Personal blog of Yzmir Ramirez

The manufacturing industry took a hard hit from the Covid-19 pandemic, but there are signs of how it is slowly starting to come back into shape — helped in part by new efforts to make factories more responsive to the fluctuations in demand that come with the ups and downs of grappling with the shifting economy, virus outbreaks and more. Today, a businesses that is positioning itself as part of that new guard of flexible custom manufacturing — a startup called Fractory — is announcing a Series A of $9 million (€7.7 million) that underscores the trend.

The funding is being led by OTB Ventures, a leading European investor focussed on early growth, post-product, high-tech start-ups, with existing investors Trind Ventures, Superhero Capital, United Angels VC, Startup Wise Guys and Verve Ventures also participating.

Founded in Estonia but now based in Manchester, England — historically a strong hub for manufacturing in the country, and close to Fractory’s customers — Fractory has built a platform to make it easier for those that need to get custom metalwork to upload and order it, and for factories to pick up new customers and jobs based on those requests.

Fractory’s Series A will be used to continue expanding its technology, and to bring more partners into its ecosystem.

To date, the company has worked with more than 24,000 customers and hundreds of manufacturers and metal companies, and altogether it has helped crank out more than 2.5 million metal parts.

To be clear, Fractory isn’t a manufacturer itself, nor does it have no plans to get involved in that part of the process. Rather, it is in the business of enterprise software, with a marketplace for those who are able to carry out manufacturing jobs — currently in the area of metalwork — to engage with companies that need metal parts made for them, using intelligent tools to identify what needs to be made and connecting that potential job to the specialist manufacturers that can make it.

The challenge that Fractory is solving is not unlike that faced in a lot of industries that have variable supply and demand, a lot of fragmentation, and generally an inefficient way of sourcing work.

As Martin Vares, Fractory’s founder and MD, described it to me, companies who need metal parts made might have one factory they regularly work with. But if there are any circumstances that might mean that this factory cannot carry out a job, then the customer needs to shop around and find others to do it instead. This can be a time-consuming, and costly process.

“It’s a very fragmented market and there are so many ways to manufacture products, and the connection between those two is complicated,” he said. “In the past, if you wanted to outsource something, it would mean multiple emails to multiple places. But you can’t go to 30 different suppliers like that individually. We make it into a one-stop shop.”

On the other side, factories are always looking for better ways to fill out their roster of work so there is little downtime — factories want to avoid having people paid to work with no work coming in, or machinery that is not being used.

“The average uptime capacity is 50%,” Vares said of the metalwork plants on Fractory’s platform (and in the industry in general). “We have a lot more machines out there than are being used. We really want to solve the issue of leftover capacity and make the market function better and reduce waste. We want to make their factories more efficient and thus sustainable.”

The Fractory approach involves customers — today those customers are typically in construction, or other heavy machinery industries like ship building, aerospace and automotive — uploading CAD files specifying what they need made. These then get sent out to a network of manufacturers to bid for and take on as jobs — a little like a freelance marketplace, but for manufacturing jobs. About 30% of those jobs are then fully automated, while the other 70% might include some involvement from Fractory to help advise customers on their approach, including in the quoting of the work, manufacturing, delivery and more. The plan is to build in more technology to improve the proportion that can be automated, Vares said. That would include further investment in RPA, but also computer vision to better understand what a customer is looking to do, and how best to execute it.

Currently Fractory’s platform can help fill orders for laser cutting and metal folding services, including work like CNC machining, and it’s next looking at industrial additive 3D printing. It will also be looking at other materials like stonework and chip making.

Manufacturing is one of those industries that has in some ways been very slow to modernize, which in a way is not a huge surprise: equipment is heavy and expensive, and generally the maxim of “if it ain’t broke, don’t fix it” applies in this world. That’s why companies that are building more intelligent software to at least run that legacy equipment more efficiently are finding some footing. Xometry, a bigger company out of the U.S. that also has built a bridge between manufacturers and companies that need things custom made, went public earlier this year and now has a market cap of over $3 billion. Others in the same space include Hubs (which is now part of Protolabs) and Qimtek, among others.

One selling point that Fractory has been pushing is that it generally aims to keep manufacturing local to the customer to reduce the logistics component of the work to reduce carbon emissions, although as the company grows it will be interesting to see how and if it adheres to that commitment.

In the meantime, investors believe that Fractory’s approach and fast growth are strong signs that it’s here to stay and make an impact in the industry.

“Fractory has created an enterprise software platform like no other in the manufacturing setting. Its rapid customer adoption is clear demonstrable feedback of the value that Fractory brings to manufacturing supply chains with technology to automate and digitise an ecosystem poised for innovation,” said Marcin Hejka in a statement. “We have invested in a great product and a talented group of software engineers, committed to developing a product and continuing with their formidable track record of rapid international growth

Honeywell, which only recently announced its entry into the quantum computing race, and Cambridge Quantum Computing (CQ), which focuses on building software for quantum computers, today announced that they are combining Honeywell’s Quantum Solutions (HQS) business with Cambridge Quantum in the form of a new joint venture.

Honeywell has long partnered with CQ and invested in the company last year, too. The idea here is to combine Honeywell’s hardware expertise with CQ’s software focus to build what the two companies call “the world’s highest-performing quantum computer and a full suite of quantum software, including the first and most advanced quantum operating system.”

The merged companies (or ‘combination,’ as the companies’ press releases calls it) expect the deal to be completed in the third quarter of 2021. Honeywell Chairman and CEO Darius Adamczyk will become the chairman of the new company. CQ founder and CEO Ilyas Khan will become the CEO and current Honeywell Quantum Solutions President Tony Uttley will remain in this role at the new company.

The idea here is for Honeywell to spin off HQS and combine it with CQC to form a new company, while still playing a role in its leadership and finances. Honeywell will own a majority stake in the new company and invest between $270 and $300 million. It will also have a long-term agreement with the new company to build the ion traps at the core of its quantum hardware. CQ’s shareholders will own 45% of the new company.

Image Credits: Honeywell

“The new company will have the best talent in the industry, the world’s highest-performing quantum computer, the first and most advanced quantum operating system, and comprehensive, hardware-agnostic software that will drive the future of the quantum computing industry,” said Adamczyk. “The new company will be extremely well positioned to create value in the near-term within the quantum computing industry by offering the critical global infrastructure needed to support the sector’s explosive growth.”

The companies argue that a successful quantum business will need to be supported by large-scale investments and offer a one-stop shop for customers that combines hardware and software. By combining the two companies now, they note, they’ll be able to build on their respective leadership positions in their areas of expertise and scale their businesses while also accelerate their R&D and product roadmaps.

“Since we first announced Honeywell’s quantum business in 2018, we have heard from many investors who have been eager to invest directly in our leading technologies at the forefront of this exciting and dynamic industry – now, they will be able to do so,” Adamczyk said. “The new company will provide the best avenue for us to onboard new, diverse sources of capital at scale that will help drive rapid growth.”

CQ launched in 2014 and now has about 150 employees. The company raised a total of $72.8 million, including a $45 million round, which it announced last December. Honeywell, IBM Ventures, JSR Corporation, Serendipity Capital, Alvarium Investments and Talipot Holdings invested in this last round — which also means that IBM, which uses a different technology but, in many ways, directly competes with the new company, now owns a (small) part of it.

DeepSee.ai, a startup that helps enterprises use AI to automate line-of-business problems, today announced that it has raised a $22.6 million Series A funding round led by led by ForgePoint Capital. Previous investors AllegisCyber Capital and Signal Peak Ventures also participated in this round, which brings the Salt Lake City-based company’s total funding to date to $30.7 million.

The company argues that it offers enterprises a different take on process automation. The industry buzzword these days is “robotic process automation,” but DeepSee.ai argues that what it does is different. I describe its system as “knowledge process automation” (KPA). The company itself defines this as a system that “mines unstructured data, operationalizes AI-powered insights, and automates results into real-time action for the enterprise.” But the company also argues that today’s bots focus on basic task automation that doesn’t offer the kind of deeper insights that sophisticated machine learning models can bring to the table. The company also stresses that it doesn’t aim to replace knowledge workers but helps them leverage AI to turn into actionable insights the plethora of data that businesses now collect.

Image Credits: DeepSee.ai

“Executives are telling me they need business outcomes and not science projects,” writes DeepSee.ai CEO Steve Shillingford. “And today, the burgeoning frustration with most AI-centric deployments in large-scale enterprises is they look great in theory but largely fail in production. We think that’s because right now the current ‘AI approach’ lacks a holistic business context relevance. It’s unthinking, rigid and without the contextual input of subject-matter experts on the ground. We founded DeepSee to bridge the gap between powerful technology and line-of-business, with adaptable solutions that empower our customers to operationalize AI-powered automation — delivering faster, better and cheaper results for our users.”

To help businesses get started with the platform, DeepSee.ai offers three core tools. There’s DeepSee Assembler, which ingests unstructured data and gets it ready for labeling, model review and analysis. Then, DeepSee Atlas can use this data to train AI models that can understand a company’s business processes and help subject-matter experts define templates, rules and logic for automating a company’s internal processes. The third tool, DeepSee Advisor, meanwhile focuses on using text analysis to help companies better understand and evaluate their business processes.

Currently, the company’s focus is on providing these tools for insurance companies, the public sector and capital markets. In the insurance space, use cases include fraud detection, claims prediction and processing, and using large amounts of unstructured data to identify patterns in agent audits, for example.

That’s a relatively limited number of industries for a startup to operate in, but the company says it will use its new funding to accelerate product development and expand to new verticals.

“Using KPA, line-of-business executives can bridge data science and enterprise outcomes, operationalize AI/ML-powered automation at scale, and use predictive insights in real time to grow revenue, reduce cost and mitigate risk,” said Sean Cunningham, managing director of ForgePoint Capital. “As a leading cybersecurity investor, ForgePoint sees the daily security challenges around insider threat, data visibility and compliance. This investment in DeepSee accelerates the ability to reduce risk with business automation and delivers much-needed AI transparency required by customers for implementation.”

Scale AI CEO Alexandr Wang doesn’t need a crystal ball to see where artificial intelligence will be used in the future. He just looks at his customer list.

The four-year-old startup, which recently hit a valuation of more than $3.5 billion, got its start supplying autonomous vehicle companies with the labeled data needed to train machine learning models to develop and eventually commercialize robotaxis, self-driving trucks and automated bots used in warehouses and on-demand delivery.

In 2020, that changed as e-commerce, enterprise automation, government, insurance, real estate and robotics companies turned to Scale’s visual data labeling platform to develop and apply artificial intelligence to their respective businesses. Now, the company is preparing for the customer list to grow and become more varied.

How 2020 shaped up for AI

Scale AI’s customer list has included an array of autonomous vehicle companies including Alphabet, Voyage, nuTonomy, Embark, Nuro and Zoox. While it began to diversify with additions like Airbnb, DoorDash and Pinterest, there were still sectors that had yet to jump on board. That changed in 2020, Wang said.

Scale began to see incredible use cases of AI within the government as well as enterprise automation, according to Wang. Scale AI began working more closely with government agencies this year and added enterprise automation customers like States Title, a residential real estate company.

Wang also saw an increase in uses around conversational AI, in both consumer and enterprise applications as well as growth in e-commerce as companies sought out ways to use AI to provide personalized recommendations for its customers that were on par with Amazon.

Robotics continued to expand as well in 2020, although it spread to use cases beyond robotaxis, autonomous delivery and self-driving trucks, Wang said.

“A lot of the innovations that have happened within the self-driving industry, we’re starting to see trickle out throughout a lot of other robotics problems,” Wang said. “And so it’s been super exciting to see the breadth of AI continue to broaden and serve our ability to support all these use cases.”

The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses, Wang said, adding that advancements in natural language processing of text, improved offerings from cloud companies like AWS, Azure and Google Cloud and greater access to datasets helped sustain this trend.

“We’re finally getting to the point where we can help with computational AI, which has been this thing that’s been pitched for forever,” he said.

That slow burn heated up with the COVID-19 pandemic, said Wang, noting that interest has been particularly strong within government and enterprise automation as these entities looked for ways to operate more efficiently.

“There was this big reckoning,” Wang said of 2020 and the effect that COVID-19 had on traditional business enterprises.

If the future is mostly remote with consumers buying online instead of in-person, companies started to ask, “How do we start building for that?,” according to Wang.

The push for operational efficiency coupled with the capabilities of the technology is only going to accelerate the use of AI for automating processes like mortgage applications or customer loans at banks, Wang said, who noted that outside of the tech world there are industries that still rely on a lot of paper and manual processes.

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

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

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

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

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

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

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

Skydio only just recently announced its expansion into the enterprise and commercial market with hardware and software tools for its autonomous drone technology, and now it’s taking the lid off a brand new big partnership with one commercial partner. Skydio will work with EagleView to deploy automated residential roof inspections using Skydio drones, with service initially provide via EagleView’s Assess product, launching first in the Dallas/Ft. Worth area of Texas.

The plan is to expand coverage to additional metro areas starting next year, and then broaden to rural customers as well. The partners will use AI-based analysis, paired with Skydio’s high-resolution, precision imaging to provide roofing status information to insurance companies, claims adjustment companies and government agencies, providing a new level of quality and accuracy for property inspections that don’t even require an in-person roof inspection component.

Skydio announced its enterprise product expansion in July, alongside a new $100 million funding round. The startup, which has already delivered two generations of its groundbreaking fully autonomous consumer drone, also debuted the X2, a commercial drone that includes additional features like a thermal imaging camera. It’s also offering a suite of “enterprise skills,” software features that can provide its partners with automated workflows and AI analysis and processing, including a House Scan feature for residential roof inspection, which is core to this new partnership.

“The best-kept secret in quantum computing.” That’s what Cambridge Quantum Computing (CQC) CEO Ilyas Khan called Honeywell‘s efforts in building the world’s most powerful quantum computer. In a race where most of the major players are vying for attention, Honeywell has quietly worked on its efforts for the last few years (and under strict NDA’s, it seems). But today, the company announced a major breakthrough that it claims will allow it to launch the world’s most powerful quantum computer within the next three months.

In addition, Honeywell also today announced that it has made strategic investments in CQC and Zapata Computing, both of which focus on the software side of quantum computing. The company has also partnered with JPMorgan Chase to develop quantum algorithms using Honeywell’s quantum computer. The company also recently announced a partnership with Microsoft.

Honeywell has long built the kind of complex control systems that power many of the world’s largest industrial sites. It’s that kind of experience that has now allowed it to build an advanced ion trap that is at the core of its efforts.

This ion trap, the company claims in a paper that accompanies today’s announcement, has allowed the team to achieve decoherence times that are significantly longer than those of its competitors.

“It starts really with the heritage that Honeywell had to work from,” Tony Uttley, the president of Honeywell Quantum Solutions, told me. “And we, because of our businesses within aerospace and defense and our business in oil and gas — with solutions that have to do with the integration of complex control systems because of our chemicals and materials businesses — we had all of the underlying pieces for quantum computing, which are just fabulously different from classical computing. You need to have ultra-high vacuum system capabilities. You need to have cryogenic capabilities. You need to have precision control. You need to have lasers and photonic capabilities. You have to have magnetic and vibrational stability capabilities. And for us, we had our own foundry and so we are able to literally design our architecture from the trap up.”

The result of this is a quantum computer that promises to achieve a quantum Volume of 64. Quantum Volume (QV), it’s worth mentioning, is a metric that takes into account both the number of qubits in a system as well as decoherence times. IBM and others have championed this metric as a way to, at least for now, compare the power of various quantum computers.

So far, IBM’s own machines have achieved QV 32, which would make Honeywell’s machine significantly more powerful.

Khan, whose company provides software tools for quantum computing and was one of the first to work with Honeywell on this project, also noted that the focus on the ion trap is giving Honeywell a bit of an advantage. “I think that the choice of the ion trap approach by Honeywell is a reflection of a very deliberate focus on the quality of qubit rather than the number of qubits, which I think is fairly sophisticated,” he said. “Until recently, the headline was always growth, the number of qubits running.”

The Honeywell team noted that many of its current customers are also likely users of its quantum solutions. These customers, after all, are working on exactly the kind of problems in chemistry or material science that quantum computing, at least in its earliest forms, is uniquely suited for.

Currently, Honeywell has about 100 scientists, engineers and developers dedicated to its quantum project.

For the longest time, even while scientists were working to make it a reality, quantum computing seemed like science fiction. It’s hard enough to make any sense out of quantum physics to begin with, let alone the practical applications of this less than intuitive theory. But we’ve now arrived at a point where companies like D-Wave, Rigetti, IBM and others actually produce real quantum computers.

They are still in their infancy and nowhere near as powerful as necessary to compute anything but very basic programs, simply because they can’t run long enough before the quantum states decohere, but virtually all experts say that these are solvable problems and that now is the time to prepare for the advent of quantum computing. Indeed, Gartner just launched a Quantum Volume metric, based on IBM’s research, that looks to help CIOs prepare for the impact of quantum computing.

To discuss the state of the industry and why now is the time to get ready, I sat down with IBM’s Jay Gambetta, who will also join us for a panel on Quantum Computing at our TC Sessions: Enterprise event in San Francisco on September 5, together with Microsoft’s Krysta Svore and Intel’s Jim Clark.

Here at TechCrunch, we like to think about what’s next, and there are few technologies quite as exotic and futuristic as quantum computing. After what felt like decades of being “almost there,” we now have working quantum computers that are able to run basic algorithms, even if only for a very short time. As those times increase, we’ll slowly but surely get to the point where we can realize the full potential of quantum computing.

For our TechCrunch Sessions: Enterprise event in San Francisco on September 5, we’re bringing together some of the sharpest minds from some of the leading companies in quantum computing to talk about what this technology will mean for enterprises (p.s. early-bird ticket sales end this Friday). This could, after all, be one of those technologies where early movers will gain a massive advantage over their competitors. But how do you prepare yourself for this future today, while many aspects of quantum computing are still in development?

IBM’s quantum computer demonstrated at Disrupt SF 2018

Joining us onstage will be Microsoft’s Krysta Svore, who leads the company’s Quantum efforts; IBM’s Jay Gambetta, the principal theoretical scientist behind IBM’s quantum computing effort; and Jim Clark, the director of quantum hardware at Intel Labs.

That’s pretty much a Who’s Who of the current state of quantum computing, even though all of these companies are at different stages of their quantum journey. IBM already has working quantum computers, Intel has built a quantum processor and is investing heavily into the technology and Microsoft is trying a very different approach to the technology that may lead to a breakthrough in the long run but that is currently keeping it from having a working machine. In return, though, Microsoft has invested heavily into building the software tools for building quantum applications.

During the panel, we’ll discuss the current state of the industry, where quantum computing can already help enterprises today and what they can do to prepare for the future. The implications of this new technology also go well beyond faster computing (for some use cases); there are also the security issues that will arise once quantum computers become widely available and current encryption methodologies become easily breakable.

The early-bird ticket discount ends this Friday, August 9. Be sure to grab your tickets to get the max $100 savings before prices go up. If you’re a startup in the enterprise space, we still have some startup demo tables available! Each demo table comes with four tickets to the show and a high-visibility exhibit space to showcase your company to attendees — learn more here.

San Diego-based Edgybees today announced the launch of Argus, its API-based developer platform that makes it easy to add augmented reality features to live video feeds.

The service has long used this capability to run its own drone platform for first responders and enterprise customers, which allows its users to tag and track objects and people in emergency situations, for example, to create better situational awareness for first responders.

I first saw a demo of the service a year ago, when the team walked a group of journalists through a simulated emergency, with live drone footage and an overlay of a street map and the location of ambulances and other emergency personnel. It’s clear how these features could be used in other situations as well, given that few companies have the expertise to combine the video footage, GPS data and other information, including geographic information systems, for their own custom projects.

Indeed, that’s what inspired the team to open up its platform. As the Edgybees team told me during an interview at the Ourcrowd Summit last month, it’s impossible for the company to build a new solution for every vertical that could make use of it. So instead of even trying (though it’ll keep refining its existing products), it’s now opening up its platform.

“The potential for augmented reality beyond the entertainment sector is endless, especially as video becomes an essential medium for organizations relying on drone footage or CCTV,” said Adam Kaplan, CEO and co-founder of Edgybees. “As forward-thinking industries look to make sense of all the data at their fingertips, we’re giving developers a way to tailor our offering and set them up for success.”

In the run-up to today’s launch, the company has already worked with organizations like the PGA to use its software to enhance the live coverage of its golf tournaments.