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Deep Vision announces its low-latency AI processor for the edge
Deep Vision, a new AI startup that is building an AI inferencing chip for edge computing solutions, is coming out of stealth today. The six-year-old company’s new ARA-1 processors promise to strike the right balance between low latency, energy efficiency and compute power for use in anything from sensors to cameras and full-fledged edge servers.
Because of its strength in real-time video analysis, the company is aiming its chip at solutions around smart retail, including cashier-less stores, smart cities and Industry 4.0/robotics. The company is also working with suppliers to the automotive industry, but less around autonomous driving than monitoring in-cabin activity to ensure that drivers are paying attention to the road and aren’t distracted or sleepy.
Image Credits: Deep Vision
The company was founded by its CTO Rehan Hameed and its Chief Architect Wajahat Qadeer, who recruited Ravi Annavajjhala, who previously worked at Intel and SanDisk, as the company’s CEO. Hameed and Qadeer developed Deep Vision’s architecture as part of a PhD thesis at Stanford.
“They came up with a very compelling architecture for AI that minimizes data movement within the chip,” Annavajjhala explained. “That gives you extraordinary efficiency — both in terms of performance per dollar and performance per watt — when looking at AI workloads.”
Long before the team had working hardware, though, the company focused on building its compiler to ensure that its solution could actually address its customers’ needs. Only then did they finalize the chip design.
Image Credits: Deep Vision
As Hameed told me, Deep Vision’s focus was always on reducing latency. While its competitors often emphasize throughput, the team believes that for edge solutions, latency is the more important metric. While architectures that focus on throughput make sense in the data center, Deep Vision CTO Hameed argues that this doesn’t necessarily make them a good fit at the edge.
“[Throughput architectures] require a large number of streams being processed by the accelerator at the same time to fully utilize the hardware, whether it’s through batching or pipeline execution,” he explained. “That’s the only way for them to get their big throughput. The result, of course, is high latency for individual tasks and that makes them a poor fit in our opinion for an edge use case where real-time performance is key.”
To enable this performance — and Deep Vision claims that its processor offers far lower latency than Google’s Edge TPUs and Movidius’ MyriadX, for example — the team is using an architecture that reduces data movement on the chip to a minimum. In addition, its software optimizes the overall data flow inside the architecture based on the specific workload.
Image Credits: Deep Vision
“In our design, instead of baking in a particular acceleration strategy into the hardware, we have instead built the right programmable primitives into our own processor, which allows the software to map any type of data flow or any execution flow that you might find in a neural network graph efficiently on top of the same set of basic primitives,” said Hameed.
With this, the compiler can then look at the model and figure out how to best map it on the hardware to optimize for data flow and minimize data movement. Thanks to this, the processor and compiler can also support virtually any neural network framework and optimize their models without the developers having to think about the specific hardware constraints that often make working with other chips hard.
“Every aspect of our hardware/software stack has been architected with the same two high-level goals in mind,” Hameed said. “One is to minimize the data movement to drive efficiency. And then also to keep every part of the design flexible in a way where the right execution plan can be used for every type of problem.”
Since its founding, the company has raised about $19 million and filed nine patents. The new chip has been sampling for a while, and even though the company already has a couple of customers, it chose to remain under the radar until now. The company obviously hopes that its unique architecture can give it an edge in this market, which is getting increasingly competitive. Besides the likes of Intel’s Movidius chips (and custom chips from Google and AWS for their own clouds), there are also plenty of startups in this space, including the likes of Hailo, which raised a $60 million Series B round earlier this year and recently launched its new chips, too.
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3 growth tactics that helped us surpass Noom and Weight Watchers
Many consumers might think Noom or Weight Watchers are industry leaders with their nonstop commercials, but neither is the fastest-growing weight management program.
Over the past year, nutrition app Lifesum has acquired users at nearly twice the rate of both Noom and Weight Watchers, according to statistics from Sensor Tower, the independent market intelligence for the mobile app economy.
Over this past summer, we surpassed Noom on the global scale with 45 million users. More impressively, we accomplished this without any TV buys. That’s right — no multimillion dollar ad campaigns, allowing us to redistribute precious marketing dollars to other growth projects.
Here’s a closer look at the three growth marketing tactics I credit with helping us scale Lifesum over the last 36 months. It’s a strategy any startup can use, regardless of size or budget.
Understand the different generational lenses
Generations approach products differently. It’s important for startups to understand the different generational approaches of their customers. Startups that spend time thinking and strategizing about where generational trends are going will scale faster.
Here’s a closer look at the three growth marketing tactics I credit with helping us scale Lifesum over the last 36 months. It’s a strategy any startup can use, regardless of size or budget.
Millennials and Generation Z are now the largest consumer market in the world, so you can’t ignore them if you want to scale. With Lifesum these generations have helped our brand surpass the older and well-established competitors. We achieved this by intimately understanding how they view health and fitness.
Gen Z and millennials are all about empowerment. They grew up with Google and Facebook, having information at their fingertips. They are far less likely to be moved by a TV commercial since they desire to discover the world on their own.
In our industry, we’ve learned millennials and Gen Z don’t want a one-size-fits-all weight loss program or to count calories like their parents did 20 years ago. As millennials and Gen Z started embracing keto, intermittent fasting and pescatarian diets, our nutrition team had already created tailored programs to help them stick with it.
Bypass traditional marketing methods
As a brand, it’s important to look ahead and anticipate what is coming next. This also applies to marketing your product. If you get in early with emerging marketing platforms, you will save money and potentially reach more early adopters.
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Arrikto raises $10M for its MLOps platform
Arrikto, a startup that wants to speed up the machine learning development lifecycle by allowing engineers and data scientists to treat data like code, is coming out of stealth today and announcing a $10 million Series A round. The round was led by Unusual Ventures, with Unusual’s John Vrionis joining the board.
“Our technology at Arrikto helps companies overcome the complexities of implementing and managing machine learning applications,” Arrikto CEO and co-founder Constantinos Venetsanopoulos explained. “We make it super easy to set up end-to-end machine learning pipelines. More specifically, we make it easy to build, train, deploy ML models into production using Kubernetes and intelligent intelligently manage all the data around it.”
Like so many developer-centric platforms today, Arrikto is all about “shift left.” Currently, the team argues, machine learning teams and developer teams don’t speak the same language and use different tools to build models and to put them into production.
Image Credits: Arrikto
“Much like DevOps shifted deployment left, to developers in the software development life cycle, Arrikto shifts deployment left to data scientists in the machine learning life cycle,” Venetsanopoulos explained.
Arrikto also aims to reduce the technical barriers that still make implementing machine learning so difficult for most enterprises. Venetsanopoulos noted that just like Kubernetes showed businesses what a simple and scalable infrastructure could look like, Arrikto can show them what a simpler ML production pipeline can look like — and do so in a Kubernetes-native way.
Arrikto CEO Constantinos Venetsanopoulos. Image Credits: Arrikto
At the core of Arrikto is Kubeflow, the Google -incubated open-source machine learning toolkit for Kubernetes — and in many ways, you can think of Arrikto as offering an enterprise-ready version of Kubeflow. Among other projects, the team also built MiniKF to run Kubeflow on a laptop and uses Kale, which lets engineers build Kubeflow pipelines from their JupyterLab notebooks.
As Venetsanopoulos noted, Arrikto’s technology does three things: it simplifies deploying and managing Kubeflow, allows data scientists to manage it using the tools they already know, and it creates a portable environment for data science that enables data versioning and data sharing across teams and clouds.
While Arrikto has stayed off the radar since it launched out of Athens, Greece in 2015, the founding team of Venetsanopoulos and CTO Vangelis Koukis already managed to get a number of large enterprises to adopt its platform. Arrikto currently has more than 100 customers and, while the company isn’t allowed to name any of them just yet, Venetsanopoulos said they include one of the largest oil and gas companies, for example.
And while you may not think of Athens as a startup hub, Venetsanopoulos argues that this is changing and there is a lot of talent there (though the company is also using the funding to build out its sales and marketing team in Silicon Valley). “There’s top-notch talent from top-notch universities that’s still untapped. It’s like we have an unfair advantage,” he said.
“We see a strong market opportunity as enterprises seek to leverage cloud-native solutions to unlock the benefits of machine learning,” Unusual’s Vrionis said. “Arrikto has taken an innovative and holistic approach to MLOps across the entire data, model and code lifecycle. Data scientists will be empowered to accelerate time to market through increased automation and collaboration without requiring engineering teams.”
Image Credits: Arrikto
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Strava raises $110 million, touts growth rate of 2 million new users per month in 2020
Activity and fitness tracking platform Strava has raised $110 million in new funding, in a Series F round led by TCV and Sequoia, and including participation by Dragoneer group, Madrone Capital Partners, Jackson Square Ventures and Go4it Capital. The funding will be used to propel the development of new features, and expand the company’s reach to cover even more users.
Already in 2020, Strava has seen significant growth. The company claims that it has added more than 2 million new “athletes” (how Strava refers to its users) per month in 2020. The company positions its activity tracking as focused on the community and networking aspects of the app and service, with features like virtual competitions and community goal-setting as representative of that approach.
Strava has 70 million members, according to the company, with presence in 195 countries globally. The company debuted a new Strava Metro service earlier this year, leveraging the data it collects from its users in an aggregated and anonymized way to provide city planners and transportation managers with valuable data about how people get around their cities and communities — all free for these governments and public agencies to use, once they’re approved for access by Strava.
The company’s uptick in new user adds in 2020 is likely due at least in part to COVID-19, which saw a general increase in the number of people pursuing outdoor activities, including cycling and running, particularly at the beginning of the pandemic when more aggressive lockdown measures were being put in place. As we see a likely return of many of those more aggressive measures due to surges in positive cases globally, gym closures could provoke even more interest in outdoor activity — though winter’s effect on that appetite among users in colder climates will be interesting to watch.
Strava’s app is available free on iOS and Android, with in-app purchases available for premium subscription features.
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5 key innovations taking e-scooters to a half-billion rides in 2021
Four years ago, shared e-scooters didn’t exist. Today, they’re on track to surpass half a billion rides globally by 2021, far outpacing early growth in the carbon-heavy ride-hailing industry founded by Uber in 2009.
That’s a dramatic shift in urban transportation by any measure, and it prompts a simple but important question: How did we get here?
Understanding the key developments that helped advance micromobility over the past several years can give us valuable insights not only into where the industry is headed, but about how we can successfully shape it to meet the needs of hundreds of millions of current and future riders around the world.
From vehicle design and data to safety reporting and infrastructure, these five innovative moments have helped fuel the global growth of shared e-scooters and are helping lead cities into a healthier, more sustainable future.
#1: Shared scooters launched (fall 2017)
The very first fleet of Bird e-scooters was launched in Santa Monica, California in September of 2017. Up until this point, the micromobility industry consisted almost entirely of docked and dockless bike sharing systems that were averaging approximately 35 million trips across the United States every year — more than half of them in New York City alone.
After an encouraging start, shared e-scooter riders in the U.S. took nearly 39 million trips in 2018 and another 86 million the following year. A similar trajectory is being seen across the Atlantic, as nations such as Italy, England and the Ukraine join a rapidly expanding list of countries including Germany, France, Israel, Spain, Portugal, Belgium, Denmark, Poland and others who have chosen to supplement their urban transportation networks with modern micromobility alternatives.
Shared scooters can now be found in over 200 cities on almost every continent around the world.
#2: First custom-designed shared scooters released (fall 2018)
The first e-scooter programs taught us two things very quickly: There’s high demand for this type of micromobility offering, and custom-designed vehicles are necessary to successfully meet that demand.
The fact is, shared scooters are ridden more frequently, handle more diverse road surfaces and endure more varied weather conditions than privately owned ones. That’s why Bird’s vehicle team unveiled the industry’s first custom-designed e-scooter, the Bird Zero, in October of 2018. Equipped with more battery life, better lighting, enhanced durability and more advanced GPS technology, this was the first in a series of comprehensive vehicle evolutions intended to increase safety, sustainability and lifespan — and it worked. Tens of thousands of these scooters are still in use today, and every month of continued service reduces their already low per-mile lifetime carbon emissions even further.
Subsequent custom vehicle designs, including the Bird One and Bird Two, have added onto this foundation, introducing industry-first features such as:
- On-board diagnostic sensors capable of detecting over 200 faults.
- Vehicle intelligence systems capable of running and reporting millions of autonomous fault checks per day.
- IP67 or IP68 waterproofing on batteries.
- 14,000 mile (22,500 km) battery life, resulting in more than 10 years of average everyday use.
- Mechanical design independently tested to withstand more than 60,000 curbside impacts.
#3: Comprehensive industry safety report released (spring 2019)
Safety has rightly been the most important focus, and the most discussed aspect, of shared micromobility since its inception. It’s why Bird launched the industry’s earliest and most comprehensive free helmets for all riders campaign in January of 2018, along with a host of other safety initiatives.
In April of 2019, these programs culminated in a comprehensive e-scooter safety report. This was the first in-depth look at modern micromobility systems, using accident reports and other data to demonstrate that shared scooters have risks and vulnerabilities similar to bicycles. The report laid the groundwork for cooperative safety measures to be taken by both operators and cities to ensure that not only riders and pedestrians but all road users are protected.
Over the past year and a half, we’ve used the findings contained within the report, along with others that have since echoed its findings, to imagine and develop a series of product innovations that are helping set the standard for e-scooter safety across the industry. These include:
- Shared micromobility’s first Helmet Selfie feature to promote helmet use.
- Shared micromobility’s first Warm Up Mode feature to assist new riders.
- The first and most accurate geofencing for e-scooters to create reduced-speed and no-riding zones.
- Responsible data-sharing standards and practices to help cities build new infrastructure for bikes and scooters.
#4: Open Mobility Foundation created (summer 2019)
The last bullet above is particularly important. Cities have a crucial role to play in limiting the number of cars on the road and maximizing the amount of infrastructure available for bikes and scooters. It’s a proven strategy to improve the safety of all road users that depends heavily on one critical input: reliable, standardized data.
Since our first launch, Bird has been a strong proponent of responsible data sharing with cities. What was lacking, however, was a unified body to help guide and develop mobility data standards across the micromobility industry.
All of that changed in June of 2019, when cities like Los Angeles, New York and San Francisco came together with companies like Bird and Microsoft and a consortium of nonprofit organizations called OASIS to form the Open Mobility Foundation (OMF). As chairperson and general manager of the LADOT Seleta Reynolds wrote in Forbes, the OMF platform “helps us achieve important city goals like increasing safety, equity, and health outcomes, while lowering emissions, and reducing congestion.”
These collaborative efforts to manage micromobility systems using open-source code and shared data standards might seem wonky, but they’ve had some very tangible real-world effects. In Atlanta, shared e-scooter data has been used to quadruple the city’s protected bike lanes by 2021. Santa Monica recently used scooter data to draft and pass an amendment that will add 19 new miles of separated micromobility infrastructure.
#5: UK, NY e-scooter programs approved (spring 2020)
This year’s decisions by the UK and the state of New York to legalize shared e-scooters and launch respective pilot programs may not be an innovation, but it’s a crucial development that will ensure the industry tops 500 million rides in 2021.
From an environmental and urban mobility perspective, London and New York are two of the most important cities in the world. Combined, they’re home to 17 million people and more than 10 million daily car trips. The introduction of e-scooters into these two densely packed and highly mobile cities will have a dramatic impact on daily commuter habits, particularly at a time when public transit ridership is still suffering due to COVID-19. That’s good news for cities, citizens and the environment.
The data that will be gained from such a high volume of micromobility rides won’t just help inform infrastructure improvements in New York and London. It will be added to a growing body of research that’s rapidly influencing micromobility technology and accelerating its adoption around the world.
Looking forward
So what can we learn from all of this? What will the first four years and 500 million rides of the shared e-scooter industry tell us about the future of micromobility?
First, we should expect its growth to continue. Adaptable, environmentally friendly solutions to car congestion and urban pollution were in high demand even before the global spread of the coronavirus in 2020. Now they’re proving themselves to be a necessity. Look for the relationships between cities and operators to strengthen and become more cooperative as scooters transition from a perceived recreational vehicle to an essential part of the urban transportation grid. This will include dramatic, data-informed improvements in protected infrastructure for both cyclists and scooter riders.
Second, we should anticipate that e-scooter technology will continue to develop around two key pillars: safety and sustainability. This applies as much to the form and functionality of the vehicles themselves as it does to the daily operations that manage them. Longer lifespan, improved battery performance, increased durability and enhanced diagnostics will be the benchmarks by which we measure this progress.
Finally, we should anticipate that, as the data from hundreds of millions of annual rides continues to accumulate, our understanding of urban mobility needs will become much clearer and more nuanced. Urban planning decisions will be able to be made based on street and hour-specific needs, identifying potentially dangerous areas and taking low-cost, high-impact actions to remedy them.
If current trends continue, and there’s every reason to believe that they will, the time it takes to add another half-billion e-scooter rides to the global total will very soon shrink from four years to less than one.
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Squarespace adds support for memberships and paywalled content
Squarespace is adding a new monetization option for websites built on the platform: Member Areas, where businesses can charge for access to exclusive content.
Chief Product Officer Paul Gubbay said that particularly in the midst of the pandemic, businesses on Squarespace “want to experiment with different ways to make money.” They can already use the platform to sell products and services, and even to schedule appointments, but with Member Areas, “We allow you to sell your expertise, to sell your content.”
That could, of course, mean an online publication that wants to paywall some of its articles, but it could also mean a chef who wants to charge for access to cooking videos and recipes, or a fitness instructor hoping to make money from online classes.
Group Product Manager Kimberly Lin showed me how Member Areas are integrated into a Squarespace website, allowing the website owner to assign different access requirements to different pages — some could require a recurring membership fee, while others require a one-time payment and still others can be free with registration.
Squarespace also supports different membership tiers, as well as publishing member-only podcasts and newsletters. Site creators get access to CRM data on each of their members, with plans for more segmentation tools in the future.
Squarespace is making this available as an add-on to the core website building platform, with pricing starting at $9 per month. Gubbay emphasized the “simplicity” of adding these features to an existing Squarespace website, making it easy to put “anything you want” behind a paywall.
Lin also said that by integrating with the website builder, Squarespace can offer page protection that’s “truly secure,” because visitors can’t circumvent it by simply tracking down a paywalled URL.
As an early success story, Gubbay mentioned a jewelry merchant on Squarespace that started scheduling sessions where she gives design advice, then created Member Areas with videos and other jewelry-related content.
“First and foremost, we want to make sure we have product-market fit,” Gubbay added. “But I think what we’re going to be interested in doing as we move forward is helping people understand that, guiding them to the parts of the platform where they become a multi-modal seller.”
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The DOJ has approved Mastercard’s acquisition of Finicity
Federal regulators have approved Mastercard’s acquisition of Salt Lake City-based startup Finicity, which provides open-banking APIs. The deal is expected to go for $825 million.
“We were notified that the Department of Justice completed its review of our planned acquisition of Finicity and has cleared it to move forward,” Mastercard wrote in a statement. “We are pleased to have reached this milestone.”
Finicity allows users to be able to decide how their financial information is shared and who can make money decisions on their behalf through open APIs. The buy will allow Mastercard to offer consumers and businesses more choice in these transactions, without requiring them to do heavy lifting themselves.
Finicity, according to Crunchbase, has raised nearly $80 million in known venture capital as a private company. When closed, it will be one of the largest fintech acquisitions at nearly $1 billion in 2020.
The DOJ approval comes just two weeks after the body filed an antitrust lawsuit challenging Visa’s proposed $5.3 billion buy of Plaid. Plaid, which empowers a large chunk of financial services through its data network, including Venmo and Acorns, is being accused of making Visa a monopoly in online debt services.
Plaid has denied these claims, saying that “Visa intends to defend the transaction vigorously.” The feds are also looking into Intuit’s $7 billion proposed buy of Credit Karma, which was first announced in February 2020.
The approval of the Mastercard-Finicity transaction could be a shot in the arm for fintech startup valuations. After both the Plaid and Credit Karma deals came under increasing regulatory scrutiny, it was an open questions whether big-dollar M&A was going to be an option for fintech unicorns.
If the path was closed due to regulatory concerns, fintech startups would have to either pursue earlier, smaller sales themselves, or wait for an eventual IPO. If that was the case, venture capitalists might shun putting as much capital to work in the sector. However, the Finicity approval makes it clear that not all fintech M&A worth $500 million or more is going to encounter oversight headaches. That should be welcome news for late-stage fintech valuations.
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Gretel announces $12M Series A to make it easier to anonymize data
As companies work with data, one of the big obstacles they face is making sure they are not exposing personally identifiable information (PII) or other sensitive data. It usually requires a painstaking manual effort to strip out that data. Gretel, an early-stage startup, wants to change that by making it faster and easier to anonymize data sets. Today the company announced a $12 million Series A led by Greylock. The company has now raised $15.5 million.
Gretel co-founder and CEO Alex Watson says that his company was founded to make it simpler to anonymize data and unlock data sets that were previously out of reach because of privacy concerns.
“As a developer, you want to test an idea or build a new feature, and it can take weeks to get access to the data you need. Then essentially it boils down to getting approvals to get started, then snapshotting a database, and manually removing what looks like personal data and hoping that you got everything.”
Watson, who previously worked as a GM at AWS, believed that there needed to be a faster and more reliable way to anonymize the data, and that’s why he started Gretel. The first product is an open-source, synthetic machine learning library for developers that strips out personally identifiable information.
“Developers use our open source library, which trains machine learning models on their sensitive data, then as that training is happening we are enforcing something called differential privacy, which basically ensures that the model doesn’t memorize details about secrets for individual people inside of the data,” he said. The result is a new artificial data set that is anonymized and safe to share across a business.
The company was founded last year, and they have actually used this year to develop the open-source product and build an open-source community around it. “So our approach and our go-to-market here is we’ve open-sourced our underlying libraries, and we will also build a SaaS service that makes it really easy to generate synthetic data and anonymized data at scale,” he said.
As the founders build the company, they are looking at how to build a diverse and inclusive organization, something that they discuss at their regular founders’ meetings, especially as they look to take these investment dollars and begin to hire additional senior people.
“We make a conscious effort to have diverse candidates apply, and to really make sure we reach out to them and have a conversation, and that’s paid off, or is in the process of paying off I would say, with the candidates in our pipeline right now. So we’re excited. It’s tremendously important that we avoid group think that happens so often,” he said.
The company doesn’t have paying customers, but the plan is to build off the relationships it has with design partners and begin taking in revenue next year. Sridhar Ramaswamy, the partner at Greylock who is leading the investment, says that his firm is placing a bet on a pre-revenue company because he sees great potential for a service like this.
“We think Gretel will democratize safe and controlled access to data for the whole world the way GitHub democratized source code access and control,” Ramaswamy said.
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Undock raises $1.6M to help solve your group scheduling nightmares
Over the past decade, many startups have tried (and many have failed) to rethink the way we schedule our meetings and calls. But we seem to be in a calendrical renaissance, with incumbents like Google and Outlook getting smarter and smarter and newcomers like Calendly growing significantly.
Undock, an Entrepreneurs Roundtable Accelerator-backed startup, is looking to enter the space.
The startup recently closed a $1.6 million seed round with investors that include Lightship Capital, Bessemer Venture Partners, Lerer Hippeau, Alumni Ventures Group, Active Capital, Arlan Hamilton of Backstage Capital, Sarah Imbach of PayPal/LinkedIn and several other angel investors.
For now, Undock is a Chrome extension that allows users to seamlessly see mutual availability across a group, whether or not all users in the group have Undock, all from within their email. Founder and CEO Nash Ahmed wouldn’t go into too much detail about the technology that allows Undock to accomplish this. But, on the surface, users who don’t yet have Undock can temporarily link their calendar to the individual meeting request to automatically find times that work for everyone in the group. Otherwise, they can see the suggested times of the rest of the group and mark the ones that work for them.
This is just the beginning of the journey for Undock. The company plans to launch a full-featured calendar in Q1 of 2021, that would include collaborative editing right within calendar events, and embedded video conferencing.
According to Ahmed, the most important differentiating features of Undock are that it focuses on mutual availability (not just singular availability) and that it does so right within the email client.
Image Credits: Undock
Scheduling will always be free within Undock, but the full calendar (when it’s released publicly) will have a variety of tiers starting at $10/month per user. Undock will also borrow from the Slack model and charge more for retention of information.
“The greatest challenge is definitely customer education,” said Ahmed, explaining that early on some users were confused by the product’s simplicity. “We messaged it by saying it’s like autocomplete. And early users would get into their email and then ask what to do next, or if they had to go back to Undock or to the Chrome extension. And we’d have to say ‘no, just keep typing.’ ”
The Undock team, which is Black and female-founded, numbers 18 people; 28% of the team is female, 22% are Black and 11% are LGBTQ, and the diversity of the leadership team is even higher.
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Computer vision startup Chooch.ai scores $20M Series A
Chooch.ai, a startup that hopes to bring computer vision more broadly to companies to help them identify and tag elements at high speed, announced a $20 million Series A today.
Vickers Venture Partners led the round with participation from 212, Streamlined Ventures, Alumni Ventures Group, Waterman Ventures and several other unnamed investors. Today’s investment brings the total raised to $25.8 million, according to the company.
“Basically we set out to copy human visual intelligence in machines. That’s really what this whole journey is about,” CEO and co-founder Emrah Gultekin explained. As the company describes it, “Chooch Al can rapidly ingest and process visual data from any spectrum, generating AI models in hours that can detect objects, actions, processes, coordinates, states, and more.”
Chooch is trying to differentiate itself from other AI startups by taking a broader approach that could work in any setting, rather than concentrating on specific vertical applications. Using the pandemic as an example, Gultekin says you could use his company’s software to identify everyone who is not wearing a mask in the building or everyone who is not wearing a hard hat at a construction site.
With 22 employees spread across the U.S., India and Turkey, Chooch is building a diverse company just by virtue of its geography, but as it doubles the workforce in the coming year, it wants to continue to build on that.
“We’re immigrants. We’ve been through a lot of different things, and we recognize some of the issues and are very sensitive to them. One of our senior members is a person of color and we are very cognizant of the fact that we need to develop that part of our company,” he said. At a recent company meeting, he said that they were discussing how to build diversity into the policies and values of the company as they move forward.
The company currently has 18 enterprise clients and hopes to use the money to add engineers, data scientists and begin to build out a worldwide sales team to continue to build the product and expand its go-to-market effort.
Gultekin says that the company’s unusual name comes from a mix of the words choose and search. He says that it is also an old Italian insult. “It means dummy or idiot, which is what artificial intelligence is today. It’s a poor reflection of humanity or human intelligence in humans,” he said. His startup aims to change that.
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