artificial intelligence
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Introhive raises $100M for AI-powered sales tools to help companies build ‘relationship graphs’
By its nature, sales is one of the most social faces of a business, so it’s no surprise that there are tools being built for sales teams that are tapping into some of the most interesting dynamics of the world of social networking, and that the startups that are doing this most successfully are making a killing.
In the latest example, a startup out of Canada called Introhive — which has built an AI engine that ingests huge amounts of data from across disparate applications to help companies (and specifically anyone in their organization that is selling to someone) to build better “relationship graphs” for target organizations — is announcing $100 million in funding.
Growth equity firm PSG is leading the round, with The Business Development Bank of Canada (BDC), Evergreen Capital and Mavan Capital Partners also participating.
The company is not disclosing valuation but CEO and co-founder Jody Glidden tells me the company is doing well. It has raised about $150 million to date and is doubling revenues every year for the last several with a platform used by large enterprises — PwC, Colliers International, Wilson Sonsini Goodrich & Rosati, Plante Moran and Clark Nexsen are a few of them. Typical deployments range between 10,000 and 100,000 seats — it’s not just people with “sales” in their job titles using Introhive — and customer retention is currently at 95%.
The idea for Introhive came as many do to enterprise startup founders: they identify something that doesn’t quite work as they want it to, and then start a new company to try to fix it. In the case of Glidden, he and Stewart Walchli were at RIM (the old parent of BlackBerry), which had acquired a previous startup of theirs called Chalk Media.
Although they had just joined a much bigger company (it was 2008, and BlackBerry was still far from being completely killed off by Google and Apple) Glidden said he was surprised to see how hard it was to tap its vast troves of information to find prospective sales leads.
“We realized there were a whole lot of problems with sales people at RIM not able to hit their revenue numbers,” he recalled, and so they started asking themselves some questions. “Are they bringing in right lead data? Are they able to be as intelligent as they can be?” It took some years — four, exactly — and perhaps the rise of Facebook and its focus on the “social graph,” for them to land on how to articulate the problem. They needed to “unlock relationship graph in CRM,” Glidden said.
And Introhive was the company that they formed in 2012 to address that. The company not only provides a way to better leverage CRM-related data to find the best targets for particular products or services, but it also provides analytics to the team to measure how people are doing, and over time also helps predict “winnability”.
But that was not immediate: It took several years to build out its AI platform, Glidden said, with a lot of trial and error to ensure that the data that Introhive ingested was structured correctly to match up with other information to yield productive information.
“We ran into big problems in the first years because there were so many potential systems to tap into, homegrown or otherwise, for certain info. We effectively spent a lot of time building our own version of MuleSoft to fix that,” he said with a laugh. “But since it’s also something we use for our customers we ended up employing hundreds of engineers to build this underpinning layer to understand it all.”
As a result, it took between four and five years for Introhive to make its own first sale, and in the process the whole company almost went under, he recalled. “It took a long time to get that engine running because if you are automating data that is wrong 35% of the time, you won’t keep your customers.”
The machine is more well-oiled today, of course, and is on a roll to bring in more functions to work off the data trove that it has built.
There is something about the service that reminds me a bit of LinkedIn or ZoomInfo — which you may use in your own work, or come across when Googling someone online for some reason (hey — I’m not asking why here) — for providing some kind of data base/org chart of people connected to a business. But to be very clear, the data that Introhive builds for a customer stays with that customer, and doesn’t go anywhere else.
Glidden says that there are no plans to build any kind of “freemium” version of the service, or one that anyone can tap as a SaaS, but rather to remain focused on helping larger enterprises make better sense of their data and how it can better inform the wider concept of sales.
That in itself raises an interesting point about Introhive and business in general. When you consider a company like PwC, there are likely many people who specifically might hold a job title with the word “sales” in it, but just as many whose jobs are predicated on closing deals, consultants and partners for example, who do not, but might just as easily benefit from having better visibility of a “relationship graph” of people connected to buying products at a business they are working with, or want to work with. Sales is more than just about salespeople for many organizations.
And for that reason, you can guess that one interesting aspect of Introhive is if it might evolve these tools over time to tackle other parts of an organization and how it works. Similar to the social graphs of social media, which map out how people can be connected to one another, relationship graphs in the workplace potentially resonate well beyond signing a deal, too. Business intelligence and marketing automation are already in the mix for the company.
“Introhive is on the forefront of helping grow sales and customers through its visionary, AI-powered revenue acceleration platform built for companies of all sizes and complexity. It seamlessly improves business operations across multiple departments by helping teams reduce time on manual inputs and giving them advanced insights on where they can generate more revenue, build more relationships and easily identify what great sales reps are doing that average reps aren’t,” said PSG managing director, Rick Essex. “The team’s acumen and highly capital-efficient model has set the company on a clear path for growth, and we’re proud to partner with them on this journey.”
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Solar concentration startup Heliogen basks in $108M of new funding
Sunlight is a great source of energy, but it rarely gets hot enough to fry an egg, let alone melt steel. Heliogen aims to change that with its high-tech concentrated solar technique, and has raised more than a hundred million dollars to test its 1,000-degree solar furnace at a few participating mines and refineries.
We covered Heliogen when it made its debut in 2019, and the details in that article still get at the core of the company’s tech. Computer vision techniques are used to carefully control a large set of mirrors, which reflect and concentrate the sun’s light to the extent that it can reach in excess of 1,000 degrees Celsius, almost twice what previous solar concentrators could do. “It’s like a death ray,” founder Bill Gross explained then.
That lets the system replace fossil fuels and other legacy systems in many applications where such temperatures are required, for example mining and smelting operations. By using a Heliogen concentrator, they could run on sunlight during much of the day and only rely on other sources at night, potentially halving their fuel expenditure and consequently both saving money and stepping toward a greener future.
Both goals hint at why utilities and a major mining and steel-making company are now investors. Heliogen raised a $25 million A-2, led by Prime Movers Lab, but soon also pulled together a much larger “bridge extension round” in their terminology of $83 million that brought in the miner ArcelorMittal, Edison International, Ocgrow Ventures, A.T. Gekko and more.
The money will be used both to continue development of the “Sunlight Refinery,” as Heliogen calls it, and deploy some actual on-site installations that would work in real production workflows at scale. “We are constantly making design and cost improvements to increase efficiency and decrease costs,” a representative of the company told me.
One of those pilot sites will be in Boron, California, where Rio Tinto operates a borates mine and will include Heliogen’s tech as part of its usual on-site processes, according to an MOU signed in March. Another MOU with ArcelorMittal will “evaluate the potential of Heliogen’s products in several of ArcelorMittal’s steel plants.” Facilities are planned in the U.S., MENA and Asia Pacific areas.
Beyond mining and smelting, the technique could be used to generate hydrogen in a zero-carbon way. That would be a big step toward building a working hydrogen infrastructure for next-generation fuel supply, since current methods make it difficult to do without relying on fossil fuels in the first place. And no doubt there are other industrial processes that could benefit from a free and zero-carbon source of high heat.
“We’re being granted the resources to do more projects that address the most carbon-intensive human activities and work toward our goals of lowering the price and emissions of energy for everyone on the planet,” Gross said in a release announcing the round(s). “We thank all of our investors for enabling us to pursue our mission and offer the world technology that will allow it to achieve a post-carbon economy.”
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Fraud protection startup nSure AI raises $6.8M in seed funding
Fraud protection startup nSure AI has raised $6.8 million in seed funding, led by DisruptiveAI, Phoenix Insurance, AXA-backed venture builder Kamet, Moneta Seeds and private investors.
The round will help the company bolster the predictive AI and machine learning algorithms that power nSure AI’s “first of its kind” fraud protection platform. Prior to this round, the company received $550,000 in pre-seed funding from Kamet in March 2019.
The Tel Aviv-headquartered startup, which currently has 16 employees, provides fraud detection for high-risk digital goods, such as electronic gift cards, airline tickets, software and games. While most fraud detection tools analyze each online transaction in an attempt to decide which purchases to approve and decline, nSure AI’s risk engine leverages deep learning techniques to accurately identify fraudulent transactions.
NSure AI, which is backed by insurance company AXA, said it has a 98% approval rating on average for purchases, compared to an industry average of 80%, allowing retailers to recapture nearly $100 billion a year in revenue lost by declining legitimate customers. The company is so confident in its technology that it will accept liability for any fraudulent transaction allowed by the platform.
Founders Alex Zeltcer and Ziv Isaiah started the company after experiencing the unique challenges faced by retailers of digital assets. The first week of their online gift card business found that 40% of sales were fraudulent, resulting in chargebacks. The founders began to develop their own platform for supporting the sale of high-risk digital goods after no other fraud detection service met their needs.
Zeltcer, co-founder and chief executive, said the investment “enables us to register thousands of new merchants, who can feel confident selling higher-risk digital goods, without accepting fraud as a part of business.”
NSure AI, which currently monitors and manages millions of transactions every month, has approved close to $1 billion in volume since going live in 2019.
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Enterprise AI platform Dataiku launches managed service for smaller companies
Dataiku is going downstream with a new product today called Dataiku Online. As the name suggests, Dataiku Online is a fully managed version of Dataiku. It lets you take advantage of the data science platform without going through a complicated setup process that involves a system administrator and your own infrastructure.
If you’re not familiar with Dataiku, the platform lets you turn raw data into advanced analytics, run some data visualization tasks, create data-backed dashboards and train machine learning models. In particular, Dataiku can be used by data scientists, but also business analysts and less technical people.
The company has been mostly focused on big enterprise clients. Right now, Dataiku has more than 400 customers, such as Unilever, Schlumberger, GE, BNP Paribas, Cisco, Merck and NXP Semiconductors.
There are two ways to use Dataiku. You can install the software solution on your own, on-premise servers. You can also run it on a cloud instance. With Dataiku Online, the startup offers a third option and takes care of setup and infrastructure for you.
“Customers using Dataiku Online get all the same features that our on-premises and cloud instances provide, so everything from data preparation and visualization to advanced data analytics and machine learning capabilities,” co-founder and CEO Florian Douetteau said. “We’re really focused on getting startups and SMBs on the platform — there’s a perception that small or early-stage companies don’t have the resources or technical expertise to get value from AI projects, but that’s simply not true. Even small teams that lack data scientists or specialty ML engineers can use our platform to do a lot of the technical heavy lifting, so they can focus on actually operationalizing AI in their business.”
Customers using Dataiku Online can take advantage of Dataiku’s pre-built connectors. For instance, you can connect your Dataiku instance with a cloud data warehouse, such as Snowflake Data Cloud, Amazon Redshift and Google BigQuery. You can also connect to a SQL database (MySQL, PostgreSQL…), or you can just run it on CSV files stored on Amazon S3.
And if you’re just getting started and you have to work on data ingestion, Dataiku works well with popular data ingestion services. “A typical stack for our Dataiku Online Customers involves leveraging data ingestion tools like FiveTran, Stitch or Alooma, that sync to a cloud data warehouse like Google BigQuery, Amazon Redshift or Snowflake. Dataiku fits nicely within their modern data stacks,” Douetteau said.
Dataiku Online is a nice offering to get started with Dataiku. High-growth startups might start with Dataiku Online as they tend to be short on staff and want to be up and running as quickly as possible. But as you become bigger, you could imagine switching to a cloud or on-premise installation of Dataiku. Employees can keep using the same platform as the company scales.
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Slintel scores $20M Series A as buyer intelligence tool gains traction
One clear outcome of the pandemic was that it pushed more people to do their shopping online, and that was as true for B2B as it was for B2C. Knowing which of your B2B customers are most likely to convert puts any sales team ahead of the game. Slintel, a startup providing that kind of data, announced a $20 million Series A today.
The company has attracted some big-name investors, with GGV leading the round and Accel, Sequoia and Stellaris also participating. The investment brings the total raised to over $24 million, including a $4.2 million seed round from last November.
That’s a quick turnaround from seed to A, and company founder and CEO Deepak Anchala says that while he had plenty of runway left from the seed round, the demand was such that it seemed prudent to take the A money sooner than he had planned. “So we had enough cash in the bank, but investors came to us and we got a pretty good valuation compared to the previous round, so we decided to take it and use that money to go faster,” Anchala said.
Certainly the market dynamics were working in Slintel’s favor. Without giving revenue details, Anchala said that revenue grew 5x last year in the middle of the worst of the pandemic. He says that meant buyers were spending less time with sales and marketing folks to understand products and more time online researching on their own.
“So what Slintel does as a product is we mine buyer insights. We understand where the buyers are in their journey, what their pain points are, what products they use, what they need and when they need it. So we understand all of this to create a 360-degree view of the buyer that you provide these insights to sales and marketing teams to help them sell better,” he said.
After growing at such a rapid clip last year, the company expected more modest growth this year at perhaps 3x, but with the added investment, he expects to grow faster again. “With the funding we’re actually looking at much bigger numbers. We’re looking at 5x in our revenue this year, and also trying for 4x revenue next year.”
He says that the money gives him the opportunity to improve the product and put more investment into marketing, which he believes will contribute to additional sales. Since the round closed six weeks ago, he says that he has increased his advertising budget and also hopes to attract customers via SEO, free tools on the company website and events.
The company had 45 employees at the time of its seed round in November and has more than doubled that number in the interim, to 100 spread out across 10 cities. He expects to double again by this time next year as the company is growing quickly. As a global company with some employees in India and some in the U.S., he intends to be remote-first even after offices begin to reopen in different areas. He says that he plans to have company gatherings each quarter to let people gather in person on occasion.
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Decades-old ASCII adventure NetHack may hint at the future of AI
Machine learning models have already mastered Chess, Go, Atari games and more, but in order for it to ascend to the next level, researchers at Facebook intend for AI to take on a different kind of game: the notoriously difficult and infinitely complex NetHack.
“We wanted to construct what we think is the most accessible ‘grand challenge’ with this game. It won’t solve AI, but it will unlock pathways towards better AI,” said Facebook AI Research’s Edward Grefenstette. “Games are a good domain to find our assumptions about what makes machines intelligent and break them.”
You may not be familiar with NetHack, but it’s one of the most influential games of all time. You’re an adventurer in a fantasy world, delving through the increasingly dangerous depths of a dungeon that’s different every time. You must battle monsters, navigate traps and other hazards, and meanwhile stay on good terms with your god. It’s the first “roguelike” (after Rogue, its immediate and much simpler predecessor) and arguably still the best — almost certainly the hardest.
(It’s free, by the way, and you can download and play it on nearly any platform.)
Its simple ASCII graphics, using a g for a goblin, an @ for the player, lines and dots for the level’s architecture, and so on, belie its incredible complexity. Because Nethack, which made its debut in 1987, has been under active development ever since, with its shifting team of developers expanding its roster of objects and creatures, rules, and the countless, countless interactions between them all.
And this is part of what makes NetHack such a difficult and interesting challenge for AI: It’s so open-ended. Not only is the world different every time, but every object and creature can interact in new ways, most of them hand-coded over decades to cover every possible player choice.
NetHack with a tile-based graphics update – all the information is still available via text.
“Atari, Dota 2, StarCraft 2… the solutions we’ve had to make progress there are very interesting. NetHack just presents different challenges. You have to rely on human knowledge to play the game as a human,” said Grefenstette.
In these other games, there’s a more or less obvious strategy to winning. Of course it’s more complex in a game like Dota 2 than in an Atari 800 game, but the idea is the same — there are pieces the player controls, a game board of environment, and win conditions to pursue. That’s kind of the case in NetHack, but it’s weirder than that. For one thing, the game is different every time, and not just in the details.
“New dungeon, new world, new monsters and items, you don’t have a save point. If you make a mistake and die you don’t get a second shot. It’s a bit like real life,” said Grefenstette. “You have to learn from mistakes and come to new situations armed with that knowledge.”
Drinking a corrosive potion is a bad idea, of course, but what about throwing it at a monster? Coating your weapon with it? Pouring it on the lock of a treasure chest? Diluting it with water? We have intuitive ideas about these actions, but a game-playing AI doesn’t think the way we do.
The depth and complexity of the systems in NetHack are difficult to explain, but that diversity and difficulty make the game a perfect candidate for a competition, according to Grefenstette. “You have to rely on human knowledge to play the game,” he said.
People have been designing bots to play NetHack for many years that rely not on neural networks but decision trees as complex as the game itself. The team at Facebook Research hopes to engender a new approach by building a training environment that people can test machine learning-based game-playing algorithms on.
NetHack screens with labels showing what the AI is aware of.
The NetHack Learning Environment was actually put together last year, but the NetHack Challenge is only just now getting started. The NLE is basically a version of the game embedded in a dedicated computing environment that lets an AI interact with it through text commands (directions, actions like attack or quaff)
It’s a tempting target for ambitious AI designers. While games like StarCraft 2 may enjoy a higher profile in some ways, NetHack is legendary and the idea of building a model on completely different lines from those used to dominate other games is an interesting challenge.
It’s also, as Grefenstette explained, a more accessible one than many in the past. If you wanted to build an AI for StarCraft 2, you needed a lot of computing power available to run visual recognition engines on the imagery from the game. But in this case the entire game is transmitted via text, making it extremely efficient to work with. It can be played thousands of times faster than any human could with even the most basic computing setup. That leaves the challenge wide open to individuals and groups who don’t have access to the kind of high-power setups necessary to power other machine learning methods.
“We wanted to create a research environment that had a lot of challenges for the AI community, but not restrict it to only large academic labs,” he said.
For the next few months, NLE will be available for people to test on, and competitors can basically build their bot or AI by whatever means they choose. But when the competition itself starts in earnest on October 15, they’ll be limited to interacting with the game in its controlled environment through standard commands — no special access, no inspecting RAM, etc.
The goal of the competition will be to complete the game, and the Facebook team will track how many times the agent “ascends,” as it’s called in NetHack, in a set amount of time. But “we’re assuming this is going to be zero for everyone,” Grefenstette admitted. After all, this is one of the hardest games ever made, and even humans who have played it for years have trouble winning even once in a lifetime, let alone several times in a row. There will be other scoring metrics to judge winners in a number of categories.
The hope is that this challenge provides the seed of a new approach to AI, one that more fundamentally resembles actual human thinking. Shortcuts, trial and error, score-hacking, and zerging won’t work here — the agent needs to learn systems of logic and apply them flexibly and intelligently, or die horribly at the hands of an enraged centaur or owlbear.
You can check out the rules and other specifics of the NetHack Challenge here. Results will be announced at the NeurIPS conference later this year.
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Whatfix nabs $90M to help workers onboard and get the most out of their IT stacks
“Digital transformation” has been on the mind of many an organization in the last year: the pandemic and the shift it’s brought to how we work are speeding up investments in new apps, infrastructure and work practices to improve productivity regardless of where we sit all day. Now, it looks like we’re on to the next stage of that journey: actually figuring out how to adopt and run with all that new tech.
In a sign of the times, today a startup called Whatfix — which has built a platform that helps make better use of tech investments by giving chatbot-style guidance to users on how to use apps, with the option also to apply AI to understand what a person is doing to suggest what actions to take next — is announcing $90 million in funding. It will use the money to continue expanding its tech platform and hiring more talent to meet demand, said CEO Khadim Batti, who co-founded the company with Vara Kumar (CTO), in an interview this week.
Sources close to the company — co-headquartered in San Jose and Bangalore — confirmed that the Series D round was made at a valuation of around $600 million, triple Whatfix’s value in its Series C round last year.
That sharp rise is due in part to the state of the market today, but also the company’s growth within that bigger trend. Whatfix today has some 500 global customers on its books, The Netherlands Red Cross, Experian, Sentry Financial Services, Cardinal Health Canada, BMC Software Inc., and Bausch & Lomb among them. Some 75% of its business is coming out of the U.S., with another 18% from Europe. Revenues in the last six months have been growing at a rate of 100% quarter-on-quarter.
“This pandemic has proven an inflection point for adoption,” said Batti (pictured above, left with Kumar, right).
This latest tranche of equity funding is coming from a mix of financial and strategic investors.
SoftBank’s Vision Fund 2 is leading the round, with Eight Roads Ventures, Sequoia Capital India, Dragoneer Investment Group, F-Prime Capital and Cisco Investments also investing. The company has raised just under $140 million in total.
“Digital adoption solutions” — the general term describing what Whatfix has built — have become a popular solution for enterprises that have found themselves in an IT pickle, Batti said.
“We’ve seen more than $500 billion spent on enterprise software, with areas like SaaS growing very fast. There is so much there, and every employee has access to do better work. But most are not adopting or using that software. This means a lot [of inefficiency] in ‘digital transformation,’” said Batti. “We are focusing on fixing this problem.”
Digital adoption and digital experience overall can come in many forms these days.
They include assistants that are embedded directly into apps themselves (with some versions of this — such as Clippy on Word — nearly as old as software itself). The category also includes separate platforms that integrate at the back end with the apps that you use, providing not just a single ingestion point for data but intelligence on how best to use it, and what to use. (Dooly for sales teams is an example of that, although I don’t know if it would describe itself as a “digital adoption solution” per se.)
Others like Pendo are geared more at observing how your sites and apps are being adopted and used by others. And there are a number of others out there specifically looking at digital adoption by enterprises and competing directly with Whatfix: they include Apty, Userlane, Applearn.
One of the biggest — WalkMe — yesterday announced an IPO at an estimated $2.5 billion valuation.
Overall digital adoption and digital experience are big businesses: one analyst estimates that the market is growing currently at a rate of just under 11% annually and will be worth $15.8 billion by 2025.
Whatfix is built around the premise that it sits on top of whatever apps a company may choose to use, and will work with just about any piece of modern software, Batti said. That includes Whatfix being able to provide assistance on apps even when they have been customised for a particular workplace. It most commonly appears like a little chatbot on the user’s screen, like the one in this paragraph, which can expand with more details and information as needed, like this:
The company works with the most popular software packages — including Salesforce, MS Dynamics, Oracle’s CRM platform, ServiceNow, SuccessFactors, SharePoint, Workday — but, since it is used in the form of a browser extension or an overlay integrated by a company’s IT department, it can be used to help guide people with any application that’s available over the web. Batti said that one priority the startup has is to build deeper integrations with specific apps so that Whatfix can be used better across mobile and with local apps in future, not just via the web.
Many might think of “digital adoption” as training someone to use a particular software package, and while Whatfix is used for that, the company has also found a lot of traction as a tool beyond it, providing support on a more regular basis and across a wider variety of use cases, whether it’s to help guide people through app usage, or to monitor what they are doing in order to help suggest what to do next, and even populate relevant fields if “next” means using a different app.
The platform can be used to create usage guides, multilingual support, multi-device support, user tracking and more, and it comes with low-code options (it can be intergrated into an app with a single line of code, the company says).
The company claims its assistants can increase employee productivity by 35%, reduce training time and costs by 60%, reduce employee case tickets by 50% and increase application data accuracy by 20%.
While the field for digital adoption is very crowded today, it’s numbers like these, Whatfix’s own growth, and the fact that software is continuing to get more capable, but also more complex, that have interested investors.
“Digital Adoption Solutions are enhancing the growth and importance of SaaS products for enterprises globally,” said Munish Varma, Managing Partner, SoftBank Investment Advisers, in a statement. “Whatfix makes it easier for companies to use SaaS products, which increases productivity. Whatfix, with its roster of global clients, is well placed to become a DAS leader, and we are excited to be part of their journey.” Sumer Juneja, Partner, SoftBank Investment Advisers, added: “Enterprises spend billions on applications across multiple functions and yet employee adoption is low. Quick adoption ensures payback on software investments. Whatfix’s solutions will be a key driver for enterprises to achieve this goal, which is reflected in their growth.”
What will be interesting to watch is how platforms like Whatfix’s will evolve over time, and what further functions they might take on. For example, in enterprises, one of the biggest vulnerabilities in security has been how people mistakenly click on dodgy links in emails or otherwise inadvertently pass on information to malicious hackers. Could there be a role for digital adoption assistants to identify when this might happen and alert people before they click the wrong way? Regardless, the question and very existence of loopholes like that are signals for why we’ll probably why we’ll continue to see tools like Whatfix’s around for some time to come.
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Apple’s Live Text lets you interact with text in your photos
Apple has introduced a new feature to its camera system that automatically recognizes and transcribes text in your photos, from a phone number on a business card to a whiteboard full of notes. Live Text, as the feature is called, doesn’t need any prompting or special work from the user — just tap the icon and you’re good to go.
Announced by Craig Federighi on the virtual stage of WWDC, Live Text will be arriving on iPhones with iOS 15. He demonstrated it with a couple pictures, one of a whiteboard after a meeting, and a couple snapshots that included restaurant signs in the background.
Tapping the Live Text button in the lower right gave detected text a slight underline, and then a swipe allowed it to be selected and copied. In the case of the whiteboard, it collected several sentences of notes including bullet points, and with one of the restaurant signs it grabbed the phone number, which could be called or saved.
Certain types of text strings can be recognized, as well: a tracking code will be seen as such and a link to the tracking URL will be made immediately available. Translation can be done quickly too, to or from any language supported by Apple’s other translation tools.
The feature is reminiscent of many found in Google’s long-developed Lens app, and the Pixel 4 added more robust scanning capability in 2019. The difference is that the text is captured more or less passively in every photo taken by an iPhone running the new system — you don’t have to enter scanner mode or launch a separate app.
This is a nice thing for anyone to have, but it could be especially helpful for people with visual impairments. A snapshot or two makes any text, otherwise difficult to read, able to be dictated or saved.
The process takes place entirely on the phone, so don’t worry that this info is being sent to a datacenter somewhere. That also means it’s fairly quick, though until we test it for ourselves we can’t say whether it’s instantaneous or, like some other machine learning features, something that happens over the next few seconds or minutes after you take a shot. Your back catalog of photos will be Live Text-ified in your phone’s idle moments, though.
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Mendel raises $18M to tease out data structure from medicine’s disparate document trove
The medical industry is sitting on a huge trove of data, but in many cases it can be a challenge to realize the value of it because that data is unstructured and in disparate places.
Today, a startup called Mendel, which has built an AI platform both to ingest and bring order to that body of information, is announcing $18 million in funding to continue its growth and to build out what it describes as a “clinical data marketplace” for people not just to organize, but also to share and exchange that data for research purposes. It’s also going to be using the funding to hire more talent — technical and support — for its two offices, in San Jose, California and Cairo, Egypt.
The Series A round is being led by DCM, with OliveTree, Zola Global, and MTVLP, and previous backers Launch Capital, SOSV, Bootstrap Labs and chairman of UCSF Health Hub Mark Goldstein also participating.
The funding comes on the heels of what Mendel says is a surge of interest among research and pharmaceutical companies in sourcing better data to gain a better understanding of longer-term patient care and progress, in particular across wider groups of users, not just at a time when it has been more challenging to observe people and run trials, but in light of the understanding that using AI to leverage much bigger data sets can produce better insights.
This can be important, for example, in proactively identifying symptoms of particular ailments or the pathology of a disease, but also recurring and more typical responses to specific treatment courses.
We previously wrote about Mendel back in 2017 when the company had received a seed round of $2 million to better match cancer patients with the various clinical trials that are regularly being run: the idea was that certain trials address specific types of cancers and types of patients, and those who are willing to try newer approaches will be better or worse suited to each of these.
It turned out, however, that Mendel discovered a problem in the data that it would have needed to enable its matching algorithms to work, said Dr. Karim Galil, Mendel’s CEO and founder.
“As we were trying to build the trial business, we discovered a more basic problem that hadn’t been solved,” he said in an interview. “It was the reading and understanding medical records of a patient. If you can’t do that you can’t do trial matching.”
So the startup decided to become an R&D shop for at least three years to solve that problem before doing anything with trials, he continued.
Although there are today many AI companies that are parsing unstructured information in order to extract better insights, Mendel is what you might think of as part of the guard of tech companies that are building out specific AI knowledge bases for distinct verticals or areas of expertise. (Another example from another vertical is Eigen, working in the legal and finance industries, while Google’s DeepMind is another major AI player looking at ways of better harnessing data in the sphere of medicine.)
The issue of “reading” natural language is more nuanced than you might think in the world of medicine. Galil compared it to the phrase “I’m going to leave you” in English, which could just as easily mean someone is departing, say, a room, as someone is walking out of a relationship. The “true” answer — and as we humans know even truth can be elusive — can only start to be found in the context.
The same goes for doctors and their observation notes, Galil said. “There is a lot hidden between the lines, and problems can be specific to a person,” or to a situation.
That has proven to be a lucrative area to tackle.
Mendel uses a mix of computer vision and natural language processing built by teams with extensive experience in both clinical environments and in building AI algorithms and currently provides tools to automate clinical data abstraction, OCR, special tools to redact and remove personal identifiable information automatically to share records, search engines to search clinical data and — yes — an engine to enable better matching of people to clinical trials. Customers include pharmaceutical and life science companies, real-world data and real-world evidence (RWD and RWE) providers and research groups.
And to underscore just how much there is still left to do in the world of medicine, along with this funding round, Mendel is announcing a partnership with eFax, an online faxing solution used by a huge number of healthcare providers.
Faxing is totally antiquated in some parts of the world now — I’m not even sure that people the age of my children (tweens) even know what a “fax” is — but they remain one of the most-used ways to transfer documents and information between people in the worlds of healthcare and medicine, with 90% of the industry using them today. The partnership with Mendel will mean that those eFaxes will now be “read” and digitized and ingested into wider platforms to tap that data in a more useful way.
“There is huge potential for the global healthcare industry to leverage AI,” said Mendel board member and partner at DCM, Kyle Lui, in a statement. “Mendel has created a unique and seamless solution for healthcare organizations to automatically make sense of their clinical data using AI. We look forward to continuing to work with the team on this next stage of growth.”
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Rebranded Toyota Ventures invests $300 million in emerging tech and carbon neutrality
Toyota AI Ventures, Toyota’s standalone venture capital fund, has dropped the “AI” and is reborn as, simply, Toyota Ventures. The fund is commemorating its new identity by investing an additional $300 million in emerging technologies and carbon neutrality via two early-stage funds: the Toyota Ventures Frontier Fund and the Toyota Ventures Climate Fund.
The introduction of these two new funds, each worth $150 million, brings Toyota Ventures’ total assets under management to over $500 million. With the new capital infusion into the Frontier Fund comes an expansion of Toyota Ventures’ core thesis, which previously focused on AI, autonomy, mobility, robotics and the cloud, and now is adding smart cities, digital health, fintech and energy. So while Toyota Ventures’ investment approach isn’t changing, it’s broadening the scope of startups it will consider investing in.
“AI is kind of shrinking as a proportion of everything,” Jim Adler, founding managing director of Toyota Ventures, told TechCrunch. “The first mission of the Frontier Fund has always been to discover what’s next for Toyota. Toyota pivoted to cars in the 1930s, and Toyota will grow to other businesses in the future. Startups are experiments in the marketplace, and this is a way for us to understand and get comfortable with where innovations are coming from.”
Toyota as a global company has more than 370,000 employees that cover a range of business units in which the company at large stands to benefit from investing, such as financial technology. The Frontier Fund is a step outside of mobility. It not only seeks to bring emerging tech to market, but it also wants to bring new innovations onboard, whether as a customer or an acquisition, according to Adler.
“I think the vision of the company really is that machines are here to stay, they amplify the human experience, and Toyota understands how machines amplify humans really well for the benefit of society, which sounds incredibly corny, but the company really believes that,” said Adler.
By that same token, the new Climate Fund seeks to invest in startups that can help Toyota accelerate its goal of reaching carbon neutrality by 2050. The company has been investing in hydrogen for years, including a recent partnership with Japanese fuel company ENEOS, but it’s open to whatever technology will help achieve carbon neutrality, according to Adler.
“We think renewable energies will play a role,” said Adler. “Hydrogen production, storage distribution and utilization will play a role. We think carbon capture and storage will play a role. We’re not going to get dogmatic about hydrogen because we’ve been at it for decades and maybe things will change. Hydrogen hasn’t been crowdsourced across the startup community because there just wasn’t a market for it, but I think the market may be emerging.”
The fund is accepting online pitches on its website from entrepreneurs seeking early-stage funding. On Thursday, Toyota Ventures also announced it would be expanding its team and working with a new Advisor Network as a resource for founders looking for guidance on anything from product development to diversity and recruitment.
“Toyota Ventures has been an invaluable partner for Boxbot since they invested in our seed round in 2018,” said Austin Oehlerking, co-founder and CEO of Boxbot, in a statement. “They have been instrumental in helping us to navigate complicated, existential challenges on our journey from concept to product/market fit. Jim and the team really understand how corporate venture capital should function in order to successfully partner with startups.”
Adler says he and his team come from an entrepreneurial background, so they understand what it’s like on the other side of the table. Toyota Ventures’ focuses on early-stage startups because that’s where it believes some of the most interesting innovations come from.
“I’m a big believer that early-stage venture capital is a telescope into the future,” said Adler. “I think we can actually find those incredibly valuable innovations that make this all worthwhile.”
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