Proper raises $4.3M seed round to help other fintechs wrangle data

What’s one of the hottest areas within fintech today? Funny enough, it’s fintech for fintechs (say that a few times fast).

Fintech startups have absolutely proliferated in the past few years, but it’s still a highly fragmented space — which is where “fintech for fintech” companies come in. Companies in fintech often have complex tech stacks, relying on data from various sources and service providers to underpin their core businesses.

Proper recognizes that this fragmentation can get messy very quickly. The company is working to streamline operations at other fintech startups, specifically through its reconciliation software that those companies can use to help ensure they’re working with accurate and precise data even when it comes from different places, co-founder and CEO Kyle Maloney told TechCrunch. The company’s core product is a universal ledger of sorts that displays and reconciles transaction data based on core accounting principles, Maloney said.

Maloney and his co-founder, Travis Gibson, both met while working on engineering teams at card issuance company Marqeta.

Proper co-founders Kyle Maloney and Travis Gibson

Proper co-founders Kyle Maloney and Travis Gibson Image Credits: Proper

“During our time at Marqeta, when we were building out new money movement integrations, we were constantly in this process of stitching together various underlying providers — think bank partners, payments, vendors, direct integrations with networks,” Gibson said in a call with TechCrunch.

Gibson added that in their prior roles, they’d often spend 80 to 90% of his day building underlying financial data infrastructure to support different methods of money movement. Proper was created out of a desire to help make the data management process easier for fintech companies so they can focus on their core business, Gibson said.

The problem can get especially sticky for early-stage, high-growth companies looking to build more customized products, Maloney explained.

“Using a holistic stack like Stripe may not work for every type of money movement operation [early-stage companies] are trying to complete. And so what happens is, they end up in this existential crisis, where Stripe is great, or any number of these providers are awesome, but they may want to use a best-in-class solution, like Modern Treasury for ACH [for example], and now they have to go build a bunch of infrastructure under the hood that Stripe was providing, specifically on their service,” Maloney said.

A number of other “fintech for fintech” startups, like Meld and Novopayment, have taken the approach of developing APIs to help fintechs solve integration issues. But Proper’s product isn’t an API, because the company itself handles building out integrations rather than simply providing developers with the tools to do so themselves, Gibson explained. In the long-term, he said, Proper does plan to build APIs and expand its portfolio of integrations.

Fintech Proper's data reconciliation platform

A screenshot of Proper’s data reconciliation platform Image Credits: Proper

Proper uses two main strategies to ensure data accuracy, Gibson explained. The first is “balanced reconciliation,” where the startup tracks money at banks and external parties to verify that funds they believe have been transferred have made it to the destination. The second is “transaction matching,” which is basically what it sounds like — taking data from a given transaction across different sources and making sure that data ties, according to Gibson.

The startup, which participated in Y Combinator’s Winter 2022 cohort, announced today that it has closed a $4.3 million seed round led by Redpoint Ventures with participation from BoxGroup, Mischief, Y Combinator, and others. Gibson and Maloney declined to share the number of customers Proper serves today, but said it is bringing on several new clients each week.

The team, which is comprised of 6 people today, plans to use the new capital to build out a no-code financial operations dashboard, which will fit in with the company’s goal of helping engineering teams save time.

“One of the things that we’ve heard a ton is that there’s a huge need for financial operations teams internally to be able to manage, configure, and, process money movement without the need of engineering intervention,” Maloney said.

Maloney said he’s mainly seen other fintechs handle their reconciliation and ledgering troubles internally with in-house solutions.

“We’ve not seen anything out there that really solves both the reconciliation and ledgering problem in a single, ubiquitous solution. We know today that companies often are managing these processes internally with bespoke SQL queries and Python scripts and operations teams and spreadsheets,” Maloney said.

InFlow, a science-based app for ADHD, raises $2.3M Seed led by Hoxton Ventures

Attention deficit hyperactivity disorder (ADHD) symptoms can include anxiety, chronic boredom, impulsiveness, trouble concentrating, controlling anger, and even depression. But ADHD suffers can face lengthy waiting periods for assessment and prohibitively expensive treatments.

Now a startup, which launched in 2020, hopes to address this by luring the knowledge of a team of clinicians and coaches into an app with a guided program to address ADHD symptoms. To help, Inflow claims to enable users to implement Cognitive Behavioral Therapy (CBT) coping strategies into their daily lives. 

In 2020 it raised $680K from Rhythm VC and angel investors. It’s now raised $2.3M in seed funding in a round led by London-based Hoxton Ventures.

A former grade of Y Combinator 21’s batch, Inflow also drew in participation from US-based Route 66 Ventures.

Several prominent angel investors are also backing the company, including the founders of addiction digital clinic Quit Genius (Yusuf Sherwani, Maroof Ahmed, Sarim Siddiqui), and the CEO of legal services chatbot DoNotPay, Joshua Browder.

Founded in 2020 by Seb Isaacs, Levi Epstein (formerly Product Manager at Babylon Health), and ADHD expert Dr. George Sachs, Inflow will use the funding to expand its team and roll out additional tools and services.

InFlow app

InFlow app

InFlow competes in an ADHD app market populated by the likes of apps like SimpleMind Pro, Brain Focus and Focus@Will, but, in truth, most apps are simply touted as productivity apps that might be adapted for use by those with ADHD, but few are designed in a clinical manner.

How Inflow works is that users complete short daily exercises and challenges to develop healthy habits, learn skills, practice ADHD-specific mindfulness techniques, learn about their neurological differences, and reframe negative thoughts, says the company.

InFlow claims it is being downloaded over 15k times every month.

Co-founder Seb Isaacs said: “We knew we could simplify the ADHD care process and reach millions of underserved people living with ADHD. Inflow can offer immediate, affordable, and on-demand support in ways our burdened mental health system simply cannot. There’s no waitlist, no need for a referral, no complicated intake process.”

Hussein Kanji, Partner at Hoxton Ventures, added: “It’s been a privilege to watch Inflow deliver on its mission to see every person with ADHD thrive.”

Phylagen, which tracks indoor microbiomes, says it’s right now “racing to meet demand”

Two years into a worldwide pandemic, outfits around the globe are wrestling with how to resume their in-person operations safely. Consider that Apple just scrapped its office-return deadline, while Google, which plans to require its workforce to return to its offices three times a week at some point next year, made it clear to employees yesterday that if they don’t get vaccinated, they will eventually lose their jobs. “Our vaccination requirements are one of the most important ways we can keep our workforce safe and keep our services running,” said Google in a statement to CNBC:

Still, even vaccinated individuals can become infected with variants of the highly contagious coronavirus. Enter Phylagen, a low-flying, seven-year-old, San Francisco-based company that says it’s able to combine microbial genomics and data analytics to answer the question of whether a physical space has played host to someone with Covid-19.

How does it do it? In broad strokes, Phylagen employs a network of sensors, swabs, and sample collectors who put the materials into packages twice a week, then ship them to a centralized lab. Phylagen then promises data within 72 hours about whether sick people have carried germs inside a building — which it divides into floors and zones for tracking purposes — or whether the building’s air is safe to breathe.

The company calls it “enterprise pathogen monitoring as a service,” and its feasibility has long fascinated founder and CEO Jessica Green, a former biology professor who is formally trained as both a civil engineer and a microbiome scientist.

It was a lonely obsession until recently, however. As Green explains it: “We spend 90% of our time indoors and know nothing about what we’re breathing in, even while during this very conversation, we’ll both emit millions of micro organisms and inhale hundreds and thousands of viruses, bacteria, and molds that [can have] really severe consequences for our health and well-being.” While she “knew this decades ago,” she adds, the public’s understanding has “come to fruition with this pandemic.”

Phylagen wasn’t always so focused on the air we breath. From its earliest days until some time last spring, the company operated in what’s called the supply chain track-and-trace market, a segment that businesses use to ensure that their products have followed an expected path toward their final destination. (Detours can mean products have been tampered with, which can ruin a company’s reputation or even lead to deadly consequences, especially when it comes to pharmaceuticals.)

Green suggests there was interest in the product as a means to track Covid as the pandemic took hold, but as it became clearer that the virus was spreading through aerosols and not surfaces, Phylagen pivoted completely to another application of Phylagen’s  technology. It began to use its learnings — and its ever-growing database of microorganisms — not for traceability applications, but instead to enter buildings, capture the microorganisms found there, then digitize the information and push it out to customers.

Apparently, there are a growing number of them. While Green declined to name specific clients, saying only that Phylagen is working closely with numerous big tech companies and commercial real estate companies, she said the business, cofounded by  Harrison Dillon — he previously cofounded the industrial biotech firm Solazyme — is going like gangbusters heading into 2022.

Revenue, she says, has grown 10 times year over year. The company has 40 employees up from 20. Phylagen plans to double headcount again, in fact, aided by strategic funding the company quietly raised this past summer from Johnson Controls, a publicly traded European conglomerate that produces fire, HVAC, and security equipment for buildings.

Altogether, Phylagen has raised $30 million to date, including from 3M, Breakout Ventures, and Cultivian Sandbox.

Of course, questions remain, including whether Phylagen can outmaneuver rivals that are springing up in the space.

“There are emerging competitors because this is the new normal,” acknowledges Green. “Everybody is going to be demanding healthy indoor air, and there are currently very antiquated ways of measuring indoor air quality, and no affordable, reliable ways to test for anything biological that’s air-related.”

Phylagen’s own processes may seem antiquated to those who don’t want to wait 72 hours for results from either the labs that Phylagen owns (it has one in San Francisco and another in Manhattan) or with which it partners. After all, given how quickly the coronavirus is still spreading, two or three days might sound to some potential customers like an eternity.

Green suggests that window will shrink soon enough. “In the next generation [of testing], everything will be automated and on site. Imagine a CO2 sensor or Nest thermostat that gives information on temperature and relative humidity. There is a clear path to being able to detect DNA and RNA that is airborne in a similar way, and that’s what we’re working toward.”

Certainly, if Phylagen fulfills its potential, its opportunities look, well, significant. For one thing, Phylagen can test for much more than Covid-19 and says that allergens are also on its road map.

Beyond its commercial possibilities, there is also the home. Already, early investor 3M appears to be champing at the bit to develop data-driven consumer products. It even began selling a home cleanliness kit in September using Phylagen’s technology, though judging by the kit’s price on Amazon — it costs roughly $180 –it’s too expensive for most homeowners to consider at this point and is more of a trial balloon.

In the meantime, Green insists that the company remains entirely focused for now on its enterprise customers, in part because it doesn’t have time to consider other products, not anytime soon.

“The main thing to take away from that [3M] product is that we can really make any menu of organisms that we want to test for,” she says. “But the most relevant and the largest market opportunity and the biggest market need right now is the commercial building space.

“It’s more a function of what we’re able to keep up with,” she adds. “Right now, We’re racing to meet demand.”

As working out goes virtual, Moxie raises $6.3M Seed+ round led by Resolute Ventures

With the pandemic sending the planet indoors to workout, the at-home fitness market has boomed. It was only in October last year that three-year-old Future closed $24 million in Series B and Playbook (streaming for personal trainers) raised $9.3 million in a Series A. Into this market launched Moxie, a platform that allowed fitness instructors to broadcast live and recorded classes, access licensed music playlists and deploy a CRM and payment tools. Classes range from $5-$25 and various subscriptions and packages are offered.

Moxie has now raised a $6.3M ‘Seed+’ funding round led by Resolute Ventures with participation from Bessemer Ventures, Greycroft Ventures, Gokul Rajaram, and additional investors. With the $2.1M Seed round from last October, that means Moxie has now raised a total of $8.4M.

With the funding, Moxie now plans to better optimize the user experience with a curated selection of top Moxie classes; new tools that help connect users to instructors; and the ability to preview classes before attending.

The company claims to have experienced “exponential growth” because of its convenience in the pandemic era, with 8,000 classes and 1 million class-minutes completed in March. Moxie’s independent instructors set their own schedules and prices, and get to keep 85% of what they earn on the platform.

The company will also now launch ‘Moxie Benefits’ in partnership with Stride Health, provide instructors with access to health insurance, dental and vision plans, life insurance, and other benefits.

Also planned is ‘Moxie Teams’, enabling groups of instructors to join together to form small businesses on the platform, not unlike the way some Uber drivers form teams.

Jason Goldberg, CEO and founder said in a statement: “Moxie was born during the pandemic alongside thousands of independent fitness instructors who were forced out of gyms and studios and suddenly had to become entrepreneurs and navigate the new frontier of virtual fitness. Now we are seeing widespread adoption of online fitness into people’s lives, and Moxie’s growth proves that these shifts in consumer behavior have staying power. We know that 89% of Moxie users plan to continue virtual workouts post COVID — they love the convenience.”

Resolute Ventures Partner & Co-Founder Raanan Bar-Cohen said: “Our investment theory has always been to identify entrepreneurial founders solving for today’s problems. With Moxie, we saw an experienced operator in Jason, with a product that solved for the issues that instructors and consumers had experienced in the shift to online fitness, as well as a clear roadmap for continued success.”

So why has Moxie managed to cleave to the new virtual workout culture? Goldberg tells me it’s down to a range of factors.

For starters, it’s a two-sided fitness marketplace that has live interactive group fitness classes, unlike VOD apps, and, crucially, unlike Peloton. Additionally, any instructor can teach on Moxie, rather than wait to be picked as a ‘star’ by Peloton. Since 90% of classes are live group fitness classes, they are effectively replacing yoga studios and HIIT classes, rather than personal training. He says many top instructors are now earning $6-figures on the platform.

Certainly, Moxie has managed to capitalize on the fact that while gyms are closed, it’s easy to do virtual classes. Will they still stick around when the pandemic is over? Presumably many will find it more convenient than schlepping to the gym and less intimidating than joining classes in person. Additionally, users can switch classes as easily as switching TV channels.

As Goldberg told me via email: “Covid forced everyone to try virtual fitness for the first time. Guess what? People found it more convenient and more connected than going to offline gyms. And guess what? Peloton is not for everyone.”

Metalenz reimagines the camera in 2D and raises $10M to ship it

As impressive as the cameras in our smartphones are, they’re fundamentally limited by the physical necessities of lenses and sensors. Metalenz skips over that part with a camera made of a single “metasurface” that could save precious space and battery life in phones and other devices… and they’re about to ship it.

The concept is similar to, but not descended from, the “metamaterials” that gave rise to flat beam-forming radar and lidar of Lumotive and Echodyne. The idea is to take a complex 3D structure and accomplish what it does using a precisely engineered “2D” surface — not actually two-dimensional, of course, but usually a plane with features measured in microns.

In the case of a camera, the main components are of course a lens (these days it’s usually several stacked), which corrals the light, and an image sensor, which senses and measures that light. The problem faced by cameras now, particularly in smartphones, is that the lenses can’t be made much smaller without seriously affecting the clarity of the image. Likewise sensors are nearly at the limit of how much light they can work with. Consequently most of the photography advancements of the last few years have been done on the computational side.

Using an engineered surface that does away with the need for complex optics and other camera systems has been a goal for years. Back in 2016 I wrote about a NASA project that took inspiration from moth eyes to create a 2D camera of sorts. It’s harder than it sounds, though — usable imagery has been generated in labs, but it’s not the kind of thing that you take to Apple or Samsung.

Metalenz aims to change that. The company’s tech is built on the work of Harvard’s Frederico Capasso, who has been publishing on the science behind metasurfaces for years. He and Rob Devlin, who did his doctorate work in Capasso’s lab, co-founded the company to commercialize their efforts.

“Early demos were extremely inefficient,” said Devlin of the field’s first entrants. “You had light scattering all over the place, the materials and processes were non-standard, the designs weren’t able to handle the demands that a real world throws at you. Making one that works and publishing a paper on it is one thing, making 10 million and making sure they all do the same thing is another.”

Their breakthrough — if years of hard work and research can be called that — is the ability not just to make a metasurface camera that produces decent images, but to do it without exotic components or manufacturing processes.

“We’re really using all standard semiconductor processes and materials here, the exact same equipment — but with lenses instead of electronics,” said Devlin. “We can already make a million lenses a day with our foundry partners.”

Diagram comparing the multi-lens barrel of a conventional phone camera, and their simpler "meta-optic"

The thing at the bottom is the chip where the image processor and logic would be, but the meta-optic could also integrate with that. the top is a pinhole.

The first challenge is more or less contained in the fact that incoming light, without lenses to bend and direct it, hits the metasurface in a much more chaotic way. Devlin’s own PhD work was concerned with taming this chaos.

“Light on a macro [i.e. conventional scale, not close-focusing] lens is controlled on the macro scale, you’re relying on the curvature to bend the light. There’s only so much you can do with it,” he explained. “But here you have features a thousand times smaller than a human hair, which gives us very fine control over the light that hits the lens.”

Those features, as you can see in this extreme close-up of the metasurface, are precisely tuned cylinders, “almost like little nano-scale Coke cans,” Devlin suggested. Like other metamaterials, these structures, far smaller than a visible or near-infrared light ray’s wavelength, manipulate the radiation by means that take a few years of study to understand.

Diagram showing chips being manufactured, then an extreme close up showing nano-scale features.The result is a camera with extremely small proportions and vastly less complexity than the compact camera stacks found in consumer and industrial devices. To be clear, Metalenz isn’t looking to replace the main camera on your iPhone — for conventional photography purposes the conventional lens and sensor are still the way to go. But there are other applications that play to the chip-style lens’s strengths.

Something like the FaceID assembly, for instance, presents an opportunity. “That module is a very complex one for the cell phone world — it’s almost like a Rube Goldberg machine,” said Devlin. Likewise the miniature lidar sensor.

At this scale, the priorities are different, and by subtracting the lens from the equation the amount of light that reaches the sensor is significantly increased. That means it can potentially be smaller in every dimension while performing better and drawing less power.

Image (of a very small test board) from a traditional camera, left, and metasurface camera, right. Beyond the vignetting it’s not really easy to tell what’s different, which is kind of the point.

Lest you think this is still a lab-bound “wouldn’t it be nice if” type device, Metalenz is well on its way to commercial availability. The $10M round A they just raised was led by 3M Ventures, Applied Ventures LLC, Intel Capital, M Ventures and TDK Ventures, along with Tsingyuan Ventures and Braemar Energy Ventures — a lot of suppliers in there.

Unlike many other hardware startups, Metalenz isn’t starting with a short run of boutique demo devices but going big out of the gate.

“Because we’re using traditional fabrication techniques, it allows us to scale really quickly. We’re not building factories or foundries, we don’t have to raise hundreds of mils; we can use whats already there,” said Devlin. “But it means we have to look at applications that are high volume. We need the units to be in that tens of millions range for our foundry partners to see it making sense.”

Although Devlin declined to get specific, he did say that their first partner is “active in 3D sensing” and that a consumer device, though not a phone, would be shipping with Metalenz cameras in early 2022 — and later in 2022 will see a phone-based solution shipping as well.

In other words, while Metalenz is indeed a startup just coming out of stealth and raising its A round… it already has shipments planned on the order of tens of millions. The $10M isn’t a bridge to commercial viability but short term cash to hire and cover up-front costs associated with such a serious endeavor. It’s doubtful anyone on that list of investors harbors any serious doubts on ROI.

The 3D sensing thing is Metalenz’s first major application, but the company is already working on others. The potential to reduce complex lab equipment to handheld electronics that can be fielded easily is one, and improving the benchtop versions of tools with more light-gathering ability or quicker operation is another.

Though a device you use may in a few years have a Metalenz component in it, it’s likely you won’t know — the phone manufacturer will probably take all the credit for the improved performance or slimmer form factor. Nevertheless, it may show up in teardowns and bills of material, at which point you’ll know this particular university spin-out has made it to the big leagues.

AWS expands on SageMaker capabilities with end-to-end features for machine learning

Nearly three years after it was first launched, Amazon Web Services’ SageMaker platform has gotten a significant upgrade in the form of new features making it easier for developers to automate and scale each step of the process to build new automation and machine learning capabilities, the company said.

As machine learning moves into the mainstream, business units across organizations will find applications for automation,  and AWS is trying to make the development of those bespoke applications easier for its customers.

“One of the best parts of having such a widely-adopted service like SageMaker is that we get lots of customer suggestions which fuel our next set of deliverables,” said AWS vice president of machine learning, Swami Sivasubramanian. “Today, we are announcing a set of tools for Amazon SageMaker that makes it much easier for developers to build end-to-end machine learning pipelines to prepare, build, train, explain, inspect, monitor, debug and run custom machine learning models with greater visibility, explainability, and automation at scale.”

Already companies like 3M, ADP, AstraZeneca, Avis, Bayer, Capital One, Cerner, Domino’s Pizza, Fidelity Investments, Lenovo, Lyft, T-Mobile, and Thomson Reuters are using SageMaker tools in their own operations, according to AWS.

The company’s new products include Amazon SageMaker Data Wrangler, which the company said was providing a way to normalize data from disparate sources so the data is consistently easy to use. Data Wrangler can also ease the process of grouping disparate data sources into features to highlight certain types of data. The Data Wrangler tool contains over 300 built-in data transformers that can help customers normalize, transform and combine features without having to write any code.

Amazon also unveiled the Feature Store, which allows customers to create repositories that make it easier to store, update, retrieve and share machine learning features for training and inference.

Another new tool that Amazon Web Services touted was its workflow management and automation toolkit, Pipelines. The Pipelines tech is designed to provide orchestration and automation features not dissimilar from traditional programming. Using pipelines, developers can define each step of an end-to-end machine learning workflow, the company said in a statement. Developers can use the tools to re-run an end-to-end workflow from SageMaker Studio using the same settings to get the same model every time, or they can re-run the workflow with new data to update their models.

To address the longstanding issues with data bias in artificial intelligence and machine learning models, Amazon launched SageMaker Clarify. First announced today, this tool allegedly provides bias detection across the machine learning workflow, so developers can build with an eye towards better transparency on how models were set up. There are open source tools that can do these tests, Amazon acknowledged, but the tools are manual and require a lot of lifting from developers, according to the company.

Other products designed to simplify the machine learning application development process include SageMaker Debugger, which enables to developers to train models faster by monitoring system resource utilization and alerting developers to potential bottlenecks; Distributed Training, which makes it possible to train large, complex, deep learning models faster than current approaches by automatically splitting data cross multiple GPUs to accelerate training times; and SageMaker Edge Manager, a machine learning model management tool for edge devices, which allows developers to optimize, secure, monitor and manage models deployed on fleets of edge devices.

Last but not least, Amazon unveiled SageMaker JumpStart, which provides developers with a searchable interface to find algorithms and sample notebooks so they can get started on their machine learning journey. The company said it would give developers new to machine learning the option to select several pre-built machine learning solutions and deploy them into SageMaker environments.

3M and MIT partner to develop a new, affordable rapid COVID-19 test

A heavyweight partnership between industry and academic sciences is throwing their considerable weight into an important task: Creating a new low-cost, rapid diagnostic test for COVID-19. Chemical industry leader 3M has partnered with MIT to create a diagnostic tool for COVID-19 that’s easy-to-use, and that can be manufactured cheaply and in large volume for mass distribution and use.

The test is currently the research phase, with a team led by MIT’s Professor Hadley Sikes of the school’s Department of Chemical Engineering. Sikes’ laboratory has a specific focus on creating and developing tech to enhance the performance of protein tests that are meant to provide rapid, accurate results.

3M is contributing its biomaterials and bioprocessing expertise, along with its experience in creating products designed to be manufactured at scale. The end goal is to create a test that detects viral antigens, a type of test first cleared for use in COVID-19 detection at the beginning of May by the FDA. These tests provide results much faster than the molecular PCR-based test – but do have a higher change of fall negatives. Still, their ability to be administered at point-of-care, and return results within just minutes, could help considerably in ramping up testing efforts, especially in cases where individuals aren’t necessarily presenting symptoms but are in situations where they could pose a risk to others if carrying the virus while asymptomatic.

The new 3M and MIT projects is part of the RADx Tech program created by the National Institute of Health (NIH) specifically to fund the development of tests that can expand U.S. testing deployment. An initial $500,000 of funding was provided to MIT and 3M from the program, and it can potentially receive further funding after achieving other development milestones.

3M files suit over third-party price gouging of N95 masks on Amazon

Amazon has promised vigilance against third-party price gouging since COVID-19 achieved global pandemic status. The company’s efforts have had mixed success, however, due in part to the sheer volume of vendors that utilize the company’s massive commerce platform. In a suit filed in California this week, 3M claims the seller was charging massively inflated prices for either damaged or counterfeit products.

“3M alleges that the defendants charged prices for the fraudulent respirators that exceeded as much as 20 times 3M’s N95 respirator list prices,” the company writes. “Amazon learned that the defendants misrepresented what would be delivered for these exorbitant prices, and that buyers had received non-3M respirators, fewer items than purchased, products in suspect packaging, and defective or damaged items. Amazon has blocked the accounts on its platform.”

N95 masks have become one of the most in-demand pieces of PPE during the ongoing crisis, due to their extreme filtration efficacy. The CDC recommends the respirators versus surgical masks, due to their ability to filter out small particles. The latter is mostly effective for large droplets and fluid. N95 masks, on the other hand, are capable of filtering out more than 95% of large and small air particles. For that reason, many groups have insisted the equipment be reserved for front-line responders.

Amazon confirmed its involvement in the suit, telling TechCrunch, “There is no place for counterfeiting or price gouging on Amazon and we’re proud to be working with 3M to hold these bad actors accountable. Amazon has longstanding policies against counterfeiting and price gouging and processes in place to proactively block suspicious products and egregious prices. When we find a bad actor violating our policies, we work quickly to remove the products and take action on the bad actor, as we’ve done here, and we welcome collaboration from brands like 3M.”

The site says it has removed more than half a million product offers and suspended more than 6,000 accounts over price gouging. In its own release, 3M claims to have been involved in the removal of more than 3,000 sites featuring counterfeit products or deceitful claims.


Ford partners with Thermo Fisher on COVID-19 test kits, expands production to face masks, gowns

Ford has expanded its plan to make critical medical equipment and supplies, including a new effort to make reusable gowns from airbag materials as well as a partnership with scientific instrument provider Thermo Fisher Scientific to ramp up production of COVID-19 collection kits to test for the virus.

This broader plan highlights the latest effort by automakers and medical device manufacturers to help ease a shortage of equipment and supplies such as face shields, face masks, protective gowns and ventilators, a medical device that is used in the treatment of COVID-19, a disease caused by coronavirus.

Ford announced in March a partnership with 3M to build Powered Air-Purifying Respirators (PAPRs) as well as a separate effort to produce more than 3 million face shields at its factory in Plymouth, Mich.

On Monday, Ford provided an update on its 3M partnership and laid out new plans to produce other medical equipment. Ford will start Tuesday producing PAPRs — respirators used by healthcare workers that filter out contaminants in the air — at its Vreeland facility near Flat Rock, Mich. Paid United Auto Worker volunteers will be working to assemble the PAPR devices. Ford said it expects to be able to make 100,000 PAPR devices.

Ford to make Powered Air-Purifying Respirator

Ford will start producing an all-new PAPR design to help protect health care professionals on the front lines fighting COVID-19.

“I think our immediate focus is on the surge need that is really at the end of April, May and June, so we’re focusing on that timeframe,” Jim Baumbick, vice president of Ford Enterprise Product Line Management said during a call with reporters Monday. “What I can also say is we have very clear signals working with our partners that three on that the demand is far outpacing the supply of this critical equipment. We know that there’s incredible demand, and need for this during this short time horizon.”

Ford engineers have also been working to increase the output of PAPRs and N95 respirators at 3M’s U.S.-based manufacturing facilities. 3M has doubled its N95 production to more than 1.1 billion annually and has plans to double that again in the next 12 months, according to Mike Kesti, the global technical director of the personal safety division at 3M.

In addition to its previously announced plans to make face shields, Ford outlined three additional efforts, including face mask and gown production as well as the partnership with Thermo Fisher Scientific.

The company has started to produce face masks for its own workers to use throughout its global operations. The face masks, which are being made at Ford’s Van Dyke Transmission Plant in Sterling Heights, Mich., were developed in collaboration with the UAW and are being made for internal use to lessen the burden on an already squeezed supply chain. Ford said it is looking to have the masks certified for medical use.

Ford has also tapped supplier Joyson Safety Systems to make reusable gowns from airbag material. The automaker worked with a local hospital in Michigan to develop a pattern for the gowns. The airbag material used for the gown is nylon based and has built in coating.

“This is really a great find that we could take something that we already knew how to produce and then turn that into isolation gowns, and they are washable,” said Marcy Fisher, Ford director of global body exterior and interior engineering.

Ford-supplier Joyson Safety Systems will cut and sew 1.3 million gowns by July 4. The gowns are self-tested to federal standards and are washable up to 50 times, according to Ford.

Finally, the company said it will help Thermo Fisher Scientific expand production of COVID-19 collection kits. Ford engineers at its Kansas City Assembly Plant are helping set up additional collection kit production machinery. These engineers are also helping Thermo Fisher adapt machinery that currently runs glass vials for other products to run plastic vials required in drive-through coronavirus test collection.

Rahko raises £1.3M seed from Balderton for quantum machine learning tech

There remains a problem with the race to create a quantum computer, which is that experiments in this area can be extremely error-prone. Rahko is a new UK startup that thinks is can address this problem with what’s known as ‘Quantum machine learning’.

It’s now raised a £1.3M ($1.6M) in a seed round led by Balderton Capital, a rare move for a VC which normally only comes in at a Series A level. Joining the round is AI Seed and angel investors Charles Songhurst (former Microsoft Head of Corporate Strategy), Tom McInerney (Founder, TGM Ventures), John Spindler (CEO, Capital Enterprise) and James Field (CEO, LabGenius).

Rahko says it is building ‘quantum discovery’ capabilities for chemical simulation, which could enable groundbreaking advances in batteries, chemicals, advanced materials and drugs. It was started by cofounders Leonard Wossnig, Edward Grant, Miriam Cha and Ian Horobin.

Leo and Ed were longtime collaborators through their PhDs at University College London. They had been working on research in quantum machine learning (QML) with now lead developers Shuxiang Cao and Hongxiang Chen for several years and had been consolidating all their research into a QML platform.

They say the QML platform attracted serious attention from a tech giant and overtures were made. Leo and Ed made the decision not to give away control of the sum of their work, and decided instead to launch a business to commercialize it.

Chemical simulation is a vital capability for research that has not advanced significantly in recent years due to the limited computational power of classical computer. Rahko claims it has an arsenal of tools that may make quantum computers accessible and commercially usable at an accelerated pace, often through the use of hybrid approaches with classical computers.

Leo Wossnig, CEO, said: “Most people find quantum computers mysterious and wonder if they are going to save or break the world as we know it. In reality, quantum computing is going to unlock radical advances in areas of research and technology in which we have found ourselves stuck for some time now. Our team is excited to get together every day to work on problems that would have been impossible to solve only a couple of years ago. We are delighted to welcome on board this unique group of investors who truly share our excitement.” Earlier this year, Wossnig was the recipient of the prestigious 2019 Google Fellowship in Quantum Computing, for his achievement in computer science.

Lars Fjeldsoe-Nielsen, General Partner at Balderton Capital, said: “Rahko is one of the top teams in the world working on a complex space at the very edge of science and computing. The application of discoveries within quantum has already been profound and impacted our fundamental understanding of the world around us. The pace and rate of change in this field over the past few years has been astonishing, and we feel incredibly lucky to be supporting this exceptional team as they continue to push the boundaries of what’s possible.”

Rahko is one of several startups originating from UCL’s Computer Science programme, supported by Conception X, a venture builder for deep tech startups. It works in partnership with several of the world’s largest quantum hardware manufacturers, leading academic teams and national laboratories.

Wossnig added: “Quantum software is a relatively new field. It is growing very quickly but at this stage the field is small enough for us to know all of the best teams out there and be working with many of them. IBM and Microsoft, for instance, have large software teams but we are partners with both of them.”

The entire quantum computing industry is relying on quantum hardware maturing to a scale that will allow powerful, commercially valuable applications. It’s estimated this will be in 3-5 years. Until this happens it is a little premature to say definitively who is leading the race.