SignalFire Machine Learning Algorithms Pick 8 Hot Startups
Imagine if you could analyze trillions of data points using machine learning algorithms to come up with a list of the absolute best startups to invest in. That’s no small task as there are an estimated 23,000 startups in Silicon Valley alone. One startup called SignalFire is doing just that by taking unstructured data from over 2 million data sources and then using machine learning algorithms to pick the best startups to invest in. Wouldn’t you be the least bit curious to know which companies they picked? We were extremely interested to know, so we had one of our on-staff PhDs take a look on Crunchbase and lo and behold, 8 startups were listed that SignalFire has invested in so far. Here they are:
Now the amounts you see above represent total investment amounts. We don’t know exactly how much money SignalFire invested in each one, but we do know that SignalFire raised a total of $53 million from investors for the purpose of making actual investments in the companies their machine learning algorithms selected. We were fascinated by this list and immediately wanted to know just what these 8 startups are getting up to. Here’s a quick look at each company.
Snitch.io was founded less than 2 years ago by Yousef Ourabi, presently a software development manager at Amazon Web Services, who presumably told his employer he has a cool startup on the side. The Company has taken in an undisclosed amount of funding to offer a service that alerts you when your SSL certificate is about to expire for $10 a month. A SSL certificate is something most people with websites need. We’re not quite sure what the barriers to entry are for this business model but it can’t be that tough to duplicate. Presumably they need funding to market the isht out of their service offering and gain as much market share as possible, as quick as possible.
Founded in 2014, MileZero has taken in an undisclosed amount of funding to build a “last mile” logistics cloud for leading retailers and carriers around the world. “Last mile” is a term used in supply chain management that refers to the movement of goods between a transportation hub and the final destination like a home or business. One of the co-founders, Charles Griffith, developed Amazon’s delivery platform responsible for every last mile delivery worldwide. Maybe they are developing some sort of delivery app, an Uber for package delivery if you will.
Founded last year, Smartspot has taken in $1.8 million in seed funding from renowned VC firm Khosla Ventures and of course SignalFire. The Egyptian founder is a fairy tale story of someone who went from making $20 a day throwing pizzas in Brooklyn to being accepted by prestigious startup incubator firm Y Combinator for his smart workout mirrors for gyms. These aren’t exactly mirrors but large screen TVs that record your workout in real-time and use computer vision to overlay it with useful information as seen below:
The idea is that then you can have a personal trainer review it later for a fraction of the price it would cost to actually have a personal trainer attend your workout. As you would expect, you can log on to the Smartspot website and review all your workout data anytime. The Company has deployed their augmented reality workout solutions in at least 10 gyms and people seem to be loving it.
We talked about Momentum Machines and their burger robot and now we have a pizza robot to tell you about. Founded in September 2015, Zume Pizza has taken in an undisclosed amount of seed funding to set up a pizza company where the pizzas are made by robots. If that wasn’t enough, they’ve embarked on getting approval to have pizza delivery trucks with ovens that the pizza will finish cooking in when the delivery truck pulls up to your driveway. Here are some robot pizzas to make you hungry:
Nom nom nom. The main value proposition for Zume is not that robots made your pizza but that they only use premium ingredients that are never frozen such as produce sourced from local farms. The Company recently celebrated their 10,000th delivery.
Update 11/2/2018 Zume has raised $375 million in Series C funding reportedly by Softbank Vision Fund, to support their growth and hiring. This brings the company’s total funding to $423 million to date.
Founded in 2014, Frame.io has taken in $12.2 million in funding so far to develop a video sharing and collaboration tool. “We replace the hodgepodge of Dropbox for file sharing, Vimeo for video review and email for communication, but that’s just a start…” says the Company, and apparently they have over 200,000 people using the platform so far. Here’s a screenshot of the tool take from the Company’s homepage of the tool:
While in this example the tool is used to share “surf videos”, other users might be musicians making a music video, directors making a movie, or any other project that requires people to share multimedia clips. SignalFire’s first investment in Frame.io was their seed funding round ($2.2 mill.) in October 2015 and just a few weeks ago they also participated in a Series A funding round ($10 mill.).
Founded last year, PropelPLM is a Product Lifecycle Management (PLM) tool that’s built entirely on Salesforce, and because Salesforce lives in the cloud, this tool lives in the cloud too. The Company sheds some light on their value proposition with this cool cloud-shaped diagram with icons and buzzwords:
It’s pretty much just a $4.2 million Salesforce customization, nothing to get that excited about unless you’re a product manager.
Founded just last year, Jyve has taken in nearly $6 million in seed funding which they closed in March this year. They appear to be running in stealth mode and don’t say too much on their website except that they plan to “Revolutionize the CPG industry by providing infrastructure formerly only available to larger companies“. They also go on to elaborate a bit on their hoodie-wearing culture as follows:
When we were starting out, we had no uniforms. A handful of our early Jyvers went to the mall and made Jyve t-shirts. They designed and paid for these t-shirts on their own.
Wow. Startup employees paying for things with their own money. How hardcore is that? We also found a trademark they filed which stated they plan to provide a “Downloadable mobile software application that permits individuals to accept jobs in the fields of merchandising and sales posted by branded consumer packaged goods companies and complete tasks by following a customized script“. Here’s a screenshot of their app:
So pretty much just an industry-specific work-on-demand app then or is there more to it? We’ll just have to wait to wait and see.
Update 1/24/19: Jyve has raised $20 million to support its growth. This brings the company’s total funding to $35 million to date.
Founded in 2011, ClassDojo has taken in just over $31 million to develop an app that promotes collaboration between students and parents. Incredibly, the app is already being used by over 85,000 schools in the United States.
The app hasn’t generated any revenues yet but it will “always be free for teachers” so you can see where that business model is heading.
Update 2/28/19: ClassDojo has raised $35 million in Series C funding co-led by GSV and SignalFire to fuel the expansion of ClassDojo’s free communications app and to drive its efforts to monetize its service by way of a new service called Beyond School. This brings the company’s total funding to $66.1 million to date.
So there you have the 8 startups SignalFire picked by using their machine learning algorithms to analyze a massive big data set of over a trillion data points. However, just because their algorithm selects a startup, it doesn’t mean that SignalFire automatically gets to invest in it. They need to establish a relationship with the founders or current investors and ask to be included in an investment round. This makes us wonder just how many companies they shortlisted which they weren’t able to invest in. It will be interesting to see how much SignalFire raises for their next investment round and what startups their machine learning algorithms will identify next.
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