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SambaNova raises $676M at a $5.1B valuation to double down on cloud-based AI software for enterprises
Artificial intelligence technology holds a huge amount of promise for enterprises — as a tool to process and understand their data more efficiently; as a way to leapfrog into new kinds of services and products; and as a critical stepping stone into whatever the future might hold for their businesses. But the problem for many enterprises is that they are not tech businesses at their core, so bringing on and using AI will typically involve a lot of heavy lifting. Today, one of the startups building AI services is announcing a big round of funding to help bridge that gap.
SambaNova — a startup building AI hardware and integrated systems that run on it that only officially came out of three years in stealth last December — is announcing a huge round of funding today to take its business out into the world. The company has closed on $676 million in financing, a Series D that co-founder and CEO Rodrigo Liang has confirmed values the company at $5.1 billion.
The round is being led by SoftBank, which is making the investment via Vision Fund 2. Temasek and the government of Singapore Investment Corp. (GIC), both new investors, are also participating, along with previous backers BlackRock, Intel Capital, GV (formerly Google Ventures), Walden International and WRVI, among other unnamed investors. (Sidenote: BlackRock and Temasek separately kicked off an investment partnership yesterday, although it’s not clear if this falls into that remit.)
Co-founded by two Stanford professors, Kunle Olukotun and Chris Ré, and Liang, who had been an engineering executive at Oracle, SambaNova has been around since 2017 and has raised more than $1 billion to date — both to build out its AI-focused hardware, which it calls DataScale, and to build out the system that runs on it. (The “Samba” in the name is a reference to Liang’s Brazilian heritage, he said, but also the Latino music and dance that speaks of constant movement and shifting, not unlike the journey AI data regularly needs to take that makes it too complicated and too intensive to run on more traditional systems.)
SambaNova on one level competes for enterprise business against companies like Nvidia, Cerebras Systems and Graphcore — another startup in the space which earlier this year also raised a significant round. However, SambaNova has also taken a slightly different approach to the AI challenge.
In December, the startup launched Dataflow-as-a-Service as an on-demand, subscription-based way for enterprises to tap into SambaNova’s AI system, with the focus just on the applications that run on it, without needing to focus on maintaining those systems themselves. It’s the latter that SambaNova will be focusing on selling and delivering with this latest tranche of funding, Liang said.
SambaNova’s opportunity, Liang believes, lies in selling software-based AI systems to enterprises that are keen to adopt more AI into their business, but might lack the talent and other resources to do so if it requires running and maintaining large systems.
“The market right now has a lot of interest in AI. They are finding they have to transition to this way of competing, and it’s no longer acceptable not to be considering it,” said Liang in an interview.
The problem, he said, is that most AI companies “want to talk chips,” yet many would-be customers will lack the teams and appetite to essentially become technology companies to run those services. “Rather than you coming in and thinking about how to hire scientists and hire and then deploy an AI service, you can now subscribe, and bring in that technology overnight. We’re very proud that our technology is pushing the envelope on cases in the industry.”
To be clear, a company will still need data scientists, just not the same number, and specifically not the same number dedicating their time to maintaining systems, updating code and other more incremental work that comes managing an end-to-end process.
SambaNova has not disclosed many customers so far in the work that it has done — the two reference names it provided to me are both research labs, the Argonne National Laboratory and the Lawrence Livermore National Laboratory — but Liang noted some typical use cases.
One was in imaging, such as in the healthcare industry, where the company’s technology is being used to help train systems based on high-resolution imagery, along with other healthcare-related work. The coincidentally-named Corona supercomputer at the Livermore Lab (it was named after the 2014 lunar eclipse, not the dark cloud of a pandemic that we’re currently living through) is using SambaNova’s technology to help run calculations related to some COVID-19 therapeutic and antiviral compound research, Marshall Choy, the company’s VP of product, told me.
Another set of applications involves building systems around custom language models, for example in specific industries like finance, to process data quicker. And a third is in recommendation algorithms, something that appears in most digital services and frankly could always do to work a little better than it does today. I’m guessing that in the coming months it will release more information about where and who is using its technology.
Liang also would not comment on whether Google and Intel were specifically tapping SambaNova as a partner in their own AI services, but he didn’t rule out the prospect of partnering to go to market. Indeed, both have strong enterprise businesses that span well beyond technology companies, and so working with a third party that is helping to make even their own AI cores more accessible could be an interesting prospect, and SambaNova’s DataScale (and the Dataflow-as-a-Service system) both work using input from frameworks like PyTorch and TensorFlow, so there is a level of integration already there.
“We’re quite comfortable in collaborating with others in this space,” Liang said. “We think the market will be large and will start segmenting. The opportunity for us is in being able to take hold of some of the hardest problems in a much simpler way on their behalf. That is a very valuable proposition.”
The promise of creating a more accessible AI for businesses is one that has eluded quite a few companies to date, so the prospect of finally cracking that nut is one that appeals to investors.
“SambaNova has created a leading systems architecture that is flexible, efficient and scalable. This provides a holistic software and hardware solution for customers and alleviates the additional complexity driven by single technology component solutions,” said Deep Nishar, senior managing partner at SoftBank Investment Advisers, in a statement. “We are excited to partner with Rodrigo and the SambaNova team to support their mission of bringing advanced AI solutions to organizations globally.”
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6sense raises $125M at a $2.1B valuation for its ‘ID graph’, an AI-based predictive sales and marketing platform
AI has become a fundamental cornerstone of how tech companies are building tools for salespeople: they are useful for supercharging (and complementing) the abilities of talented humans, or helping them keep themselves significantly more organised; even if in some cases — as with chatbots — they are replacing them altogether. In the latest development, 6sense, one of the pioneers in using AI to boost the sales and marketing experience, is announcing a major round of funding that underscores the traction AI tools are seeing in the sales realm.
The startup has raised $125 million at a valuation of $2.1 billion, a Series D being led by D1 Capital Partners, with Sapphire Ventures, Tiger Global and previous backer Insight Partners also participating.
The company plans to use the funding to expand its platform and its predictive capabilities across a wider range of sources.
For some context, this is a huge jump for the company compared to its last fundraise: at the end of 2019, when it raised $40 million, it was valued at a mere $300 million, according to data from PitchBook.
But it’s not a big surprise: at a time when a lot of companies are going through “digital transformation” and investing in better tools for their employees to work more efficiently remotely (especially important for sales people who might have previously worked together in physical teams), 6sense is on track for its fourth year of more than 100% growth, adding 100 new customers in the fourth quarter alone. It caters to small, medium, and large businesses, and some of its customers include Dell, Mediafly, Sage and SocialChorus.
The company’s approach speaks to a classic problem that AI tools are often tasked with solving: the data that sales people need to use and keep up to date on customer accounts, and critically targets, lives in a number of different silos — they can include CRM systems, or large databases outside of the company, or signals on social media.
While some tools are being built to handle all of that from the ground up, 6sense takes a different approach, providing a way of ingesting and utilizing all of it to get a complete picture of a company and the individuals a salesperson might want to target within it. It takes into account some of the harder nuts to crack in the market, such as how to track “anonymous buying behavior” to a more concrete customer name; how to prioritizes accounts according to those most likely to buy; and planning for multi-channel campaigns.
6sense has patented the technology it uses to achieve this and calls its approach building an “ID graph.” (Which you can think of as the sales equivalent of the social graph of Facebook, or the knowledge graph that LinkedIn has aimed to build mapping skills and jobs globally.) The key with 6sense is that it is building a set of tools that not just sales people can use, but marketers too — useful since the two sit much closer together at companies these days.
Jason Zintak, the company’s CEO (who worked for many years as a salesperson himself, so gets the pain points very well), referred to the approach and concept behind 6sense as “revtech”: aimed at organizations in the business whose work generates revenue for the company.
“Our AI is focused on signal, identifying companies that are in the market to buy something,” said Zintak in an interview. “Once you have that you can sell to them.”
That focus and traction with customers is one reason investors are interested.
“Customer conversations are a critical part of our due diligence process, and the feedback from 6sense customers is among the best we’ve heard,” said Dan Sundheim, founder and chief investment officer at D1 Capital Partners, in a statement. “Improving revenue results is a goal for every business, but it’s easier said than done. The way 6sense consistently creates value for customers made it clear that they deliver a unique, must-have solution for B2B revenue teams.”
Teddie Wardi at Insight highlights that AI and the predictive elements of 6sense’s technology — which have been a consistent part of the product since it was founded — are what help it stand out.
“AI generally is a buzzword, but here it is a key part of the solution, the brand behind the platform,” he said in an interview. “Instead of having massive funnels, 6sense switches the whole thing around. Catching the right person at the right time and in the right context make sales and marketing more effective. And the AI piece is what really powers it. It uses signals to construct the buyer journey and tell the sales person when it is the right time to engage.”
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TaxDown banks ~$3M for tech that helps people get their taxes done
Madrid-based TaxDown, which automates income tax filing by calculating regional deductions due to users so they don’t have to navigate complex tax rules themselves, has raised €2.4 million (~$3M) in seed funding.
US-based FJ Labs has joined TaxDown’s investment board as it closes the seed round. It says all its previous investors participated in the round, including James Argalas (Presidio Union); Abac Nest, Abac’s venture capital business; Baldomero Falcones, the former Chairman at Mastercard; and the founders of Jobandtalent, Juan Urdiales and Felipe Navío (another Madrid-based startup).
For the past three years TaxDown been offering a service in Spain but is now eyeing international expansion, as well as further growth in its home market.
Last year, it says it managed more than €29M in taxes for users — delivering savings of €4M+ to users.
Its target is to hit 500,000 users in Spain this year. While international expansion is planned for the second half of 2021, with TaxDown saying it’s focused on other European and Latin American markets.
“From the beginning, our ambition has been to help people fill in their taxes all over the world. That is why we developed our proprietary software/tax language that allows a tax expert with no coding capabilities to translate the tax law into calculation and logic that can be interpreted by our backend seamlessly,” says Enrique García, CEO and co-founder. “This tax language allowed us to launch in Spain in 4 months with only one tax consultant. We are confident that we can launch a new country in only 6 months.”
“The tax filing process is far from being simple,” he goes on, explaining how its tech simplifies income tax filing in Spain. “Currently, when using the Spanish Tax Agency tax-filling tool, taxpayers need to manually apply deductions on their tax forms. The problem is, with national regional deductions being different in each region in Spain, taxpayers often do not even know they’re entitled to those deductions. Thus, by not applying them to their tax form, they lose money. What TaxDown does is leverage the advanced Spanish Tax Agency technology, which offers an API to request the financial data related to a taxpayer — always with prior authorization from the user — with 2.000+ datapoints.
“Once we have that, our algorithm ‘RITA’ is capable of understanding the user’s personal and financial data, select the optimum questions that the user needs to answer — an average of 9 over a database of 3.000+ – and precisely calculate the tax return, with no errors.”
“Technology is the heart of TaxDown,” he adds. “Besides our algorithm RITA that has been trained with over 40.000+ tax returns, today we also use AI to help our ‘taxers’ with tips on how to lower future tax bills, and we have started working on live income tax simulation for our users throughout the entire year.”
García says TaxDown calculated more than 42,000 tax returns last year with a team of just two in-house tax experts — thanks to proprietary internal tools which allow them to handle this scale (by being “80x more efficient than the Spanish average”, as he puts it). He adds that further efficiency gains are expected.
“We have developed a machine-learning tool that flags the tax returns that need to be reviewed before filing based on historical data. Thus, we continuously increase the percentage of tax returns that are automatically submitted with no manual intervention,” he tells TechCrunch, adding: “Thanks to this feature, we expect to improve our efficiency at least 5x versus last year.”
According to García, TaxDown has never had any filings rejected for inaccuracies because he says its algorithms continually run tests and validate the information with the authorities. “Furthermore, our technology can flag errors in real time in case that there is a discrepancy, so our tax experts can manually check the tax return form if needed,” he adds.
Its business model — currently — is a sort of twist on freemium, in that it will only charge users if the income tax savings it calculates for them exceed €35.
García says that so far an average of three out of 10 users see financial savings from using its tool — but he suggests it’s not only savings that motivate users; he says they also want reassurance that they are taking “the best approach with their taxes: doing them effortlessly, correctly, with all the guarantees, tapping for experts’ live help at any time, ensuring the best result they can get, and of course knowing that we have their backs in case of an audit”.
Given that wider relationship it’s building with users, TaxDown sees potential to evolve its business model by expanding to offer additional fintech services, such as financial advice, in the future.
“Our vision goes far beyond income tax return preparation, we believe that tax data is becoming one of the most valuable data assets for people (take Trump’s tax returns for example), and we want to assess our ’taxers’ based on the best and more qualitative information that we can get,” says García. “Therefore, in the future we want to be a trusted financial advisor not just for taxes, but for personal finances as well. We believe we are well positioned to be an intermediary between our users and financial institutions.”
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Equity Monday: More money for fintech, Deliveroo’s IPO and AI startups
Hello and welcome back to Equity, TechCrunch’s venture capital-focused podcast where we unpack the numbers behind the headlines.
This is Equity Monday, our weekly kickoff that tracks the latest private market news, talks about the coming week, digs into some recent funding rounds and mulls over a larger theme or narrative from the private markets. You can follow the show on Twitter here and myself here — and make sure to check out our Friday show that featured the Square-Tidal deal, some recent IPOs and some super-neat rounds.
Much like today’s show, if I am being honest. Here’s the rundown:
- Coursera filed to go public, which could help set the tone for edtech founders and startups regarding their own valuations and fundraising prospects.
- Bay Area startups did not leave.
- And the world of AI venture-capital funding shows that while there’s plenty of capital, the number of startups being founded in the space is dipping.
- Starling raised a huge new round in the fintech space, and Deliveroo dropped some financial numbers on its path to going public in short-order.
- Postscript raised $35 million for its Shopify-SMS service; Praava Health raised $10.6 million to bring better healthcare to Bangladesh.
A packed kickoff to what promises to be a packed week!
Equity drops every Monday at 7:00 a.m. PST, Wednesday, and Friday at 6:00 AM PST, so subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts!
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Veo raises $25M for AI-based cameras that record and analyze football and other team sports
Sports have been among some of the most popular and lucrative media plays in the world, luring broadcasters, advertisers and consumers to fork out huge sums to secure the chance to watch (and sponsor) their favorite teams and athletes.
That content, unsurprisingly, also typically costs a ton of money to produce, narrowing the production and distribution funnel even more. But today, a startup that’s cracked open that model with an autonomous, AI -based camera that lets any team record, edit and distribute their games, is announcing a round of funding to build out its business targeting the long tail of sporting teams and fixtures.
Veo Technologies, a Copenhagen startup that has designed a video camera and cloud-based subscription service to record and then automatically pick out highlights of games, which it then hosts on a platform for its customers to access and share that video content, has picked up €20 million (around $24.5 million) in a Series B round of funding.
The funding is being led by Danish investor Chr. Augustinus Fabrikker, with participation from U.S.-based Courtside VC, France’s Ventech and Denmark’s SEED Capital. Veo’s CEO and co-founder Henrik Teisbæk said in an interview that the startup is not disclosing its valuation, but a source close to funding tells me that it’s well over $100 million.
Teisbæk said that the plan will be to use the funds to continue expanding the company’s business on two levels. First, Veo will be digging into expanding its U.S. operations, with an office in Miami.
Second, it plans to continue enhancing the scope of its technology: The company started out optimising its computer vision software to record and track the matches for the most popular team sport in the world, football (soccer to U.S. readers), with customers buying the cameras — which retail for $800 — and the corresponding (mandatory) subscriptions — $1,200 annually — both to record games for spectators, as well as to use the footage for all kinds of practical purposes like training and recruitment videos. The key is that the cameras can be set up and left to run on their own. Once they are in place, they can record using wide-angles the majority of a soccer field (or whatever playing space is being used) and then zoom and edit down based on that.
Image Credits: Veo Technologies
Now, Veo is building the computer vision algorithms to expand that proposition into a plethora of other team-based sports, including rugby, basketball and hockey, and it is ramping up the kinds of analytics that it can provide around the clips that it generates, as well as the wider match itself.
Even with the slowdown in a lot of sporting activity this year due to COVID — in the U.K. for example, we’re in a lockdown again where team sports below professional leagues, excepting teams for disabled people, have been prohibited — Veo has seen a lot of growth.
The startup currently works with some 5,000 clubs globally ranging from professional sports teams through to amateur clubs for children, and it has recorded and tracked 200,000 games since opening for business in 2018, with a large proportion of that volume in the last year and in the U.S.
For a point of reference, in 2019, when we covered a $6 million round for Veo, the startup had racked up 1,000 clubs and 25,000 games, pointing to customer growth of 400% in that period.
The COVID-19 pandemic has indeed altered the playing field — literally and figuratively — for sports in the past year. Spectators, athletes and supporting staff need to be just as mindful as anyone else when it comes to spreading the coronavirus.
That’s not just led to a change in how many games are being played, but also for attendance: witness the huge lengths that the NBA went to last year to create an extensive isolation bubble in Orlando, Florida, to play out the season, with no actual fans in physical seats watching games, but all games and fans virtually streamed into the events as they happened.
That NBA effort, needless to say, came at a huge financial cost, one that any lesser league would never be able to carry, and so that predicament has led to an interesting use case for Veo.
Pre-pandemic, the Danish startup was quietly building its business around catering to the long tail of sporting organizations which — even in the best of times — would be hard-pressed to find the funds to buy cameras and/or hire videographers to record games, not just an essential part of how people can enjoy a sporting event, but useful for helping with team development.
“There is a perception that football is already being recorded and broadcast, but in the U.K. (for example) it’s only the Premier League,” Teisbæk said. “If you go down one or two steps from that, nothing is being recorded.” Before Veo, to record a football game, he added, “you need a guy sitting on a scaffold, and time and money to then cut that down to highlights. It’s just too cumbersome. But video is the best tool there is to develop talent. Kids are visual learners. And it’s a great way to get recruited, sending videos to colleges.”
Those use cases then expanded with the pandemic, he said. “Under coronavirus rules, parents cannot go out and watch their kids, and so video becomes a tool to follow those matches.”
‘We’re a Shopify, not an Amazon’
The business model for Veo up to now has largely been around what Teisbæk described as “the long tail theory”, which in the case of sports works out, he said, as “There won’t be many viewers for each match, but there are millions of matches out there.” But if you consider how a lot of high school sports will attract locals beyond those currently attached to a school — you have alumni supporters and fans, as well as local businesses and neighborhoods — even that long tail audience might be bigger than one might imagine.
Veo’s long-tail focus has inevitably meant that its target users are in the wide array of amateur or semi-pro clubs and the people associated with them, but interestingly it has also spilled into big names, too.
Veo’s cameras are being used by professional soccer clubs in the Premier League, Spain’s La Liga, Italy’s Serie A and France’s Ligue 1, as well as several clubs in the MLS such as Inter Miami, Austin FC, Atlanta United and FC Cincinnati. Teisbæk noted that while this might never be for primary coverage, it’s there to supplement for training and also be used in the academies attached to those organizations.
The plan longer term, he said, is not to build its own media empire with the trove of content that it has amassed, but to be an enabler for creating that content for its customers, who can in turn use it as they wish. It’s a “Shopify, not an Amazon,” said Teisbæk.
“We are not building the next ESPN, but we are helping the clubs unlock these connections that are already in place by way of our technology,” he said. “We want to help them capture and stream their matches and their play for the audience that is there today.”
That may be how he views the opportunity, but some investors are already eyeing up the bigger picture.
Vasu Kulkarni, a partner at Courtside VC — a firm that has focused (as its name might imply) on backing a lot of different sports-related businesses, with The Athletic, Beam (acquired by Microsoft) and many others in its portfolio — said that he’d been looking to back a company like Veo, building a smart, tech-enabled way to record and parse sports in a more cost-effective way.
“I spent close to four years trying to find a company trying to do that,” he said.
“I’ve always been a believer in sports content captured at the long tail,” he said. Coincidentally, he himself started a company called Krossover in his dorm room to help somewhat with tracking and recording sports training. Krossover eventually was acquired by Hudl, a competitor to Veo.
“You’ll never have the NBA finals recorded on Veo, there is just too much at stake, but when you start to look at all the areas where there isn’t enough mass media value to hire people, to produce and livestream, you get to the point where computer vision and AI are going to be doing the filming to get rid of the cost.”
He said that the economics are important here: the camera needs to be less than $1,000 (which it is) and able to produce something demonstrably better than “a parent with a Best Buy camcorder that was picked up for $100.”
Kulkarni thinks that longer term there could definitely be an opportunity to consider how to help clubs bring that content to a wider audience, especially using highlights and focusing on the best of the best in amateur games — which of course are the precursors to some of those players one day being world-famous elite athletes. (Think of how exciting it is to see the footage of Michael Jordan playing as a young student for some context here.) “AI will be able to pull out the best 10-15 plays and stitch them together for highlight reels,” he said, something that could feasibly find a market with sports fans wider than just the parents of the actual players.
All of that then feeds a bigger market for what has started to feel like an insatiable appetite for sports, one that, if anything, has found even more audience at a time when many are spending more time at home and watching video overall. “The more video you get from the sport, the better the sport gets, for players and fans,” Teisbæk said.
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VSCO acquires mobile app Trash to expand into AI-powered video editing
VSCO, the popular photo and video editing app, today announced it has acquired AI-powered video editing app Trash, as the company pushes further into the video market. The deal will see Trash’s technology integrated into the VSCO app in the months ahead, with the goal of making it easier for users to creatively edit their videos.
Trash, which was co-founded by Hannah Donovan and Genevieve Patterson, cleverly uses artificial intelligence technology to analyze multiple video clips and identify the most interesting shots. It then stitches your clips together automatically to create a final product. In May, Trash added a feature called Styles that let users pick the type of video they wanted to make — like a recap, a narrative, a music video or something more artsy.
After Trash creates its AI-powered edit, users can opt to further tweak the footage using buttons on the screen that let them change the order of the clips, change filters, adjust the speed or swap the background music.
Image Credits: Trash
With the integration of Trash’s technology, VSCO envisions a way to make video editing even more approachable for newcomers, while still giving advanced users tools to dig in and do more edits, if they choose. As VSCO co-founder and CEO Joel Flory explains, it helps users get from that “point zero of staring at their Camera Roll…to actually putting something together as fast as possible.”
“Trash gets you to the starting point, but then you can dive into it and tweak [your video] to really make it your own,” he says.
The first feature to launch from the acquisition will be support for multi-clip video editing, expected in a few months. Over time, VSCO expects to roll out more of Trash’s technologies to its user base. As users make their video edits, they may also be able to save their collection of tweaks as “recipes,” like VSCO currently supports for photos.
“Trash brings to VSCO a deep level of personalization, machine learning and computer vision capabilities for mobile that we believe can power all aspects of creation on VSCO, both now and for future investments in creativity,” says Flory.
The acquisition is the latest in a series of moves VSCO has made to expand its video capabilities.
At the end of 2019, VSCO picked up video technology startup Rylo. A few months later, it had leveraged the investment to debut Montage, a set of tools that allowed users to tell longer video stories using scenes, where they could also stack and layer videos, photos, colors and shapes to create a collage-like final product. The company also made a change to its app earlier this year to allow users to publish their videos to the main VSCO feed, which had previously only supported photos.
More recently, VSCO has added new video effects, like slowing down, speeding up or reversing clips and new video capture modes.
As with its other video features, the new technology integrations from Trash will be subscriber-only features.
Today, VSCO’s subscription plan costs $19.99 per year, and provides users with access to the app’s video editing capabilities. Currently, more than 2 million of VSCO’s 100 million+ registered users are paid subscribers. And, as a result of the cost-cutting measures and layoffs VSCO announced earlier this year, the company has now turned things around to become EBITDA positive in the second half of 2020. The company says it’s on the path to profitability, and additional video features like those from Trash will help.
Image Credits: Trash
VSCO’s newer focus on video isn’t just about supporting VSCO’s business model, however, it’s also about positioning the company for the future. While the app grew popular during the Instagram era, today’s younger users are more often posting videos to TikTok instead. According to Apple, TikTok was the No. 2 most downloaded free app of the year — ahead of Instagram, Facebook and Snapchat.
Though VSCO doesn’t necessarily envision itself as only a TikTok video prep tool, it does have to consider that growing market. Similar to TikTok, VSCO’s user base consists of a younger, Gen Z demographic; 75% of VSCO’s user base is under 25, for example, and 55% of its subscribers are also under 25. Combined, its user base creates more than 8 million photos and videos per day, VSCO says.
As a result of the acquisition, Trash’s standalone app will shut down on December 18.
Donovan will join VSCO as Director of Product and Patterson as Head of Applied Research. Other Trash team members, including Karina Bernacki, Chihyu Chang and Drew Olbrich, will join as Chief of Staff, Engineering Manager and Sr. Software Engineer for iOS, respectively.
“We both believe in the power of creativity to have a healthy and positive impact on people’s lives,” said Donovan, in Trash’s announcement. “Additionally, we have similar audiences of Gen Z casual creators; and are focused on giving people ways to express themselves and share their version of the world while feeling seen, safe, and supported,” she said.
Trash had raised a total of $3.3 million — a combination of venture capital and $500,000 in grants — from BBG, Betaworks, Precursor and Dream Machine, as well as the National Science Foundation. (Multiple TechCrunch connections here: BBG is backed by our owner Verizon Media, while Dream Machine is the fund created by former TechCrunch editor Alexia Bonatsos.)
“Han and Gen and the Trash team have always paid attention to the needs of creators first and foremost. My hope is that the VSCO and Trash partnership will turn all of us into creators, and turn the gigabytes of latent videos on our phones from trash to treasures,” said Bonatsos, in a statement about the deal.
Flory declined to speak to the deal price, but characterized the acquisition as a “win-win for both the Trash team and for VSCO.”
Updated 12/3/20, 11:27 AM ET: VSCO alerted us that Patterson’s title is being updated to “Head of Applied Research.” We’ve updated the article accordingly.
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ultimate.ai scores $20M for a supportive approach to customer service automation
Ultimate.ai, a virtual customer service agent builder, has closed a $20 million Series A round of funding, led by Omers Ventures with participation from Felicis Ventures and existing investors HV Capital, and Maki.vc — bringing its total raised to date to more than $25 million.
The European startup’s flagship claim for the data-ingesting bot-builder platform is it’s capable of automating up to 80% of customer support interactions.
The focus, as tends to be the case for all these customer service conversational AI plays, is freeing (human) support agents from dealing with dull, repetitive stuff — so they can apply their (less limited) skills to more complex, consultative or emotionally demanding customer queries.
When we last spoke to the Helsinki- and Berlin-based startup, back in 2018 for a $1.3 million seed round, it described itself as a “language-agnostic” conversational AI — having started out with the hard (linguistic) challenge of Finnish — claiming that gave it an edge in a competitive space with customers in non-English speaking markets. (Though it did also tackle English too.)
Two years on the startup’s marketing focus is broader; today it talks about its customer service automation platform as an “AI-first” ‘no code’ tool — sating it wants to empower b2c users to get the most out of AI by helping them design virtual agents that can usefully handle complex customer interactions.
ultimate.ai will hand-hold you through the process of building a super savvy customer service robot, is the pitch.
Co-founder and CEO Reetu Kainulainen claims it’s always been “no code and intuitive” — though there’s now a handy reference label to align what it’s doing with a wider b2b trend. (‘No code’ or ‘low code’ referring to a digital tool-building movement that aims to widen access to powerful technologies like AI without the need for the user to possess deep technical know-how in order to make useful use of them.)
“Everything we build is to guide users to creating the best virtual agents. The whole user journey — discovery, design, expansion — is all within ultimate.ai,” Kainulainen tells TechCrunch.
“In the past two years, we have been laser focused on building a very deep customer service automation platform — one that goes beyond simple FAQ answers in chat — and enables brands to design complex, personalized workflows that can be deployed across all digital support channels.
“We believe that customer service automation will be its own category in the future and so we are working hard to define what that means today.”
As an example, Kainulainen points to “one click” integration with “any major CRM” (including Salesforce and Zendesk) — which lets customers quickly import existing customer support logs so ultimate.ai’s platform can analyze the data to help them build a useful bot.
“Immediately, you are shown a breakdown of your most common customer service cases and the impact automation can have for your business,” he goes on, saying the platform shows templates and “best practices” to help the customer design their automation workflows — “tailored for your cases and industry”.
Once a virtual agent is live users can run A/B tests via the platform to check and optimize performance — and, here too, the promise is further hand-holding, with Kainulainen saying it will “proactively suggests new cases and data to improve your virtual agent”.
“Where we are very strong is in large-scale customer support organizations, who are looking for a holistic, advanced automation platform that can be managed and implemented by non-technical users,” he says.
“The bigger picture is that each of our competitors views the opportunity more narrowly than ultimate.ai does: Our best competitors are either focused on chatbots only, or otherwise limited to the ecosystem of their mother company. Our vision has always been the big picture: Of automation becoming one of the primary means of providing customer service.”
Having multilingual smarts remains an advantage, with ultimate.ai’s virtual agents able to handle interactions in over 20 languages at this point.
“Our market — the customer service automation market — has a lot of players,” Kainulainen goes on, name-checking the likes of Ada Support and Einstein Bots (Salesforce’s own solution) as key competitors.
“This is because it is new and, until recently, solutions were so early that there were virtually no barriers to entry. But the market has changed a lot in the last four years. There are now only a handful of players globally that are worth paying attention to and we are one of them.”
The 2016-founded startup is hitting the nail on the head for a growing number of customers — with close to 100 signed up to its platform at this point, including the likes of Deezer, Telia, Footasylum, and Finnair. Per Kainulainen, it works best for “b2c brands with large (and often repetitive) customer service volumes”.
“This is where automation can provide a huge impact from day one and really free up people to take on more creative and challenging work. We have a broad customer base of close to 100 great brands… and do particularly well in industries like retail/ecommerce, telecommunications and travel,” he adds.
It’s enjoyed a major growth spurt this year, as businesses of all stripes were forced to ramp up their attention to online customer interactions as the coronavirus pandemic became an engine for digital activity.
Customer retention has also risen in priority for many businesses, as a highly contagious virus and public health safety measures put in place to reduce its spread, flipped markets into recession — which Kainulainen points to as another growth driver.
Overall, he says it’s tripled ARR over the last 12 months (albeit, it was the same growth story last year too). Plus it’s tripled headcount to deal with the COVID-19 effect.
Now ultimate.ai is gearing up for fresh growth — saying it’s expecting major developments next year.
“COVID-19 has… prompted one of the most accelerated periods of change in the customer service industry,” says Kainulainen, predicting 2021 will bring “immense innovation” in the space — and that “booming” automation technologies will take “center stage”.
Of course it’s a convenient narrative for a customer service chatbot maker to tell.
But COVID-19 is clearly accelerating digital transformation of consumer focused businesses — a movement that, logically, pumps demand for smarter tools to handle online customer support. So those positioned to harness new momentum for customer service automation — by being able to offer an accessible, scalable and effective product (as ultimate.ai claims it does) — are sitting pretty in the middle of a pandemic.
“We believe that the best product will win this market,” adds Kainulainen. “We have a big vision for what we want ultimate.ai to be. Market maturity for our technology has accelerated massively in 2020, achieving in one year what could have probably taken five. We will capitalize on that by building more, faster.”
The Series A funding will go on sales and marketing, with a planned market push in North America and a desire to go deeper throughout Europe, as well as being ploughed into further product development.
And while — clearly — not every potential b2c customer will be able to ‘automagic’ away 80% of their customer support pings, Kainulainen argues ultimate.ai can still offer a compelling sales pitch to businesses with more “consultative” customer support needs, where automation will only be able to play a far more limited role.
“There’s often a strong correlation between how consultative a customer service organization needs to be and how highly trained and experienced their team is. In other words, it is often the case that organizations with ‘lower bound’ automation potential also only need 10% automation to still drive a huge ROI,” he suggests.
“For example, one of our customers is a large national pharmacy group, where customer service agents are qualified pharmacists who provide prescription medical advice. Here, the goal isn’t to achieve a very high automation rate but rather to automate basic, repetitive processes to free up the pharmacists for more challenging tasks that better use their capabilities.
“For this customer, in addition to the automation of simple requests (which alone provides a huge value) our real-time answer recommendations help pharmacists respond faster and easier.”
Commenting on the Series A in a statement, Omers Ventures managing partner, Jambu Palaniappan, dubbed the startup’s growth “truly spectacular”, as well as lauding its “world-class team” and founders “with a strong vision and unrivalled knowledge of AI”.
“There are numerous chatbot companies out there but ultimate.ai represents something much bigger because at its core is an automation company with massive potential,” he added. “We look forward to working with Sarah, Reetu, Jaakko, and Markus as they expand internationally and advance their deep product capabilities even further.”
“The customer service industry is undergoing an automation revolution. In ultimate.ai, we saw a vision that’s bold enough to lead the way,” added Aydin Senkut, founder and managing partner of Felicis Ventures, in another supporting statement. “We believe that, just in the same way that category leaders have defined marketing and sales automation, ultimate.ai will do the same for customer service.”
Jambu Palaniappan, managing partner at Omers Ventures, will join the ultimate.ai board. Aydin Senkut, founder and managing partner of Felicis Ventures, will join as an investor, alongside former head of Airbnb for Business Mark McCabe, and former EVP global sales of payment giant Adyen, Thijn Lamers.
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Greyparrot bags $2.2M seed to scale its AI for waste management
London-based Greyparrot, which uses computer vision AI to scale efficient processing of recycling, has bagged £1.825 million (~$2.2M) in seed funding, topping up the $1.2M in pre-seed funding it had raised previously. The latest round is led by early stage European industrial tech investor Speedinvest, with participation from UK-based early stage b2b investor, Force Over Mass.
The 2019 founded startup — and TechCrunch Disrupt SF battlefield alum — has trained a series of machine learning models to recognize different types of waste, such as glass, paper, cardboard, newspapers, cans and different types of plastics, in order to make sorting recycling more efficient, applying digitization and automation to the waste management industry.
Greyparrot points out that some 60% of the 2BN tonnes of solid waste produced globally each year ends up in open dumps and landfill, causing major environmental impact. While global recycling rates are just 14% — a consequence of inefficient recycling systems, rising labour costs, and strict quality requirements imposed on recycled material. Hence the major opportunity the team has lit on for applying waste recognition software to boost recycling efficiency, reduce impurities and support scalability.
By embedding their hardware agnostic software into industrial recycling processes Greyparrot says it can offer real-time analysis on all waste flows, thereby increasing efficiency while enabling a facility to provide quality guarantee to buyers, mitigating against risk.
Currently less than 1% of waste is monitored and audited, per the startup, given the expensive involved in doing those tasks manually. So this is an application of AI that’s not so much taking over a human job as doing something humans essentially don’t bother with, to the detriment of the environment and its resources.
Greyparrot’s first product is an Automated Waste Monitoring System which is currently deployed on moving conveyor belts in sorting facilities to measure large waste flows — automating the identification of different types of waste, as well as providing composition information and analytics to help facilities increase recycling rates.
It partnered with ACI, the largest recycling system integrator in South Korea, to work on early product-market fit. It says the new funding will be used to further develop its product and scale across global markets. It’s also collaborating with suppliers of next-gen systems such as smart bins and sorting robots to integrate its software.
“One of the key problems we are solving is the lack of data,” said Mikela Druckman, co-founder & CEO of Greyparrot in a statement. “We see increasing demand from consumers, brands, governments and waste managers for better insights to transition to a more circular economy. There is an urgent opportunity to optimise waste management with further digitisation and automation using deep learning.”
“Waste is not only a massive market — it builds up to a global crisis. With an increase in both world population and per capita consumption, waste management is critical to sustaining our way of living. Greyparrot’s solution has proven to bring down recycling costs and help plants recover more waste. Ultimately it unlocks the value of waste and creates a measurable impact for the environment,” added Marie-Hélène Ametsreiter, lead partner at Speedinvest Industry, in another statement.
Greyparrot is sitting pretty in another aspect — aligning with several strategic areas of focus for the European Union, which has made digitization of legacy industries, industrial data sharing, investment in AI, plus a green transition to a circular economy core planks of its policy plan for the next five+ years. Just yesterday the Commission announced a €750BN pan-EU support proposal to feed such transitions as part of a wider coronavirus recovery plan for the trading bloc.
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Kaizo raises $3M for its AI-based tools to improve customer service support teams
CRM has for years been primarily a story of software to manage customer contacts, data to help agents do their jobs, and tools to manage incoming requests and outreach strategies. Now to add to that we’re starting to see a new theme: apps to help agents track how they work and to work better.
Today comes the latest startup in that category, a Dutch company called Kaizo, which uses AI and gamification to provide feedback on agents’ work, tips on what to do differently, and tools to set and work to goals — all of which can be used remotely, in the cloud. Today, it is announcing $3 million in a seed round of funding co-led by Gradient — Google’s AI venture fund — and French VC Partech.
And along with the seed round, Kaizo (which rebranded last week from its former name, Ticketless) is announcing that Christoph Auer-Welsbach, a former partner at IBM Ventures, is joining the company as a co-founder, alongside founder Dominik Blattner.
Although this is just a seed round, it’s coming after a period of strong growth for the company. Kaizo has already 500 companies including Truecaller, SimpleSurance, Miro, CreditRepairCloud, Justpark, Festicket and Nmbrs are using its software, covering “thousands” of customer support agents, which use a mixture of free and paid tools that integrate with established CRM software from the likes of Salesforce, Zendesk and more.
Customer service, and the idea of gamifying it to motivate employees, might feel like the last thing on people’s minds at the moment, but it is actually timely and relevant to our current state in responding to and living with the coronavirus.
People are spending much more time at home, and are turning to the internet and remote services to get what they need, and in many cases are finding that their best-laid plans are now in freefall. Both of these are driving a lot of traffic to sites and primarily customer support centers, which are getting overwhelmed with people reaching out for help.
And that’s before you consider how customer support teams might be impacted by coronavirus and the many mandates we’ve had to stay away from work, and the stresses they may be under.
“In our current social climate, customer support is an integral part of a company’s stability and growth that has embraced remote work to meet the demands of a globalized customer-base,” said Dominik Blattner, founder of Kaizo, in a statement. “With the rise of support teams utilizing a digital workplace, providing standards to measure an agent’s performance has never been more important. KPIs provide these standards, quantifying the success, achievement and contribution of each team member.”
On a more general level, Kaizo is also changing the conversation around how to improve one’s productivity. There has been a larger push for “quantified self” platforms, which has very much played out both in workplaces and in our personal lives, but a lot of services to track performance have focused on both managers and employees leaning in with a lot of input. That means if they don’t set aside the time to do that, the platforms never quite work the way they should.
This is where the AI element of Kaizo plays a key role, by taking on the need to proactively report into a system.
“This is how we’re distinct,” Auer-Welsbach said in an interview. “Normally KPIs are top-down. They are about people setting goals and then reporting they’ve done something. This is a bottom-up approach. We’re not trying to change employees’ behaviour. We plug into whatever environment they are using, and then our tool monitors. The employee doesn’t have to report or measure anything. We track clicks on the CRM, ticketing, and more, and we analyse all that.” He notes that Kaizo is looking at up to 50 datapoints in its analysis.
“We’re excited about Kaizo’s novel approach to applying AI to existing ticket data from platforms like Zendesk and Salesforce to optimize the customer support workflow,” said Darian Shirazi, General Partner at Gradient Ventures, in a statement. “Using machine learning, Kaizo understands which behaviors in customer service tickets lead to better outcomes for customers and then guides agents to replicate that using ongoing game mechanics. Customer support and service platforms today are failing to leverage data in the right way to make the life of agents easier and more effective. The demand Kaizo has seen since they launched on the Zendesk Marketplace shows agents have been waiting for such a solution for some time.”
Kaizo is not the only startup to have identified the area of building new services to improve the performance of customer support teams. Assembled earlier this month also raised $3.1 million led by Stripe for what it describes as the “operating system” for customer support.
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RealityEngines launches its autonomous AI service
RealityEngines.AI, an AI and machine learning startup founded by a number of former Google executives and engineers, is coming out of stealth today and announcing its first set of products.
When the company first announced its $5.25 million seed round last year, CEO Bindu Reddy wasn’t quite ready to disclose RealityEngines’ mission beyond saying that it planned to make machine learning easier for enterprises. With today’s launch, the team is putting this into practice by launching a set of tools that specifically tackle a number of standard enterprise use cases for ML, including user churn predictions, fraud detection, sales lead forecasting, security threat detection and cloud spend optimization. For use cases that don’t fit neatly into these buckets, the service also offers a more general predictive modeling service.
Before co-founding RealiyEngines, Reddy was the head of product for Google Apps and general manager for AI verticals at AWS. Her co-founders are Arvind Sundararajan (formerly at Google and Uber) and Siddartha Naidu (who founded BigQuery at Google). Investors in the company include Eric Schmidt, Ram Shriram, Khosla Ventures and Paul Buchheit.
As Reddy noted, the idea behind this first set of products from RealityEngines is to give businesses an easy entry into machine learning, even if they don’t have data scientists on staff.
Besides talent, another issue that businesses often face is that they don’t always have massive amounts of data to train their networks effectively. That has long been a roadblock for many companies that want to see what AI can do for them but that didn’t have the right resources to do so. RealityEngines overcomes this by creating realistic synthetic data that it can then use to augment a company’s existing data. In its tests, this creates models that are up to 15% more accurate than models that were trained without the synthetic data.
“The most prominent use of generative adversarial networks — GANS — has been to create deepfakes,” said Reddy. “Deepfakes have captured the public’s imagination by highlighting how easy it to spread misinformation with these doctored videos and images. However, GANS can also be applied to productive and good use. They can be used to create synthetic data sets which when then be combined with the original data, to produce robust AI models even when a business doesn’t have much training data.”
RealityEngines currently has about 20 employees, most of whom have a deep background in ML/AI, both as researchers and practitioners.
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