some investigation to come in the future.” So what they do instead is just discard all of this. With its data logging solution, WhyLabs aims to give these companies the tools to investigate their data and find issues right at the start of the pipeline.

Image Credits: WhyLabs

According to Karaivanova, the company doesn’t have paying customers yet, but it is working on a number of proofs of concepts. Among those users is Zulily, which is also a design partner for the company. The company is going after mid-size enterprises for the time being, but as Karaivanova noted, to hit the sweet spot for the company, a customer needs to have an established data science team with 10 to 15 ML practitioners. While the team is still figuring out its pricing model, it’ll likely be a volume-based approach, Karaivanova said.

“We love to invest in great founding teams who have built solutions at scale inside cutting-edge companies, who can then bring products to the broader market at the right time. The WhyLabs team are practitioners building for practitioners. They have intimate, first-hand knowledge of the challenges facing AI builders from their years at Amazon and are putting that experience and insight to work for their customers,” said Tim Porter, managing director at Madrona. “We couldn’t be more excited to invest in WhyLabs and partner with them to bring cross-platform model reliability and observability to this exploding category of MLOps.”


By Frederic Lardinois

Microsoft Teams gets breakout rooms, custom layouts and virtual commutes

Unsurprisingly, Teams has become a major focus for Microsoft during the COVID-19 pandemic and so it’s no surprise that the company is using its annual Ignite IT conference to announce a number of new features for the services.

Today’s announcements follow the launch of features like Together Mode and dynamic view earlier this summer.

Together Mode, which puts cutouts of meeting participants in different settings, is getting a bit of an update today with the launch of new scenes: auditoriums, coffee shops and conference rooms. Like before, the presenter chooses the scene, but what’s new now is that Microsoft is also now using machine learning to ensure that participants are automatically centered in their virtual chairs, making the whole scene look just a little bit more natural (and despite what Microsoft’s research shows, I can never help but think that this all looks a bit goofy, maybe because it reminds me of the opening credits of the Muppet Show).

Image Credits: Microsoft

Also new in Teams is custom layouts, which allow presenters to customize how their presentations — and their own video feeds — appear. With this, a presenter can superimpose her own video image over the presentation, for example.

Image Credits: Microsoft

Breakout rooms, a feature that is getting a lot of use in Zoom these days, is now also coming to Teams. Microsoft calls it the most requested feature in Teams and like in similar products, it also meeting organizers to split participants into smaller groups — and the meeting organizer can then go from room to room. Unsurprisingly, this feature is especially popular with teachers, though companies, too, often use it to facilitate brainstorming sessions, for example.

Image Credits: Microsoft

After exhausting all your brainstorming power in those breakout rooms and finishing up your meeting, Teams can now also send you an automatic recap of a meeting that includes a recording, transcript, shared files and more. These recaps will automatically appear on your Outlook calendar. In the future, Microsoft will also enable the ability to automatically store these recordings on SharePoint.

For companies that regularly host large meetings, Microsoft will launch support for up to 1,000 participants in the near future. Attendees in these meetings will get the full Teams experience, Microsoft promises. Later, Microsoft will also enable view-only meetings for up to 20,000 participants. Both of these features will become available as part of a new ‘Advanced Communications’ plan, which is probably no surprise, given how much bandwidth and compute power it will likely take to manage a 1,000-person meetings.

Image Credits: Microsoft

Microsoft also made two hardware announcements related to Team today. The first is the launch of what it calls ‘Microsoft Teams panels,’ which are essentially small tablets that businesses can put outside of their meeting rooms for wayfinding. One cool feature here — especially as business start planning their post-pandemic office strategy — is that these devices will be able to use information from the cameras in the room to count how many people are attending a meeting in person and then show remaining room capacity, for example.

The company also today announced that the giant Surface Hub 2S 85-inch model will be available in January 2021.

And there is more. Microsoft is also launching new Teams features for front-line workers to help schedule shifts, alert workers when they are using Teams off-shift and praise badges that enable organizations to recognize workers (though those workers would probably prefer hard cash over a digital badge).

Also new is an integration between Teams and RealWear head-mounted devices for remote collaboration and a new Walkie Talkie app for Android.

And since digital badges aren’t usually enough to improve employee wellbeing, Microsoft is also adding a new set of wellbeing features to Teams. These provide users with personalized recommendations to help change habits and improve wellbeing and productivity.

Image Credits: Microsoft

That includes a new ‘virtual commute’ feature that includes an integration with Headspace and an emotional check-in experience.

I’ve always been a fan of short and manageable commutes for getting some distance between work and home, but that’s not exactly a thing right now. Maybe Headspace works as an example, but there’s only so much Andy Puddicombe I can take. Still, I think I’ll keep my emotional check-ins to myself, though Microsoft obviously notes that it will keep all of that information private.

And while businesses now care about your emotional wellbeing (because it’s closely related to your productivity), managers mostly care about the wor you get done. For them, Workplace Analytics is coming to Teams, giving “managers line of sight into teamwork norms like after-hours collaboration, focus time, meeting effectiveness, and cross-company connections. These will then be compared to averages among similar teams to provide managers with actionable insights.”

If that doesn’t make your manager happy, what will? Maybe a digital praise badge?


By Frederic Lardinois

ServiceNow updates its workflow automation platform

ServiceNow today announced the latest release of its workflow automation platform. With this, the company is emphasizing a number of new solutions for specific verticals, including for telcos and financial services organizations. This focus on verticals extends the company’s previous efforts to branch out beyond the core IT management capabilities that defined its business during its early years. The company is also adding new features for making companies more resilient in the face of crises, as well as new machine learning-based tools.

Dubbed the ‘Paris’ release, this update also marks one of the first major releases for the company since former SAP CEO Bill McDermott became its president and CEO last November.

“We are in the business of operating on purpose,” McDermott said “And that purpose is to make the world of work work better for people. And frankly, it’s all about people. That’s all CEOs talk about all around the world. This COVID environment has put the focus on people. In today’s world, how do you get people to achieve missions across the enterprise? […] Businesses are changing how they run to drive customer loyalty and employee engagement.”

He argues that at this point, “technology is no longer supporting the business, technology is the business,” but at the same time, the majority of companies aren’t prepared to meet whatever digital disruption comes their way. ServiceNow, of course, wants to position itself as the platform that can help these businesses.

“We are very fortunate at ServiceNow,” CJ Desai, ServiceNow’s Chief Product Officer, said. “We are the critical platform for digital transformation, as our customers are thinking about transforming their companies.”

As far as the actual product updates, ServiceNow is launching a total of six new products. These include new business continuity management features with automated business impact analysis and tools for continuity plan development, as well as new hardware asset management for IT teams and legal service delivery for legal operations teams.

Image Credits: ServiceNow

With specialized solutions for financial services and telco users, the company is also now bringing together some of its existing solutions with more specialized services for these customers. As ServiceNow’s Dave Wright noted, this goes well beyond just putting together existing blocks.

“The first element is actually getting familiar with the business,” he explained. “So the technology, actually building the product, isn’t that hard. That’s relatively quick. But the uniqueness when you look at all of these workflows, it’s the connection of the operations to the customer service side. Telco is a great example. You’ve got the telco network operations side, making sure that all the operational equipment is active. And then you’ve got the business service side with customer service management, looking at how the customers are getting service. Now, the interesting thing is, because we’ve got both things sitting on one platform, we can link those together really easily.”

Image Credits: ServiceNow

On the machine learning side, ServiceNow made six acquisitions in the area in the last four years, Wright noted — and that is now starting to pay off. Specifically, the company is launching its new predictive intelligence workbench with this release. This new service makes it easier for process owners to detect issues, while also suggesting relevant tasks and content to agents, for example, and prioritizing incoming requests automatically. Using unsupervised learning, the system can also identify other kinds of patterns and with a number of pre-built templates, users can build their own solutions, too.

“The ServiceNow advantage has always been one architecture, one data model and one born-in-the-cloud platform that delivers workflows companies need and great experiences employees and customers expect,” said Desai. “The Now Platform Paris release provides smart experiences powered by AI, resilient operations, and the ability to optimize spend. Together, they will provide businesses with the agility they need to help them thrive in the COVID economy.”


By Frederic Lardinois

Transposit scores $35M to build data-driven runbooks for faster disaster recovery

Transposit is a company built by engineers to help engineers, and one big way to help them is to get systems up and running faster when things go wrong — as they always will at some point. Transposit has come up with a way to build runbooks for faster disaster recovery, while using data to update them in an automated fashion.

Today, the company announced a $35 million Series B investment led by Altimeter Capital with participation from existing investors Sutter Hill Ventures, SignalFire and Unusual Ventures. Today’s investment brings the total raised to $50.4 million, according to the company.

Company CEO Divanny Lamas and CTO and founder Tina Huang see technology issues as less an engineering problem and more as a human problem because it’s humans who have to clean up the messes when things go wrong. Huang says forgetting the human side of things is where she thinks technology has gone astray.

“We know that the real superpower of the product is that we focus on the human and the user side of things. And as a result, we’re building an engineering culture that I think is somewhat differentiated,” Huang told TechCrunch.

Transposit is a platform that its core helps manage APIs, connections to other programs, so it starts with a basic understanding of how various underlying technologies work together inside a company. This is essential for a tool that is trying to help engineers in a moment of panic, figure out how to get back to a working state.

When it comes to disaster recovery, there are essentially two pieces: getting the systems working again, then figuring out what happened. For the first piece the company is building data-driven runbooks. By being data-driven, they aren’t static documents. Instead the underlying machine learning algorithms can look at how the engineers recovered and adjust accordingly.

Transposit diaster recovery dashboard

Image Credits: Transposit

“We realized that no one was focusing on what we realize is the root problem here, which is how do I have access to the right set of data to make it easier to reconstruct that timeline, and understand what happened? We took those two pieces together, this notion that runbooks are a critical piece of how you spread knowledge and spread process, and this other. piece, which is the data, is critical, Huang said.

Today the company has 26 employees including Huang and Lamas who Huang brought on board from Splunk last year to be CEO. The company is somewhat unique having two women running the organization, and they are trying to build a diverse workforce as they build their company to 50 people in the next 12 months.

The current make-up is 47% female engineers, and the goal is to remain diverse as they build the company, something that Lamas admits is challenging to do. “I wish I had a magic answer, or that Tina had a magic answer. The reality is that we’re just very demanding on recruiters. And we are very insistent that we have a diverse pipeline of candidates, and are constantly looking at our numbers and looking at how we’re doing,” Lamas said.

She says being diverse actually makes it easier to recruit good candidates. “People want to work at diverse companies. And so it gives us a real edge from a kind of culture perspective, and we find that we get really amazing candidates that are just tired of the status quo. They’re tired of the old way of doing things and they want to work in a company that reflects the world that they want to live in,” she said.

The company, which launched in 2016, took a few years to build the first piece, the underlying API platform. This year it added the disaster recovery piece on top of that platform, and has been running it beta since the beginning of the summer. They hope to add additional beta customers before making it generally available later this year.


By Ron Miller

Salesforce beefing up field service offering with AI

Salesforce has been adding artificial intelligence to all parts of its platform for several years now. It calls the underlying artificial intelligence layer on the Salesforce platform Einstein. Today the company announced some enhancements to its field service offering that take advantage of this capability.

Eric Jacobson, VP of product management at Salesforce says that when COVID hit, it pretty much stopped field service in its tracks during April, but like many other parts of business, it began to pick up again later in the quarter, and people still needed to have their appliances maintained.

“Even though we’re sheltering in place, the physical world still has physical needs. Hospitals still have to maintain their equipment. Employees still need to have equipment replaced or repaired while working at home, and people still need their washing machine [or other appliances] repaired,” Jacobson said.

Today’s announcements are designed in some ways for a COVID world where efficiency is more critical than ever. That means the field service tech needs to be prepared ahead of time on all of the details of the nature of the repair. He or she has to have the right parts and customers need to know when their technician will be there.

While it’s possible to do much of that in a manual fashion, adding a dose of AI helps streamline and scale that process. For starters, the company announced Dynamic Priority. Certainly humans are capable of prioritizing a list of repairs, but by letting the machine set priority based on factors like service agreement type or how critical the repair is, it can organize calls much faster, leaving dispatchers to handle other tasks.

Even before the day starts, technicians receive their schedule and using machine learning, can determine what parts they are most likely to need in the truck for the day’s repairs. Based on the nature of the repair and the particular make and model of machine, the Einstein Recommendation Builder can help predict the parts that will be needed to minimize the number of required trips, something that is important at all times, but especially during a pandemic.

“It’s always been an inconvenience and annoyance to have somebody come back for a follow up appointment. But now it’s not just an annoyance, it’s actually a safety consideration for you and for the technician because it’s increased exposure,” Jacobson explained

Salesforce also wants to give the customer the same capability, they are used to getting in a ride share app, where you can track the progress of the driver to your destination. Appointment Assistant, a new app gives customers this ability, so they know when to expect the repair person to arrive.

Finally, Salesforce has teamed with ServiceMax to offer a new capability to get the big picture view of an asset with the goal of ensuring uptime, particularly important in settings like hospitals or manufacturing. “We’ve partnered with a long-time Salesforce partner ServiceMax to create a brand new offering that takes industry best practice and builds it right in. Asset 360 builds on top of Salesforce field service and delivers those specific capabilities around asset performance insight, viewing and managing up time and managing warranty processes to really ensure availability,” he said.

As with all Salesforce announcements, the availability of these capabilities will vary as each in various forms of development. “Dynamic Priority will be generally available in October 2020. Einstein Recommendation Builder will be in beta in October 2020. Asset 360 will be generally available in November 2020. Appointment Assistant will be in closed pilot in US in October 2020,” according to information provided by the company.


By Ron Miller

12 Paris-based VCs look at the state of their city

Four years after the Great Recession, France’s newly elected socialist president François Hollande raised taxes and increased regulations on founder-led startups. The subsequent flight of entrepreneurs to places like London and Silicon Valley portrayed France as a tough place to launch a company. By 2016, France’s national statistics bureau estimated that about three million native-born citizens had moved abroad.

Those who remained fought back: The Family was an early accelerator that encouraged French entrepreneurs to adopt Silicon Valley’s startup methodology, and the 2012 creation of Bpifrance, a public investment bank, put money into the startup ecosystem system via investors. Organizers founded La French Tech to beat the drum about native startups.

When President Emmanuel Macron took office in May 2017, he scrapped the wealth tax on everything except property assets and introduced a flat 30% tax rate on capital gains. Station F, a giant startup campus funded by billionaire entrepreneur Xavier Niel on the site of a former railway station, began attracting international talent. Tony Fadell, one of the fathers of the iPod and founder of Nest Labs, moved to Paris to set up investment firm Future Shape; VivaTech was created with government backing to become one of Europe’s largest startup conference and expos.

Now, in the COVID-19 era, the government has made €4 billion available to entrepreneurs to keep the lights on. According to a recent report from VC firm Atomico, there are 11 unicorns in France, including BlaBlaCar, OVHcloud, Deezer and Veepee. More appear to be coming; last year Macron said he wanted to see “25 French unicorns by 2025.”

According to Station F, by the end of August, there had been 24 funding rounds led by international VCs and a few big transactions. Enterprise artificial intelligence and machine-learning platform Dataiku raised a $100 million Series D round, and Paris-based gaming startup Voodoo raised an undisclosed amount from Tencent Holdings.

We asked 12 Paris -based investors to comment on the state of play in their city:

Alison Imbert, Partech

What trends are you most excited about investing in, generally?

All the fintechs addressing SMBs to help them to focus more on their core business (including banks disintermediation by fintech, new infrastructures tech that are lowering the barrier to entry to nonfintech companies).

What’s your latest, most exciting investment?

77foods (plant-based bacon) — love that alternative proteins trend as well. Obviously, we need to transform our diet toward more sustainable food. It’s the next challenge for humanity.

What are you looking for in your next investment, in general?
Impact investment: Logistic companies tackling the life cycle of products to reduce their carbon footprint and green fintech that reinvent our spending and investment strategy around more sustainable products.

Which areas are either oversaturated or would be too hard to compete in at this point for a new startup? What other types of products/services are you wary or concerned about?
D2C products.

How much are you focused on investing in your local ecosystem versus other startup hubs (or everywhere) in general? More than 50%? Less?
100% investing in France as I’m managing Paris Saclay Seed Fund, a €53 million fund, investing in pre-seed and seed startups launched by graduates and researchers from the best engineering and business schools from this ecosystem.

Which industries in your city and region seem well-positioned to thrive, or not, long term? What are companies you are excited about (your portfolio or not), which founders?
Deep tech, biotech and medical devices. Paris, and France in general, has thousands of outstanding engineers that graduate each year. Researchers are more and more willing to found companies to have a true impact on our society. I do believe that the ecosystem is more and more structured to help them to build such companies.

How should investors in other cities think about the overall investment climate and opportunities in your city?
Paris is booming for sure. It’s still behind London and Berlin probably. But we are seeing more and more European VC offices opening in the city to get direct access to our ecosystem. Even in seed rounds, we start to have European VCs competing against us. It’s good — that means that our startups are moving to the next level.

Do you expect to see a surge in more founders coming from geographies outside major cities in the years to come, with startup hubs losing people due to the pandemic and lingering concerns, plus the attraction of remote work?
For sure startups will more and more push for remote organizations. It’s an amazing way to combine quality of life for employees and attracting talent. Yet I don’t think it will be the majority. Not all founders are willing/able to build a fully remote company. It’s an important cultural choice and it’s adapted to a certain type of business. I believe in more flexible organization (e.g., tech team working remotely or 1-2 days a week for any employee).

Which industry segments that you invest in look weaker or more exposed to potential shifts in consumer and business behavior because of COVID-19? What are the opportunities startups may be able to tap into during these unprecedented times?
Travel and hospitality sectors are of course hugely impacted. Yet there are opportunities for helping those incumbents to face current challenges (e.g., better customer care and services, stronger flexibility, cost reduction and process automation).

How has COVID-19 impacted your investment strategy? What are the biggest worries of the founders in your portfolio? What is your advice to startups in your portfolio right now?
Cash is king more than ever before. My only piece of advice will be to keep a good level of cash as we have a limited view on events coming ahead. It’s easy to say but much more difficult to put in practice (e.g., to what extend should I reduce my cash burn? Should I keep on investing in the product? What is the impact on the sales team?). Startups should focus only on what is mission-critical for their clients. Yet it doesn’t impact our seed investments as we invest pre-revenue and often pre-product.

What is a moment that has given you hope in the last month or so? This can be professional, personal or a mix of the two.
There is no reason to be hopeless. Crises have happened in the past. Humanity has faced other pandemics. Humans are resilient and resourceful enough to adapt to a new environment and new constraints.


By Mike Butcher