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[ClearBrain launches analytics tools focused connecting cause and effect]
Businesses need to understand cause-and-effect: If someone did X and it increased sales, or they did Y and it hurt sales. That’s why many of them turn to analytics — but Bilal Mahmood, co-founder and CEO of ClearBrain, said existing analytics platforms can’t answer that question accurately.
“Every analytics platform today is still based on a fundamental correlation model,” Mahmood said. It’s the classic correlation-versus-causation problem — you can use the data to suggest that an action and a result are related, but you can’t draw a direct cause-and-effect relationship.
That’s the problem that ClearBrain is trying to solve with its new “causal analytics” tool. As the company put it in a blog post, “Our goal was to automate this process [of running statistical studies] and build the first large-scale causal inference engine to allow growth teams to measure the causal effect of every action.”
You can read the post for (many) more details, but the gist is that Mahmood and his team claim that they can accurate draw accurate causal relationships where others can’t.
ClearBrain analytics screenshot
The idea is to use this in conjunction with A/B testing — customers look at the data to prioritize what to test next, and to make estimates about the impact of things that can’t be tested. Otherwise, Mahmood said, “If you wanted to measure the actual impact of every variable on your website and your app — the actual impact it has on conversation — it could take you years.”
When I wrote about ClearBrian last year, it was using artificial intelligence to improve ad targeting, but Mahmood said the company built the new analytics technology in response to customer demand: “People didn’t just want to know who was going to convert, they wanted to know why, and what caused them to do so.”
The causal analytics tool is currently available to early access users, with plans for a full launch in October. Mahmood said there will be a number of pricing tiers, but they’ll be structured to make the product free for many startups.
In addition to launching the analytics tool in early access, ClearBrain also announced this week that it’s raised an additional $2 million in funding from Harrison Metal and Menlo Ventures.

[Lumineye helps first responders identify people through walls]
Any first responder knows that situational awareness is key. In domestic violence disputes, hostage rescue or human trafficking situations, first responders often need help determining where humans are behind closed doors.
That’s why Megan Lacy, Corbin Hennen and Rob Kleffner developed Lumineye, a 3D-printed radar device that uses signal analysis software to differentiate moving and breathing humans from other objects, through walls.
Lumineye uses pulse radar technology that works like echolocation (how bats and dolphins communicate). It sends signals and listens for how long it takes for a pulse to bounce back. The software analyzes these pulses to determine the approximate size, range and movement characteristics of a signal.
On the software side, Lumineye’s app will tell a user how far away a person is when they’re moving and breathing. It’s one dimensional, so it doesn’t tell the user whether the subject is to the right or left. But the device can detect humans out to 50 feet in open air; that range decreases depending upon the materials placed in between, like drywall, brick or concrete.
One scenario the team gave to describe the advantages of using Lumineye was the instance of hostage rescue. In this type of situation, it’s crucial for first responders to know how many people are in a room and how far away they are from one another. That’s where the use of multiple devices and triangulation from something like Lumineye could change a responding team’s tactical rescue approach.
Machines that currently exist to make these kind of detections are heavy and cumbersome. The team behind Lumineye was inspired to manufacture a more portable option that won’t weigh down teams during longer emergency response situations that can sometimes last for up to 12 hours or overnight. The prototype combines the detection hardware with an ordinary smartphone. It’s about 10 x 5 inches and weighs 1.5 pounds.
Lumineye wants to grow out its functionality to become more of a ubiquitous device. The team of four is planning to continue manufacturing the device, selling it directly to customers.
Lumineye Device BreathingMode
(Image: Lumineye’s device can detect humans through walls using radio frequencies)
Lumineye has just started its pilot programs, and recently spent a Saturday at a FEMA event testing out the the device’s ability to detect people covered in rubble piles. The company was born out of the Boise, Idaho cohort of Stanford’s Hacking4Defense program, a course meant to connect Silicon Valley innovations with the U.S. Department of Defense and Intelligence Community. The Idaho-based startup is graduating from Y Combinator’s Summer 2019 class.

[Y Combinator-backed Holy Grail is using machine learning to build better batteries]
For a long, long time, renewable energy proponents have considered advancements in battery technology to be the Holy Grail of the industry.
Advancements in energy storage has been among the hardest to achieve economically, thanks to the incredibly tricky chemistry that’s involved in storing power.
Now, one company that’s launching from Y Combinator believes it has found the key to making batteries better. The company is called Holy Grail and it’s launching in the accelerator’s latest cohort.
With an executive team that initially included Nuno Pereira, David Pervan and Martin Hansen, Holy Grail is trying to bring the techniques of the fabless semiconductor industry to the world of batteries.
The company’s founders believe that the only way to improve battery functionality is to take a systems approach to understanding how different anodes and cathodes will work together. It sounds simple, but Pereira says the computational power hadn’t existed to take into account all of the variables that go along with introducing a new chemical to the battery mix.
“You can’t fix a battery with just a component,” Pereira says. “All of the batteries that were created and failed in the past. They create an anode, but they don’t have a chemical that works with the cathode or the electrolyte.”
For Pereira, the creation of Holy Grail is the latest step on a long road of experimentation with mechanical and chemical engineering. “As a kid I was more interested in mechanical engineering and building stuff,” he says. But as he began tinkering with cars and became fascinated with mobility, he realized that batteries were the innovation that gave the world its charge.
In 2017 Pereira founded a company called 10Xbattery, which was making high-density lithium batteries. That company, launching with what Pereira saw as a better chemistry, encapsulated the industry’s problem at large — the lack of a holistic approach to development.
So, with the help of a now-departed co-founder, Pereira founded Holy Grail. “He essentially told me, ‘Do you want to take a step back and see if there’s a better way to do this?’ ” said Pereira.
The company pitches itself as science fiction coming from the future, but it relies on a combination of what are now fairly standard (at least in the research community) tools. Holy Grail’s pitch is that it can automate much of the research and development process to create new batteries that are optimized to the specifications of end customers.
“It’s hard for a human to do the experiments that you need and to analyze multidimensional data,” says Pereira. “There are some companies that only do the machine-learning part and the computational science part and sell the results to companies. The problem is that there’s a disconnection between experimental reality and the simulations.”
Using computer modeling, chemical engineering and automated manufacturing, Holy Grail pitches a system that can get real test batteries into the hands of end customers in the mobility, electronics and utility industries orders of magnitude more quickly than traditional research and development shops.
Currently the system that Holy Grail has built out can make 700 batteries per day. The company intends to  build a pilot plant that will make batteries for electronics and drones. For automotive and energy companies, Holy Grail says it will partner with existing battery manufacturers that can support the kind of high-throughput manufacturing big orders will require.
Think of it like bringing the fabless chip design technologies and business models to the battery industry, says Pereira.
Holy Grail already has $14 million in letters of intent with potential customers, according to Pereira, and is expecting to close additional financing as it exits Y Combinator.
To date the company has been backed by the London-based early-stage investment firm Deep Science Ventures, where Pereira worked as an entrepreneur in residence.
Ultimately, the company sees its technology being applied far beyond batteries as a new platform for materials science discoveries broadly. For now, though, the focus is on batteries.
“For the low volume we sell direct,” says Pereira. “While on high-volume production, we will implement a pilot line through the system… we are able to do the research engineering with the small ones and test the big ones. In our case when we have a cell that works, it’s not something that works in a lab, it’s something that works in the final cell.”

[Y Combinator-backed Narrator wants to become the operating system for data science]
Cedric Dussud, Michael Nason, Ahmed Elsamadisi and Matthew Star (pictured above, in order) spent the summer sharing a house in San Francisco, cooking meals together and building Narrator, a startup with ambitions of becoming a universal data model fit for any company.
Narrator  is one of more than 100 startups graduating next week from Y Combinator,  the San Francisco accelerator program. Put simply, the company provides data-science-as-a-service to its customers: fellow startups.
“We provide the equivalent of a data team for the price of an analyst,” explains Narrator co-founder and director of engineering Star. “Within the first month, our clients get an infinitely scalable data system.”
Led by chief executive officer Elsamadisi, a former senior data engineer at WeWork, the Narrator founding team is made up entirely of alums of the co-working giant. The building blocks of Narrator’s subscription-based data modeling tool were developed during Elsamadisi’s WeWork tenure, where he was tasked with making sense of the company’s disorganized trove of data.
As an early addition to WeWork’s data team, Elsamadisi spent two years bringing WeWork’s data to one place, scaling the team to 40 people and ultimately creating a functional data model the soon-to-be-public company could use to streamline operations. Then in 2017, Elsamadisi had an a-ha moment. The system he created at WeWork could be applied to any data stream, he thought.
“All companies are fundamentally the same when it comes to the kinds of data they want to understand about their business,” Narrator’s Dussud tells TechCrunch. “Every startup wants to know what’s my monthly recurring revenue, why are my customers churning or whatever the case may be. The only reason they have to go hire a data team and hire a business analyst is because the way that their data is structured is specific to that company.”
All Narrator clients use the same consistent format to absorb and manage their data, saving startups time and heaps of money.
Narrator follows a long line of Y Combinator graduates that built startups catering to other startups, as the accelerator becomes more of a SaaS incubator of sorts. PagerDuty and Docker proved that YC companies could build with a strong focus on other YC companies. Brex, a recent YC grad that issues credit cards to entrepreneurs, has leveraged the same startup-focused model for big-time success.
“Why not build a company to make something that other startups can have?” Asks Dussud. “It’s hugely valuable and only big companies have access to it. Let’s make it available to everybody.”
New York-based Narrator sees a massive opportunity ahead. Every company, after all, wants to increase revenue or decrease costs, a difficult task easier accomplished with a data-driven culture.
“If you start to imagine a world where, under the hood, the structure of the data at all companies is the same, you can now start reusing a lot of the things that in the past would actually be quite complicated,” said Star. “Right now, anytime you want to start from scratch with a new data system, you are literally starting from scratch and unfortunately reinventing the wheel. If you had a standardized system, you know, a standardized model, you could start reusing a lot of really wonderful things.”
Narrator is working with 14 clients today, each using an identical data model. Their goal is for Narrator’s structure to become the standard by which all startups do data science. In other words, Narrator hopes to become the operating system for data science.
“What’s kind of amazing is whether we’re working with a financial app … a clothing rental startup or a healthcare company, they’re all using the same data model,” said Star. “Any one of those teams, if they wanted to get the same level of analysis, they would have to hire a data analyst.”
Narrator raised $1.3 million in seed funding led by Flybridge Capital Partners prior to joining YC. Hot off the heels of the accelerator program, there’s no doubt the startup will close another round of financing soon.

[Protein replacement startups are coming for food additives as Shiru launches from Y Combinator]
Shiru, a new company that’s launching from the latest batch of Y Combinator-backed startups, is joining the ranks of the businesses angling for a spot at the vanguard of the new food technology revolution.
The company was founded by Jasmin Hume, the former director of food chemistry at Just (the company formerly known as Hampton Creek) and takes its name from a homophone of the Chinese shi rou (which Hume has roughly translated to an examination of meat). At Just, Hume was working with a team that was fractionating plants to look at their physical properties to identify what products could be made from the various proteins and chemicals researchers found in the plants.
Shiru, by contrast, is using computational biology to find the ideal proteins for specific applications in the food industry.
The company’s looking at what proteins are best for creating certain kinds of qualities that are used in food additives — things like viscosity building, solubility, foam stability, emulsification and binding, according to Hume.
In some ways, Hume’s approach looks similar to the early product roadmap for Geltor, a company backed by SOSV and IndieBio that was also looking to make functional proteins. The company, which has raised over $18 million to date, shifted its attention to proteins for the beauty industry and cosmetics instead of food — potentially leaving an opening for Shiru to exploit.
Still in its early days, Shiru doesn’t have a product nailed down yet, but the science the company is exploring is increasingly well understood, and Hume says it’s looking at several different genetically engineered feedstocks — from yeasts to undisclosed strains of bacteria and fungi to make its proteins.
“We use the power of molecular design and machine learning to identify protein structures that are more functional than existing alternatives,” says Hume. “The proteins that we are screening for are inspired by nature.”
Hume’s path to founding Shiru involves quite the pedigree. Before Just, she received her doctorate in materials chemistry from New York University,  and she’d spent a stretch as a summer associate at the New York-based frontier technology-focused investment firm Lux Capital.
Hume expects to begin pilot production of initial proteins later this year and be producing small but repeatable quantities by the end of 2020.
The company hasn’t raised any outside capital before Y Combinator  and is currently in the process of raising a round, Hume said.

[Traces AI is building a less invasive alternative to facial recognition tracking]
With all of the progress we’ve seen in deep learning tech in the past few years, it seems pretty inevitable that security cameras become smarter and more capable in regards to tracking, but there are more options than we think in how we choose to pull this off.
Traces AI is a new computer vision startup, in Y Combinator’s latest batch of bets, that’s focused on helping cameras track people without relying on facial recognition data, something the founders believe is too invasive of the public’s privacy. The startup’s technology actually blurs out all human faces in frame, only relying on the other physical attributes of a person.
“It’s a combination of different parameters from the visuals. We can use your hair style, whether you have a backpack, your type of shoes and the combination of your clothing,” co-founder Veronika Yurchuk tells TechCrunch.
Tech like this obviously doesn’t scale too well for a multi-day city-wide manhunt, and leaves room for some Jason Bourne-esque criminals to turn their jackets inside out and toss on a baseball cap to evade detection. As a potential customer, why forego a sophisticated technology just to stave off dystopia? Well, Traces AI isn’t so convinced that facial recognition tech is always the best solution; they believe that facial tracking isn’t something every customer wants or needs and there should be more variety in terms of solutions.
“The biggest concern [detractors] have is, ‘Okay, you want to ban the technology that is actually protecting people today, and will be protecting this country tomorrow?’ And, that’s hard to argue with, but what we are actually trying to do is propose an alternative that will be very effective but less invasive of privacy,” co-founder Kostya Shysh tells me.
Earlier this year, San Francisco banned government agencies from the use of facial recognition software, and it’s unlikely that they will be the only city to make that choice. In our conversation, Shysh also highlighted some of the backlash to Detroit’s Project Green Light, which brought facial recognition surveillance tech city-wide.
Traces AI’s solution can also be a better option for closed venues that have limited data on the people on their premises in the first place. One use case Shysh highlighted was being able to find a lost child in an amusement park with just a little data.
“You can actually give them a verbal description, so if you say, ‘it’s a missing 10-year-old boy, and he had blue shorts and a white t shirt,’ that will be enough information for us to start a search,” Shysh says.
In addition to being a better way to promote privacy, Shysh also sees the technology as a more effective way to reduce the racial bias of these computer vision systems that have proven less adept at distinguishing non-white faces, and are thus often more prone to false positives.
“The way our technology works, we actually blur faces of the people before sending it to the cloud. We’re doing it intentionally as one of the safety mechanisms to protect from racial and gender biases as well,” Shysh says.
The co-founders say that the U.S. and Great Britain are likely going to be their biggest markets due to the high quantity of CCTV cameras, but they’re also pursuing customers in Asian countries like Japan and Singapore, where face-obscuring facial masks are often worn and can leave facial tracking software much less effective.

[Fitz Frames, with $2.5M in seed funding, wants your kid to have custom glasses]
Fitz Frames is today launching out of beta to offer affordable, custom-made glasses for families, and in particular, for children.
The company, which has raised $2.5 million in seed funding, was founded by Heidi Hertel and is led by CEO Gabriel Schlumberger. The company declined to disclose its investors, but shared that it was a mix of angel and institutional investors.
Hertel started the company after taking her children through the process of buying glasses with little to no success.
Hertel cites two main problems with buying glasses for children: 1) There isn’t much selection around style for kids, and 2) Glasses are made for kids and adults with little variation in size for kids who are in-between.
Here’s how it works:
With an Rx from a doctor or without, kids and parents hop on the Fitz Frames  app to go through a virtual try-on. While the user is going through a virtual try-on to find the right pair of glasses, the Fitz Frames app is doing a full measurement of the face using thousands of data points, including ear-height, nose shape, etc. to make sure that the end result is a comfortable, well-fitting pair of glasses.
From there, Fitz Frames sends the measurements to their manufacturing set-up in Youngstown, Ohio, where the frames are made from polyamide powder, which is 3D-printed using selective laser sintering.
Not only does the polyamide allow for a more durable, flexible frame, but the manufacturing process as a whole allows Fitz to turn around frames quickly. The goal, according to Hertel, is to turn around a pair of glasses in a week or less.
Fitz Frames are also made with no-screw hinges, opting instead for arms that pop right out of the socket and pop back in. This means repairing a pair of Fitz Frames is far easier and doesn’t require sewing hands and tiny screwdrivers.
Kids can also have their name or favorite number or address etched into the arm of the glasses.
Fitz Frames cost $95, but the company is also offering a subscription plan for parents. The idea is that kids lose and break their glasses all the time, and that small children shouldn’t have to feel responsible for something worth hundreds of dollars.
“Glasses shouldn’t have to be so precious,” said Hertel.
The subscription, which costs $185/year, includes two pairs of glasses. From there, subscribers can get unlimited frames (but not lenses, or shipping) throughout the year. In other words, your kids can lose their glasses as often as they’d like as long as you’re cool paying for the third, fourth, and so on lenses.
Fitz Frames isn’t alone. A company called Pair Eyewear is also tackling kids glasses with an approach that focuses on easily changing the style through clip-on top-frames. Warby Parker also recently got into kids’ frames.
Not unlike Warby, Fitz is also working alongside nonprofits to make glasses more accessible. The company has a partnership with Vision to Learn, which provides eye exams and glasses to kids in low-income communities, as well as a pilot program with Loving Eyes, an org that provides custom-fit eyeglasses for children with craniofacial anomalies who can’t wear conventional glasses.
Fitz Frames also has plans to launch a pop-up in the Hamptons at the end of August.