Juniper Networks acquires Boston-area AI SD-WAN startup 128 Technology for $450M

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

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

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

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

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

128 Technologies was founded in 2014 and raised over $97 million, according to Crunchbase data. Its most recent round was a $30 million Series D investment in September 2019 led by G20 Ventures and The Perkins Fund.

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


By Ron Miller

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

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

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

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

Image Credits: Dataloop

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

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

Image Credits: Dataloop

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


By Frederic Lardinois

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


By Ingrid Lunden

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

As it closes in on ARM, Nvidia announces UK supercomputer dedicated to medical research

As Nvidia continues to work through its deal to acquire ARM for $40 billion from SoftBank, the computing giant is making another big move to lay out its commitment to investing in UK technology. Today the company announced plans to develop Cambridge-1, a new AI supercomputer that will be used for research in the health industry in the country, the first supercomputer built by Nvidia specifically for external research access, it said.

Nvidia said it is already working with GSK, AstraZeneca, London hospitals Guy’s and St Thomas’ NHS Foundation Trust, King’s College London and Oxford Nanopore to use the Cambridge-1. The supercomputer is due to come online by the end of the year and will be the company’s second supercomputer in the country. The first is already in development at the company’s AI Center of Excellence in Cambridge, and the plan is to add more supercomputers over time.

The growing role of AI has underscored an interesting crossroads in medical research. One one hand, leading researchers all acknowledge the role it will be playing in their work. On the other, none of them and their institutions have the resources to meet that demand on their own. That’s driving them all to get involved much more deeply with big tech companies like Google, Microsoft and in this case Nvidia, to carry out work.

Alongside the supercomputer news, Nvidia is making a second announcement in the area of healthcare in the UK: it has inked a partnership with GSK, which has established an AI hub in London, to build AI-based computational processes that will be using in drug vaccine and discovery — an especially timely piece of news, given that we are in a global health pandemic and all drug makers and researchers are on the hunt to understand more about, and build vaccines for, Covid-19.

The news is coinciding with Nvidia’s industry event, the GPU Technology Conference.

“Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI,” said Jensen Huang, founder and CEO of NVIDIA, will say in his keynote at the event. “The Cambridge-1 supercomputer will serve as a hub of innovation for the U.K., and further the groundbreaking work being done by the nation’s researchers in critical healthcare and drug discovery.”

The company plans to dedicate Cambridge-1 resources in four areas, it said: industry research, in particular joint research on projects that exceed the resources of any single institution; university-granted compute time; health-focused AI startups; and education for future AI practitioners. It’s already building specific applications in areas, like the drug discovery work it’s doing with GSK, that will be run on the machine.

The Cambridge-1 will be built on Nvidia’s DGX SuperPOD system, which can process 400 petaflops of AI performance and 8 petaflops of Linpack performance. Nvidia said this will rank it as the 29th fastest supercomputer in the world.

“Number 29” doesn’t sound very groundbreaking, but there are other reasons why the announcement is significant.

For starters, it underscores how the supercomputing market — while still not a mass-market enterprise — is increasingly developing more focus around specific areas of research and industries. In this case, it underscores how health research has become more complex, and how applications of artificial intelligence have both spurred that complexity but, in the case of building stronger computing power, also provides a better route — some might say one of the only viable routes in the most complex of cases — to medical breakthroughs and discoveries.

It’s also notable that the effort is being forged in the UK. Nvidia’s deal to buy ARM has seen some resistance in the market — with one group leading a campaign to stop the sale and take ARM independent — but this latest announcement underscores that the company is already involved pretty deeply in the UK market, bolstering Nvidia’s case to double down even further. (Yes, chip reference designs and building supercomputers are different enterprises, but the argument for Nvidia is one of commitment and presence.)

“AI and machine learning are like a new microscope that will help scientists to see things that they couldn’t see otherwise,” said Dr. Hal Barron, Chief Scientific Officer and President, R&D, GSK, in a statement. “NVIDIA’s investment in computing, combined with the power of deep learning, will enable solutions to some of the life sciences industry’s greatest challenges and help us continue to deliver transformational medicines and vaccines to patients. Together with GSK’s new AI lab in London, I am delighted that these advanced technologies will now be available to help the U.K.’s outstanding scientists.”

“The use of big data, supercomputing and artificial intelligence have the potential to transform research and development; from target identification through clinical research and all the way to the launch of new medicines,” added James Weatherall, PhD, Head of Data Science and AI, Astrazeneca, in his statement.

“Recent advances in AI have seen increasingly powerful models being used for complex tasks such as image recognition and natural language understanding,” said Sebastien Ourselin, Head, School of Biomedical Engineering & Imaging Sciences at King’s College London. “These models have achieved previously unimaginable performance by using an unprecedented scale of computational power, amassing millions of GPU hours per model. Through this partnership, for the first time, such a scale of computational power will be available to healthcare research – it will be truly transformational for patient health and treatment pathways.”

Dr. Ian Abbs, Chief Executive & Chief Medical Director of Guy’s and St Thomas’ NHS Foundation Trust Officer, said: “If AI is to be deployed at scale for patient care, then accuracy, robustness and safety are of paramount importance. We need to ensure AI researchers have access to the largest and most comprehensive datasets that the NHS has to offer, our clinical expertise, and the required computational infrastructure to make sense of the data. This approach is not only necessary, but also the only ethical way to deliver AI in healthcare – more advanced AI means better care for our patients.”

“Compact AI has enabled real-time sequencing in the palm of your hand, and AI supercomputers are enabling new scientific discoveries in large-scale genomic datasets,” added Gordon Sanghera, CEO, Oxford Nanopore Technologies. “These complementary innovations in data analysis support a wealth of impactful science in the UK, and critically, support our goal of bringing genomic analysis to anyone, anywhere.”

 


By Ingrid Lunden

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

Ripjar, founded by GCHQ alums, raises $36.8M for AI that detects financial crime

Financial crime as a wider category of cybercrime continues to be one of the most potent of online threats, covering nefarious actives as diverse as fraud, money laundering and funding terrorism. Today, one of the startups that has been building data intelligence solutions to help combat that is announcing a fundraise to continue fueling its growth.

Ripjar, a UK company founded by five data scientists who previously worked together in British intelligence at the Government Communications Headquarters (GCHQ, the UK’s equivalent of the NSA), has raised $36.8 million (£28 million) in a Series B, money that it plans to use to continue expanding the scope of its AI platform — which it calls Labyrinth — and scaling the business.

Labyrinth, as Ripjar describes it, works with both structured and unstructured data, using natural language processing and an API-based platform that lets organizations incorporate any data source they would like to analyse and monitor for activity.

Sources close to the company say that the funding values the startup in the region of £100 million, or about $127 million. Ripjar is currently profitable, the company confirmed.

The funding is being led by Long Ridge Equity Partners, a specialist fintech investor, with previous investors Winton Capital Ltd and Accenture plc also participating. Accenture is a strategic partner: the consultancy/systems integrator uses Ripjar’s tech to work with a number of clients in the financial services sector. Ripjar also has government clients, where its platform is used for counterterrorism work. It declines to disclose any specific names but it does note that its extensive partner list also includes the likes of PWC, BAE Systems, Dow Jones and more.

“We are excited to partner with Long Ridge who bring expertise and resources in scaling fast-growing software companies,” said Jeremy Annis, the co-founder who is both the CEO and CTO of Ripjar. “This investment signals enormous confidence in our world-leading data intelligence technology and ability to protect companies and governments from criminal behaviour which threatens their assets and prosperity. With this funding, we will accelerate the expansion of Ripjar worldwide to provide our customers with the most advanced financial crime solutions, as well as creating new iterations of the Labyrinth platform.”

The startup says that it’s had its biggest year yet — no surprise, given the circumstances. Not only has there been huge shift to online transactions in 2020 because of the rise of the Covid-19 global health pandemic; but a tightening of the world economy has led to more financial scrambling and new nefarious activity, as well as criminal acts to profit from the instability.

That’s led to inking deals with six new enterprise customers and expanding deals with four existing major clients, and Ripjar said that it now has some 20,000 clients globally.

London, as one of the world’s financial centers, has developed a strong reputation for hatching and growing interesting fintech startups, and that has also meant the UK — which also has a strong talent base in artificial intelligence — has become very fertile ground also for startups building services to help protect those fintechs.

Ripjar’s raise, and rise, come within months of two other companies building AI to combat fraud and financial crime also raising money and growing. In July, ComplyAdvantage, which has also been building a database and platform to help combat financial crime, announced a $50 million raise. And a week before that, another UK company also building AI for financial and other cybercrime detection, Quantexa, raised $64.7 million.

Ripjar counts both of these, as well as bigger targets like Palantir, among its competitors. As is most likely, the big institutions that are grappling with financial crime are most likely using a several companies’ technology at the same time.

Indeed, with the issue of money laundering alone a $2 trillion problem (with only 1-2% of that ever identified and recovered), you can see why, at least for right now, banks, governments and others would be willing to put multiple resources on the problem to try to tackle it.

“Financial institutions, corporates and government agencies face ever-increasing risks associated with financial crime and cyber threats” said Kevin Bhatt, a Managing Partner at Long Ridge, in a statement. “We believe Ripjar is well-positioned to provide artificial intelligence solutions that will allow its clients to reduce the cost of compliance, while uncovering new threats through automation. We are incredibly excited to partner with Ripjar to support their continued growth and look forward to working closely with the Ripjar team as they expand to new geographies, customers, and verticals.”


By Ingrid Lunden

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 launches new Cortana features for business users

Cortana may have failed as a virtual assistant for consumers, but Microsoft is still betting on it (or at least its brand) for business use cases, now that it has rebranded it as a ‘personal productivity assistant’ as part of Microsoft 365. Today, at its Ignite conference, Microsoft launched and announced a number of new Cortana services for business users.

These include the general availability of Cortana for the new Microsoft Teams displays the company is launching in partnership with a number of hardware vendors. You can think of these as dedicated smart displays for Teams that are somewhat akin to Google Assistant-enabled smart displays, for example — but with the sole focus on meetings. These days, it’s hard to enable a device like this without support for a voice assistant, so there you go. It’ll be available in September in English in the U.S. and will then roll out to Australia, Canada, the UK and India in the coming months.

In addition to these Teams devices, which Microsoft is not necessarily positioning for meeting rooms but as sidekicks to a regular laptop or desktop, Cortana will also soon come to Teams Rooms devices. Once we go back to offices and meeting rooms, after all, few people will want to touch a shared piece of hardware, so a touchless experience is a must.

For a while now, Microsoft has also been teasing more email-centric Cortana services. Play My Emails, a service that reads you your email out aloud and that’s already available in the U.S. on iOS and Android is coming to n Australia, Canada, the UK and India in the coming months. But more importantly, later this month, Outlook for iOS users will be able to interact with their inbox by voice, initiate calls to email senders and play emails from specific senders.

Cortana can now also send you daily briefing emails if you are a Microsoft 365 Enterprise users. This feature is now generally available and will get better meeting preparation, an integration with Microsoft To Do and other new features in the coming months.

And if you’re using Cortana on Windows 10, this chat-based app now let you compose emails, for example (at least if you speak English and are in the U.S.). And if you so desire, you can now use a wake word to launch it.


By Frederic Lardinois

EasySend raises $16M from Intel, more for its no-code approach to automating B2C interfaces

No-code and low-code software have become increasingly popular ways for companies — especially those that don’t count technology as part of their DNA — to bring in more updated IT processes without the heavy lifting needed to build and integrate services from the ground up.

As a mark of that trend, today, a company that has taken this approach to speeding up customer experience is announcing some funding. EasySend, an Israeli startup which has built a no-code platform for insurance companies and other regulated businesses to build out forms and other interfaces to take in customer information and subsequently use AI systems to process it more efficiently, is announcing that it has raised $16 million.

The funding has actually come in two tranches, a $5 million seed round from Vertex Ventures and Menora Insurance that it never disclosed, and another $11 million round that closed more recently, led by Hanaco with participation from Intel Capital. The company is already generating revenue, and did so from the start, enough that it was actually bootstrapped for the first three years of its life.

Tal Daskal, EasySend’s CEO and co-founder, said that the funding being announced today will be used to help it expand into more verticals: up to now its primary target has been insurance companies, although organically it’s picked up customers from a number of other verticals, such as telecoms carriers, banks and more.

The plan will be now to hone in on specifically marketing to and building solutions for the financial services sector, as well as hiring and expanding in Asia, Europe and the US.

Longer term, he said, that another area EasySend might like to look at more in the future is robotic process automation (RPA). RPA, and companies that deal in it like UIPath, Automation Anywhere and Blue Prism, is today focused on the back office, and EasySend’s focus on the “front office” integrates with leaders in that area. But over time, it would make sense for EasySend to cover this in a more holistic way, he added.

Menora was a strategic backer: it’s one of the largest insurance providers in Israel, Daskal said, and it used EasySend to build out better ways for consumers to submit data for claims and apply for insurance.

Intel, he said, is also strategic although how is still being worked out: what’s notable to mention here is that Intel has been building out a huge autonomous driving business in Israel, anchored by MobileEye, and not only will insurance (and overall risk management) play a big part in how that business develops, but longer term you can see how there will be a need for a lot of seamless customer interactions (and form filling) between would-be car owners, operators, and passengers in order for services to operate more efficiently.

“Intel Capital chose to invest in EasySend because of its intelligent and impactful approach to accelerating digital transformation to improve customer experiences,” said Nick Washburn, senior managing director, Intel Capital, in a statement. “EasySend’s no-code platform utilizes AI to digitize thousands of forms quickly and easily, reducing development time from months to days, and transforming customer journeys that have been paper-based, inefficient and frustrating. In today’s world, this is more critical than ever before.”

The rise and persistence of Covid-19 globally has had a big, multi-faceted impact how we all do business, and two of those ways have fed directly into the growth of EasySend.

First, the move to remote working has given organizations a giant fillip to work on digital transformation, refreshing and replacing legacy systems with processes that work faster and rely on newer technologies.

Second, consumers have really reassessed their use of insurance services, specifically health and home policies, respectively to make sure they are better equipped in the event of a Covid-19-precipitated scare, and to make sure that they are adequately covered for how they now use their homes all hours of the day.

EasySend’s platform for building and running interfaces for customer experience fall directly into the kinds of apps and services that are being identified and updated, precisely at a time when its initial target customers, insurers, are seeing a surge in business. It’s that “perfect storm” of circumstances that the startup wouldn’t have wished on the world, but which has definitely helped it along.

While there are a lot of companies on the market today that help organizations automate and run their customer interaction processes, the Daskal said that EasySend’s focus on using AI to process information is what makes the startup more unique, as it can be used not just to run things, but to help improve how things work.

It’s not just about taking in character recognition and organizing data, it’s “understanding the business logic,” he said. “We have a lot of data and we can understand [for example] where customers left the process [when filling out forms]. We can give insights into how to increase the conversion rates.”

It’s that balance of providing tools to do business better today, as well as to focus on how to build more business for tomorrow, that has caught the eye of investors.

“Hanaco is firmly invested in building a digital future. By bridging the gap between manual processes and digitization, EasySend is making this not only possible, but also easy, affordable, and practical,” said Hanaco founding partner Alon Lifshitz, in a statement.


By Ingrid Lunden

Luther.AI is a new AI tool that acts like Google for personal conversations

When it comes to pop culture, a company executive or history questions, most of us use Google as a memory crutch to recall information we can’t always keep in our heads, but Google can’t help you remember the name of your client’s spouse or the great idea you came up with at a meeting the other day.

Enter Luther.AI, which purports to be Google for your memory by capturing and transcribing audio recordings, while using AI to deliver the right information from your virtual memory bank in the moment of another online conversation or via search.

The company is releasing an initial browser-based version of their product this week at TechCrunch Disrupt where it’s competing for the $100,000 prize at TechCrunch Disrupt Battlefield.

Luther.AI’s founders say the company is built on the premise that human memory is fallible, and that weakness limits our individual intelligence. The idea behind Luther.AI is to provide a tool to retain, recall and even augment our own brains.

It’s a tall order, but the company’s founders believe it’s possible through the growing power of artificial intelligence and other technologies.

“It’s made possible through a convergence of neuroscience, NLP and blockchain to deliver seamless in-the-moment recall. GPT-3 is built on the memories of the public internet, while Luther is built on the memories of your private self,” company founder and CEO Suman Kanuganti told TechCrunch.

It starts by recording your interactions throughout the day. For starters, that will be online meetings in a browser, as we find ourselves in a time where that is the way we interact most often. Over time though, they envision a high-quality 5G recording device you wear throughout your day at work and capture your interactions.

If that is worrisome to you from a privacy perspective, Luther is building in a few safeguards starting with high-end encryption. Further, you can only save other parties’ parts of a conversation with their explicit permission. “Technologically, we make users the owner of what they are speaking. So for example, if you and I are having a conversation in the physical world unless you provide explicit permission, your memories are not shared from this particular conversation with me,” Kanuganti explained.

Finally, each person owns their own data in Luther and nobody else can access or use these conversations either from Luther or any other individual. They will eventually enforce this ownership using blockchain technology, although Kanuganti says that will be added in a future version of the product.

Luther.ai search results recalling what person said at meeting the other day about customer feedback.

Image Credits: Luther.ai

Kanuganti says the true power of the product won’t be realized with a few individuals using the product inside a company, but in the network effect of having dozens or hundreds of people using it, even though it will have utility even for an individual to help with memory recall, he said.

While they are releasing the browser-based product this week, they will eventually have a stand-alone app, and can also envision other applications taking advantage of the technology in the future via an API where developers can build Luther functionality into other apps.

The company was founded at the beginning of this year by Kanuganti and three co-founders including CTO Sharon Zhang, design director Kristie Kaiser and scientist Marc Ettlinger. It has raised $500,000 and currently has 14 employees including the founders.


By Ron Miller

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

In 2020, Warsaw’s startup ecosystem is ‘a place to observe carefully’

If you listed the trends that have captured the attention of 20 Warsaw-focused investors who replied to our recent surveys, automation/AI, enterprise SaaS, cleantech, health, remote work and the sharing economy would top the list. These VCs said they are seeking opportunities in the “digital twin” space, proptech and expanded blockchain tokenization inside industries.

Investors in Central and Eastern Europe are generally looking for the same things as VCs based elsewhere: startups that have a unique value proposition, capital efficiency, motivated teams, post-revenue and a well-defined market niche.

Out of the cohort we interviewed, several told us that COVID-19 had not yet substantially transformed how they do business. As Michał Papuga, a partner at Flashpoint VC put it, “the situation since March hasn’t changed a lot, but we went from extreme panic to extreme bullishness. Neither of these is good and I would recommend to stick to the long-term goals and not to be pressured.”

Said Pawel Lipkowski of RBL_VC, “Warsaw is at its pivotal point — think Berlin in the ‘90s. It’s a place to observe carefully.”

Here’s who we interviewed for part one:

For the conclusion, we spoke to the following investors:

Karol Szubstarski, partner, OTB Ventures

What trends are you most excited about investing in, generally?
Gradual shift of enterprises toward increased use of automation and AI, that enables dramatic improvement of efficiency, cost reduction and transfer of enterprise resources from tedious, repeatable and mundane tasks to more exciting, value added opportunities.

What’s your latest, most exciting investment?
One of the most exciting opportunities is ICEYE. The company is a leader and first mover in synthetic-aperture radar (SAR) technology for microsatellites. It is building and operating its own commercial constellation of SAR microsatellites capable of providing satellite imagery regardless of the cloud cover, weather conditions and time of the day and night (comparable resolution to traditional SAR satellites with 100x lower cost factor), which is disrupting the multibillion dollar satellite imagery market.

Are there startups that you wish you would see in the industry but don’t? What are some overlooked opportunities right now?
I would love to see more startups in the digital twin space; technology that enables creation of an exact digital replica/copy of something in physical space — a product, process or even the whole ecosystem. This kind of solution enables experiments and [the implementation of] changes that otherwise could be extremely costly or risky – it can provide immense value added for customers.

What are you looking for in your next investment, in general?
A company with unique value proposition to its customers, deep tech component that provides competitive edge over other players in the market and a founder with global vision and focus on execution of that vision.

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?
No market/sector is too saturated and has no room for innovation. Some markets seem to be more challenging than others due to immense competitive landscape (e.g., food delivery, language-learning apps) but still can be the subject of disruption due to a unique value proposition of a new entrant.

How much are you focused on investing in your local ecosystem versus other startup hubs (or everywhere) in general? More than 50%? Less?
OTB is focused on opportunities with links to Central Eastern European talent (with no bias toward any hub in the region), meaning companies that leverage local engineering/entrepreneurial talent in order to build world-class products to compete globally (usually HQ outside CEE).

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?
CEE region is recognized for its sizable and highly skilled talent pool in the fields of engineering and software development. The region is well-positioned to build up solutions that leverage deep, unique tech regardless of vertical (especially B2B). Historically, the region was especially strong in AI/ML, voice/speech/NLP technologies, cybersecurity, data analytics, etc.

How should investors in other cities think about the overall investment climate and opportunities in your city?
CEE (including Poland and Warsaw) has always been recognized as an exceptionally strong region in terms of engineering/IT talent. Inherent risk aversion of entrepreneurs has driven, for a number of years, a more “copycat”/local market approach, while holding back more ambitious, deep tech opportunities. In recent years we are witnessing a paradigm shift with a new generation of entrepreneurs tackling problems with unique, deep tech solutions, putting emphasis on global expansion, neglecting shallow local markets. As such, the quality of deals has been steadily growing and currently reflects top quality on global scale, especially on tech level. CEE market demonstrates also a growing number of startups (in total), which is mostly driven by an abundance of early-stage capital and success stories in the region (e.g., DataRobot, Bolt, UiPath) that are successfully evangelizing entrepreneurship among corporates/engineers.

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?
I believe that local hubs will hold their dominant position in the ecosystem. The remote/digital workforce will grow in numbers but proximity to capital, human resources and markets still will remain the prevalent force in shaping local startup communities.

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?
OTB invests in general in companies with clearly defined technological advantage, making quantifiable and near-term difference to their customers (usually in the B2B sector), which is a value-add regardless of the market cycle. The economic downturn works generally in favor of technological solutions enabling enterprise clients to increase efficiency, cut costs, bring optimization and replace manual labour with automation — and the vast majority of OTB portfolio fits that description. As such, the majority of the OTB portfolio has not been heavily impacted by the COVID pandemic.

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?
The COVID pandemic has not impacted our investment strategy in any way. OTB still pursues unique tech opportunities that can provide its customers with immediate value added. This kind of approach provides a relatively high level of resilience against economic downturns (obviously, sales cycles are extending but in general sales pipeline/prospects/retention remains intact). Liquidity in portfolio is always the number one concern in uncertain, challenging times. Lean approach needs to be reintroduced, companies need to preserve cash and keep optimizing — that’s the only way to get through the crisis.

Are you seeing “green shoots” regarding revenue growth, retention or other momentum in your portfolio as they adapt to the pandemic?
A good example in our portfolio is Segron, a provider of an automated testing platform for applications, databases and enterprise network infrastructure. Software development, deployment and maintenance in enterprise IT ecosystem requires continuous and rigorous testing protocols and as such a lot of manual heavy lifting with highly skilled engineering talent being involved (which can be used in a more productive way elsewhere). The COVID pandemic has kept engineers home (with no ability for remote testing) while driving demand for digital services (and as such demand for a reliable IT ecosystem). The Segron automated framework enables full automation of enterprise testing leading to increased efficiency, cutting operating costs and giving enterprise customers peace of mind and a good night’s sleep regarding their IT infrastructure in the challenging economic environment.

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.
I remain impressed by the unshakeable determination of multiple founders and their teams to overcome all the challenges of the unfavorable economic ecosystem.


By Mike Butcher

Latent AI makes edge AI workloads more efficient

Latent AI, a startup that was spun out of SRI International, makes it easier to run AI workloads at the edge by dynamically managing workloads as necessary.

Using its proprietary compression and compilation process, Latent AI promises to compress library files by 10x and run them with 5x lower latency than other systems, all while using less power thanks to its new adaptive AI technology, which the company is launching as part of its appearance in the TechCrunch Disrupt Battlefield competition today.

Founded by CEO Jags Kandasamy and CTO Sek Chai, the company has already raised a $6.5 million seed round led by Steve Jurvetson of Future Ventures and followed by Autotech Ventures .

Before starting Latent AI, Kandasamy sold his previous startup OtoSense to Analog Devices (in addition to managing HPE Mid-Market Security business before that). OtoSense used data from sound and vibration sensors for predictive maintenance use cases. Before its sale, the company worked with the likes of Delta Airlines and Airbus.

Image Credits: Latent AI

In some ways, Latent AI picks up some of this work and marries it with IP from SRI International .

“With OtoSense, I had already done some edge work,” Kandasamy said. “We had moved the audio recognition part out of the cloud. We did the learning in the cloud, but the recognition was done in the edge device and we had to convert quickly and get it down. Our bill in the first few months made us move that way. You couldn’t be streaming data over LTE or 3G for too long.”

At SRI, Chai worked on a project that looked at how to best manage power for flying objects where, if you have a single source of power, the system could intelligently allocate resources for either powering the flight or running the onboard compute workloads, mostly for surveillance, and then switch between them as needed. Most of the time, in a surveillance use case, nothing happens. And while that’s the case, you don’t need to compute every frame you see.

“We took that and we made it into a tool and a platform so that you can apply it to all sorts of use cases, from voice to vision to segmentation to time series stuff,” Kandasamy explained.

What’s important to note here is that the company offers the various components of what it calls the Latent AI Efficient Inference Platform (LEIP) as standalone modules or as a fully integrated system. The compressor and compiler are the first two of these and what the company is launching today is LEIP Adapt, the part of the system that manages the dynamic AI workloads Kandasamy described above.

Image Credits: Latent AI

In practical terms, the use case for LEIP Adapt is that your battery-powered smart doorbell, for example, can run in a low-powered mode for a long time, waiting for something to happen. Then, when somebody arrives at your door, the camera wakes up to run a larger model — maybe even on the doorbell’s base station that is plugged into power — to do image recognition. And if a whole group of people arrives at ones (which isn’t likely right now, but maybe next year, after the pandemic is under control), the system can offload the workload to the cloud as needed.

Kandasamy tells me that the interest in the technology has been “tremendous.” Given his previous experience and the network of SRI International, it’s maybe no surprise that Latent AI is getting a lot of interest from the automotive industry, but Kandasamy also noted that the company is working with consumer companies, including a camera and a hearing aid maker.

The company is also working with a major telco company that is looking at Latent AI as part of its AI orchestration platform and a large CDN provider to help them run AI workloads on a JavaScript backend.


By Frederic Lardinois