artificial intelligence
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Engine Biosciences expands its digital drug discovery pipeline with $43M round
Drug discovery is a large and growing field, encompassing both ambitious startups and billion-dollar Big Pharma incumbents. Engine Biosciences is one of the former, a Singaporean outfit with an expert founding crew and a different approach to the business of finding new therapeutics, and it just raised $43 million to keep growing.
Digital drug discovery in general means large-scale analysis of biological data like genes, gene expression, protein structures, binding sites, things like that. Where it has hit a wall in the past is not on the digital side, where any number of likely molecules or processes can be generated, but on the next step, when those notions need to be tested in vitro. So a new crop of biotech companies have worked to integrate these aspects.
Engine does so with a pair of tools it has dubbed NetMAPPR and CombiGEM. NetMAPPR is a huge sort of search engine for genes and gene interactions, taking special note of “errors” that could provide a foothold for a molecule or treatment. CombiGEM is like a mass genetic testing process that can look into thousands of gene combinations and edits on diseased cells simultaneously, providing quick experimental confirmation of the targets and effects proposed by the digital side. The company is focused on anti-cancer drugs but is looking into other fields as they become viable.
Image Credits: Engine Biosciences
The focus on gene interactions sets their approach apart, said co-founder and CEO Jeffrey Lu.
“Gene interactions are relevant to all diseases, and in cancers, where we focus, a proven approach for effective precision medicines,” he explained. “For example, there are four approved drugs targeting the PARP enzyme in the context of mutation in the BRCA gene that is changing cancer treatment for millions of people. The fundamental principle of this precision medicine is based on understanding the gene interaction between BRCA and PARP.”
The company raised a $10 million seed in 2018 and has been doing its thing ever since — but it needs more money if it’s going to bring some of these things to market.
“We already have chemical compounds directed toward the novel biology we have uncovered,” said Lu. “These are effectively prototype drugs, which are showing anti-cancer effects in diseased cells. We need to refine and optimize these prototypes to a suitable candidate to enter the clinic for testing in humans.”
Right now they’re working with other companies to do the next step up from automated testing, which is to say animal testing, to clear the way for human trials.
The CombiGEM experiments — hundreds of thousands of them — produce a large amount of data as well, and they’re sharing and collaborating on that front with several medical centers throughout Asia. “We have built what we believe to be the largest data compendium related to gene interactions in the context of cancer disease relevance,” said Lu, adding that this is crucial to the success of the machine learning algorithms they employ to predict biological processes.
The $43 million round was led by Polaris Partners, with participation by newcomers Invus and a long list of existing investors. The money will go toward the requisite testing and paperwork involved in bringing a new drug to market based on promising leads.
“We have small molecule compounds for our lead cancer programs with data from in vitro (in cancer cells) experiments. We are refining the chemistry and expanding studies this year,” said Lu. “Next year, we anticipate having our first drug candidate enter the late preclinical phase of development and regulatory work for an IND (investigational new drug) filing with the FDA, and starting the clinical trials in 2023.”
It’s a long road to human trials, let alone widespread use, but that’s the risk any drug discovery startup takes. The carrot dangling in front of them is not just the possibility of a product that could generate billions in income, but perhaps save the lives of countless cancer patients awaiting novel therapies.
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Flush with $42M, hot AI startup Faculty plans to hoover up more PhDs… and steer clear of politics
In the wake of the news that U.K.-based AI startup Faculty has raised $42.5 million in a growth funding round, I teased out more from CEO and co-founder Marc Warner on what his plans are for the company.
Faculty seems to have an uncanny knack of winning U.K. government contracts, after helping Boris Johnson win his Vote Leave campaign and thus become prime minister. It’s even helping sort out the mess that Brexit has subsequently made of the fishing industry, problems with the NHS and telling global corporates like Red Bull and Virgin Media what to suggest to their customers. Meanwhile, it continues to hoover up PhD graduates at a rate of knots to work on its AI platform.
But, speaking to me over a call, Warner said the company no longer has plans to enter the political sphere again: “Never again. It’s very controversial. I don’t want to make out that I think politics is unethical. Trying to make the world better, in whatever dimension you can, is a good thing … But from our perspective, it was, you know, ‘noisy,’ and our goal as an organization, despite current appearances to the contrary, is not to spend tonnes of time talking about this stuff. We do believe this is an important technology that should be out there and should be in a broader set of hands than just the tech giants, who are already very good at it.”
On the investment, he said: “Fundamentally, the money is about doubling down on the U.K. first and then international expansion. Over the last seven years or so we have learned what it takes to do important AI, impactful AI, at scale. And we just don’t think that there’s actually much of it out there. Customers are rightly sometimes a bit skeptical, as there’s been hype around this stuff for years and years. We figured out a bunch of the real-world applications that go into making this work so that it actually delivers the value. And so, ultimately, the money is really just about being able to build out all of the pieces to do that incredibly well for our customers.”
He said Faculty would be staying firmly HQ’d in the U.K. to take advantage of the U.K.’s talent pool: “The U.K. is a wonderful place to do AI. It’s got brilliant universities, a very dynamic startup scene. It’s actually more diverse than San Francisco. There’s government, there’s finance, there are corporates, there’s less competition from the tech giants. There’s a bit more of a heterogeneous ecosystem. There’s no sense in which we’re thinking, ‘Right, that’s it, we’re up and out!’. We love working here, we want to make things better. We’ve put an enormous amount of effort into trying to help organizations like the government and the NHS, but also a bunch of U.K. corporates in trying to embrace this technology, so that’s still going to be a terrifically important part of our business.”
That said, Faculty plans to expand abroad: “We’re going to start looking further afield as well, and take all of the lessons we’ve learned to the U.S., and then later Europe.”
But does he think this funding round will help it get ahead of other potential rivals in the space? “We tend not to think too much in terms of rivals,” he says. “The next 20 years are going to be about building intelligence into the software that already exists. If you look at the global market cap of the software businesses out there, that’s enormous. If you start adding intelligence to that, the scale of the market is so large that it’s much more important to us that we can take this incredibly important technology and deploy it safely in ways that actually improve people’s lives. It could be making products cheaper or helping organizations make their services more efficient.”
If that’s the case, then does Faculty have any kind of ethics panel overseeing its work? “We have an internal ethics panel. We have a set of principles and if we think a project might violate those principles, it gets referred to that ethics panel. It’s randomly selected from across faculty. So we’re quite careful about the projects that we work on and don’t. But to be honest, the vast majority of stuff that’s going on is very vanilla. They are just clearly ‘good for the world’ projects. The vast majority of our work is doing good work for corporate clients to help them make their businesses that bit more efficient.”
I pressed him to expand on this issue of ethics and the potential for bias. He says Faculty “builds safety in from the start. Oddly enough, the reason I first got interested in AI was reading Nick Bostrom’s work about superintelligence and the importance of AI safety. And so from the very, very first fellowship [Faculty AI researchers are called Fellows] all the way back in 2014, we’ve taught the fellows about AI safety. Over time, as soon as we were able, we started contributing to the research field. So, we’ve published papers in all of the biggest computer science conferences Neurips, ICM, ICLR, on the topic of AI safety. How to make algorithms fair, private, robust and explainable. So these are a set of problems that we care a great deal about. And, I think, are generally ‘underdone’ in the wider ecosystem. Ultimately, there shouldn’t be a separation between performance and safety. There is a bit of a tendency in other companies to say, ‘Well, you can either have performance, or you can have safety.’ But of course, we know that’s not true. The cars today are faster and safer than the Model T Ford. So it’s a sort of a false dichotomy. We’ve invested a bunch of effort in both those capabilities, so we obviously want to be able to create a wonderful performance for the task at hand, but also to ensure that the algorithms are fair, private, robust and explainable wherever required.”
That also means, he says, that AI might not always be the “bogeyman” the phrase implies: “In some cases, it’s probably not a huge deal if you’re deciding whether to put a red jumper or a blue jumper at the top of your website. There are probably not huge ethical implications in that. But in other circumstances, of course, it’s critically important that the algorithms are safe and are known to be safe and are trusted by both the users and anyone else who encounters them. In a medical context, obviously, they need to be trusted by the doctors and the patients need to make sure they actually work. So we’re really at the forefront of deploying that stuff.”
Last year the Guardian reported that Faculty had won seven government contracts in 18 months. To what does he attribute this success? “Well, I mean, we lost an enormous number more! We are a tiny supplier to government. We do our best to do work that is valuable to them. We’ve worked for many, many years with people at the home office,” he tells me.
“Without wanting to go into too much detail, that 18 months stretches over multiple prime ministers. I was appointed to the AI Council under Theresa May. Any sort of insinuations on this are just obviously nonsense. But, at least historically, most of our work was in the private sector and that continues to be critically important for us as an organization. Over the last year, we’ve tried to step up and do our bit wherever we could for the public sector. It’s facing such a big, difficult situation around COVID, and we’re very proud of the things we’ve managed to accomplish with the NHS and the impact that we had on the decisions that senior people were able to undertake.”
Returning to the issue of politics I asked him if he thought — in the wake of events such as Brexit and the election of Donald Trump, which were both affected by AI-driven political campaigning — AI is too dangerous to be applied to that arena? He laughed: “It’s a funny old funny question… It’s a really odd way to phrase a question. AI is just a technology. Fundamentally, AI is just maths.”
I asked him if he thought the application of AI in politics had had an outsized or undue influence on the way that political parties have operated in the last few years: “I’m afraid that is beyond my knowledge,” he says. But does Faculty have regrets about working in the political sphere?
“I think we’re just focused on our work. It’s not that we have strong feelings, either way, it’s just that from our perspective, it’s much, much more interesting to be able to do the things that we care about, which is deploying AI in the real world. It’s a bit of a boring answer! But it is truly how we feel. It’s much more about doing the things we think are important, rather than judging what everyone else is doing.”
Lastly, we touched on the data science capabilities of the U.K. and what the new fundraising will allow the company to do.
He said: “We started an education program. We have roughly 10% of the U.K.’s PhDs in physics, maths, engineering, applying to the program. Roughly 400 or so people have been through that program and we plan to expand that further so that more and more people get the opportunity to start a career in data science. And then inside Faculty specifically, we think we’ll be able to create 400 new jobs in areas like software engineering, data science, product management. These are very exciting new possibilities for people to really become part of the technology revolution. I think there’s going to be a wonderful new energy in Faculty, and hopefully a positive small part in increasing the U.K. tech ecosystem.”
Warner comes across as sincere in his thoughts about the future of AI and is clearly enthusiastic about where Faculty can take the whole field next, both philosophically and practically. Will Faculty soon be challenging that other AI leviathan, DeepMind, for access to all those PhDs? There’s no doubt it will.
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Fireflies.ai raises $14M for its meeting transcription and automation service
The Fireflies.ai project is a good reminder that not every startup project goes from idea to unicorn-status in 48 minutes. Instead, the startup’s CEO Krish Ramineni told TechCrunch about how a period of interest in natural language processing (NLP), tinkering with a friend, a stint at Microsoft, and even working on Slack bots led him to helping found Fireflies.ai (Fireflies), a company that today announced a $14 million raise led by Khosla.
Fireflies is a two-part service. Its first point of business is recording and transcribing voice conversations. Things like video meetings, for example. Next, Fireflies wants to plug your voice data into other applications, helping its customers automate data entry, task creation and more.
Before today’s round, the startup had raised around $5 million, including some micro-rounds, a stint in the Acceleprise accelerator, and a $4.9 million seed round raised in late 2019. That investment included participation from Canaan Partners and well-known angel April Underwood.
That Fireflies has raised more capital is not surprising, given how quickly it has accreted users. According to an interview with Ramineni, more than 10,000 teams use Fireflies today. In individual usage terms, some 35,000 organizations are represented amongst its user base.
As the company launched its product in early 2020, those results sound pretty good.
But TechCrunch was curious if revenue tracked with usage at Fireflies, as is sometimes the case. It does, Ramineni said, adding that his company grew its revenues 300% in the last six or seven months.
How did it manage such rapid growth while only having raised $5 million before, and with a team that is around 90% in its product and engineering teams? By pursuing everyone’s favorite: the bottoms-up sales model. In short, you can use Fireflies for free, but if you run out of meeting credits, other usage-based blockers or the need for different, paywalled functionality, you have to cough up for the product.
Folks are, it appears.
Fireflies is in fact an interesting hybrid of SaaS and usage-based pricing. The higher the paid tier that a user selects, the more minutes of transcription they are apportioned per month. But there are caps, limits that users can buy their way out of. TechCrunch asked Ramineni about it, with the CEO explaining that some customers want to ingest years of saved meetings. Our read is that despite work done by the startup to keep its infrastructure costs low, building pricing guardrails around product usage just makes sense for the startup.
The company will sport SaaS-like gross margins, Ramineni confirmed to TechCrunch.
Looking ahead, Fireflies wants to plug into more and more meeting platforms, and external software. You can currently link your Fireflies account to services like Zapier, Slack and your CRM. Over time, it’s not hard to see how the startup could take more direct commands from meetings, and help users better distribute, file and recall meeting information.
As someone with too many meetings, and too many notes documents spread out across the wasteland that is my Google Drive account, I get why people are using Fireflies today. But if the startup can build a no-code automation platform on top of my note taking? Then I will probably have to buy its service.
Speaking of which, as a final note, working for a Major American Corporation can have its downsides. For example, Ramineni provided TechCrunch with a recording of our interview inside of Fireflies. This was nice, as I prefer to write from both my notes and transcripts to ensure that I am not missing things, or making mistakes. Fireflies kept asking me to log in. I tried with my corporate Google account. Which blocks such log-ins. So I kept getting the same prompt again and again.
Annoying? Sure. Lethal? No.
More when we can squeeze more growth data out of the startup.
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Mental health app Wysa raises $5.5M for ’emotionally intelligent’ AI
It’s hard enough to talk about your feelings to a person; Jo Aggarwal, the founder and CEO of Wysa, is hoping you’ll find it easier to confide in a robot. Or, put more specifically, “emotionally intelligent” artificial intelligence.
Wysa is an AI-powered mental health app designed by Touchkin eServices, Aggarwal’s company that currently maintains headquarters in Bangalore, Boston and London. Wysa is something like a chatbot that can respond with words of affirmation, or guide a user through one of 150 different therapeutic techniques.
Wysa is Aggarwal’s second venture. The first was an elder care company that failed to find market fit, she says. Aggarwal found herself falling into a deep depression, from which, she says, the idea of Wysa was born in 2016.
In March, Wysa became one of 17 apps in the Google Assistant Investment Program, and in May, closed a Series A funding round of $5.5 million led by Boston’s W Health Ventures, the Google Assistant Investment Program, pi Ventures and Kae Capital.
Wysa has raised a total of $9 million in funding, says Aggarwal, and the company has 60 full-time employees and about three million users.
The ultimate goal, she says, is not to diagnose mental health conditions. Wysa is largely aimed at people who just want to vent. Most Wysa users are there to improve their sleep, anxiety or relationships, she says.
“Out of the 3 million people that use Wysa, we find that only about 10% actually need a medical diagnosis,” says Aggarwal. If a user’s conversations with Wysa equate with high scores on traditional depression questionnaires like the PHQ-9 or the anxiety disorder questionnaire GAD-7, Wysa will suggest talking to a human therapist.
Naturally, you don’t need to have a clinical mental health diagnosis to benefit from therapy.
Wysa isn’t intended to be a replacement, says Aggarwal (whether users view it as a replacement remains to be seen), but an additional tool that a user can interact with on a daily basis.
“Sixty percent of the people who come and talk to Wysa need to feel heard and validated, but if they’re given techniques of self help, they can actually work on it themselves and feel better,” Aggarwal continues.
Wysa’s approach has been refined through conversations with users and through input from therapists, says Aggarwal.
For instance, while having a conversation with a user, Wysa will first categorize their statements and then assign a type of therapy, like cognitive behavioral therapy or acceptance and commitment therapy, based on those responses. It would then select a line of questioning or therapeutic technique written ahead of time by a therapist and begin to converse with the user.
Wysa, says Aggarwal, has been gleaning its own insights from more than 100 million conversations that have unfolded this way.
“Take for instance a situation where you’re angry at somebody else. Originally our therapists would come up with a technique called the empty chair technique where you’re trying to look at it from the other person’s perspective. We found that when a person felt powerless or there were trust issues, like teens and parents, the techniques the therapists were giving weren’t actually working,” she says.
“There are 10,000 people facing trust issues who are actually refusing to do the empty chair exercise. So we have to find another way of helping them. These insights have built Wysa.”
Although Wysa has been refined in the field, research institutions have played a role in Wysa’s ongoing development. Pediatricians at the University of Cincinnati helped develop a module specifically targeted toward COVID-19 anxiety. There are also ongoing studies of Wysa’s ability to help people cope with mental health consequences from chronic pain, arthritis and diabetes at The Washington University in St. Louis and The University of New Brunswick.
Still, Wysa has had several tests in the real world. In 2020, the government of Singapore licensed Wysa, and provided the service for free to help cope with the emotional fallout of the coronavirus pandemic. Wysa is also offered through the health insurance company Aetna as a supplement to Aetna’s Employee Assistance Program.
The biggest concern about mental health apps, naturally, is that they might accidentally trigger an incident, or mistake signs of self harm. To address this, the U.K.’s National Health Service (NHS) offers specific compliance standards. Wysa is compliant with the NHS’ DCB0129 standard for clinical safety, the first AI-based mental health app to earn the distinction.
To meet those guidelines, Wysa appointed a clinical safety officer, and was required to create “escalation paths” for people who show signs of self harm.
Wysa, says Aggarwal, is also designed to flag responses to self-harm, abuse, suicidal thoughts or trauma. If a user’s responses fall into those categories Wysa will prompt the user to call a crisis line.
In the U.S., the Wysa app that anyone can download, says Aggarwal, fits the FDA’s definition of a general wellness app or a “low risk device.” That’s relevant because, during the pandemic, the FDA has created guidance to accelerate distribution of these apps.
Still, Wysa may not perfectly categorize each person’s response. A 2018 BBC investigation, for instance, noted that the app didn’t appear to appreciate the severity of a proposed underage sexual encounter. Wysa responded by updating the app to handle more instances of coercive sex.
Aggarwal also notes that Wysa contains a manual list of sentences, often containing slang, that they know the AI won’t catch or accurately categorize as harmful on its own. Those are manually updated to ensure that Wysa responds appropriately. “Our rule is that [the response] can be 80%, appropriate, but 0% triggering,” she says.
In the immediate future, Aggarwal says the goal is to become a full-stack service. Rather than having to refer patients who do receive a diagnosis to Employee Assistant Programs (as the Aetna partnership might) or outside therapists, Wysa aims to build out its own network of mental health suppliers.
On the tech side they’re planning expansion into Spanish, and will start investigating a voice-based system based on guidance from the Google Assistant Investment Fund.
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Snap acquires AR startup WaveOptics, which provides tech for Spectacles, for over $500M
Snap yesterday announced the latest iteration of its Spectacles augmented reality glasses, and today the company revealed a bit more news: it is also acquiring the startup that supplied the technology that helps power them. The Snapchat parent is snapping up WaveOptics, an AR startup that makes the waveguides and projectors used in AR glasses. These overlay virtual images on top of the views of the real world someone wearing the glasses can see, and Snap worked with WaveOptics to build its latest version of Spectacles.
The deal was first reported by The Verge, and a spokesperson for Snap directly confirmed the details to TechCrunch. Snap is paying over $500 million for the startup, in a cash-and-stock deal. The first half of that will be coming in the form of stock when the deal officially closes, and the remainder will be payable in cash or stock in two years.
This is a big leap for WaveOptics, which had raised around $65 million in funding from investors that included Bosch, Octopus Ventures and a host of individuals, from Stan Boland (veteran entrepreneur in the UK, most recently at FiveAI) and Ambarish Mitra (the co-founder of early AR startup Blippar). PitchBook estimates that its most recent valuation was only around $105 million.
WaveOptics was founded in Oxford, and from what we know it will continue to be based in the UK.
We have been covering the company since its earliest days, when it displayed some very interesting, early, and ahead-of-its-time technology: waveguides based on hologram physics and photonic crystals. The important and key thing is that its tech drastically compresses size and load of the hardware needed to process and display images, meaning a much wider and more flexible range of form factors for AR hardware based on WaveOptics tech.
It’s not clear whether WaveOptics will continue to work with other parties post-deal, but it seems that one obvious advantage for Snap would be making the startup’s technology exclusive to itself.
Snap has been on something of an acquisition march in recent times — it’s made at least three other purchases of startups since January, including Fit Analytics for an AR-fuelled move into e-commerce, as well as Pixel8Earth and StreetCred for its mapping tools.
This deal, however, marks Snap’s biggest acquisition to date in terms of valuation. That is not only a mark of the premium price that foundational artificial intelligence tech continues to command — in addition to the team of scientists that built WaveOptics, it also has 12 filed and in-progress patents — but also Snap’s financial and, frankly, existential commitment to having a seat at the table when it comes not just to social apps that use AR, but hardware, and being at the centre of not just using the tech, but setting the pace and agenda for how and where that will play out.
That’s been a tenacious and not always rewarding place for it to be, but the company — which has long described itself as a “camera company” — has kept hardware in the mix as an essential component for its future strategy.
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How to ensure data quality in the era of Big Data
A little over a decade has passed since The Economist warned us that we would soon be drowning in data. The modern data stack has emerged as a proposed life-jacket for this data flood — spearheaded by Silicon Valley startups such as Snowflake, Databricks and Confluent.
Today, any entrepreneur can sign up for BigQuery or Snowflake and have a data solution that can scale with their business in a matter of hours. The emergence of cheap, flexible and scalable data storage solutions was largely a response to changing needs spurred by the massive explosion of data.
Currently, the world produces 2.5 quintillion bytes of data daily (there are 18 zeros in a quintillion). The explosion of data continues in the roaring ‘20s, both in terms of generation and storage — the amount of stored data is expected to continue to double at least every four years. However, one integral part of modern data infrastructure still lacks solutions suitable for the Big Data era and its challenges: Monitoring of data quality and data validation.
Let me go through how we got here and the challenges ahead for data quality.
The value vs. volume dilemma of Big Data
In 2005, Tim O’Reilly published his groundbreaking article “What is Web 2.0?”, truly setting off the Big Data race. The same year, Roger Mougalas from O’Reilly introduced the term “Big Data” in its modern context — referring to a large set of data that is virtually impossible to manage and process using traditional BI tools.
Back in 2005, one of the biggest challenges with data was managing large volumes of it, as data infrastructure tooling was expensive and inflexible, and the cloud market was still in its infancy (AWS didn’t publicly launch until 2006). The other was speed: As Tristan Handy from Fishtown Analytics (the company behind dbt) notes, before Redshift launched in 2012, performing relatively straightforward analyses could be incredibly time-consuming even with medium-sized data sets. An entire data tooling ecosystem has since been created to mitigate these two problems.
The emergence of the modern data stack (example logos and categories). Image Credits: Validio
Scaling relational databases and data warehouse appliances used to be a real challenge. Only 10 years ago, a company that wanted to understand customer behavior had to buy and rack servers before its engineers and data scientists could work on generating insights. Data and its surrounding infrastructure was expensive, so only the biggest companies could afford large-scale data ingestion and storage.
The challenge before us is to ensure that the large volumes of Big Data are of sufficiently high quality before they’re used.
Then came a (Red)shift. In October 2012, AWS presented the first viable solution to the scale challenge with Redshift — a cloud-native, massively parallel processing (MPP) database that anyone could use for a monthly price of a pair of sneakers ($100) — about 1,000x cheaper than the previous “local-server” setup. With a price drop of this magnitude, the floodgates opened and every company, big or small, could now store and process massive amounts of data and unlock new opportunities.
As Jamin Ball from Altimeter Capital summarizes, Redshift was a big deal because it was the first cloud-native OLAP warehouse and reduced the cost of owning an OLAP database by orders of magnitude. The speed of processing analytical queries also increased dramatically. And later on (Snowflake pioneered this), they separated computing and storage, which, in overly simplified terms, meant customers could scale their storage and computing resources independently.
What did this all mean? An explosion of data collection and storage.
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Unbounce snags Snazzy.ai to add automated copywriting to platform
Unbounce, a Vancouver startup best known for helping marketers create automated landing pages, added a new wrinkle this morning when it announced it has acquired Snazzy.ai, an early-stage automated copywriting startup. The two companies did not share the terms.
Unbounce Chief Strategy Officer Tamara Grominsky says that her company focuses on helping customers convert their customers into sales, and with Snazzy, it gets some pretty nifty technology based on GPT-3 artificial intelligence technology.
“We’re focused right now on building conversion intelligence software that will allow marketers to work with machines to really unlock their true conversion potential […] and we saw a huge opportunity with Snazzy to focus particularly on the content creation and copy creation space to help us accelerate that strategy,” Grominsky explained.
She points out that the product is really aimed at the marketing generalist charged with overseeing landing pages, and who is responsible for a range of tasks including writing copy. “The average Unbounce customer isn’t a specialized copywriter, so they don’t spend [their work] day writing copy. They’re what we would consider a marketing generalist or really someone who’s responsible for a wide range of marketing responsibilities,” she said.
Snazzy co-founder Chris Frantz says the tech is really about getting people started, and then they can tweak the results as needed. “The hardest part has always been to get that first line, that first page, the first couple of words in — and we eliminate that entirely. That might not always result in amazing copy, but on the plus side you can always click the button again and give it another try,” he said.
Frantz says that with so much competition in the space, he and his co-founder felt they could build a market much faster as part of a larger and broader marketing platform solution like Unbounce.
“I love Tamara’s vision for the future of Unbounce. I think she has a very ambitious vision. She sold me on that very early on in the process. At the same time, there was a lot of competition in the space, and to have a key differentiator with a company like Unbounce, which has a decade of marketing experience and a lot of trust within this community, I think it’s a very powerful wedge that we can use to further grow our audience,” Frantz said.
The tool lets you write a range of copy, from landing pages to Google ad copy. The company launched in alpha last October and already had 30,000 customers, which Grominsky says Unbounce hopes to convert into customers. The good news for those customers is that the company plans to leave Snazzy as a standalone product, while incorporating the tech into the platform in ways that make sense in the coming year.
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Forecast nabs $19M for its AI-based approach to project management and resource planning
Project management has long been a people-led aspect of the workplace, but that has slowly been changing. Trends in automation, big data and AI have not only ushered in a new wave of project management applications, but they have led to a stronger culture of people willing to use them. Today, one of the startups building a platform for the next generation of project management is announcing some funding — a sign of the traction it’s getting in the market.
Forecast, a platform and startup of the same name that uses AI to help with project management and resource planning — put simply, it uses artificial intelligence to both “read” and integrate data from different enterprise applications in order to build a bigger picture of the project and potential outcomes — has raised $19 million to continue building out its business.
The company plans to use some of the funding to expand to the U.S., and some to continue building out its platform and business, headquartered in London with a development office also in Copenhagen.
This funding, a Series A, comes less than a year after the startup’s commercial launch, and it was led by Balderton Capital, with previous investors Crane Ventures Partners, SEED Capital and Heartcore also participating.
Forecast closed a seed round in November 2019 and then launched just as the pandemic was kicking off. It was a time when some projects were indeed put on ice, but others that went ahead did so with more caution on all sorts of fronts — financial, organizational and technical. It turned out to be a “right place, right time” moment for Forecast, a tool that plays directly into providing a technical platform to manage all of that in a better way, and it tripled revenues during the year. Its customers include the likes of the NHS, the Red Cross, Etain and more. It says over 150,000 projects have been created and run through its platform to date.
Project management — the process of planning what you need to do, assigning resources to the task and tracking how well all of that actually goes to plan — has long been stuck between a rock and a hard place in the world of work.
It can be essential to getting things done, especially when there are multiple departments or stakeholders involved; yet it’s forever an inexact science that often does not reflect all the complexities of an actual project, and therefore may not be as useful as it could or should be.
This was a predicament that founder and CEO Dennis Kayser knew all too well, having been an engineer and technical lead on a number of big projects himself. His pedigree is an interesting one: One of his early jobs was as a developer at Varien, where he built the first version of Magento. (The company was eventually rebranded as Magento and then acquired by eBay, then spun out, then acquired again, this time by Adobe for nearly $1.7 billion, and is now a huge player in the world of e-commerce tools.) He also spent years as a consultant at IBM, where among other things he helped build and formulate the first versions of ikea.com.
In those and other projects, he saw the pitfalls of project management not done right — not just in terms of having the right people on a project at the right time, but the resource planning needed, better calculations of financial outcomes in the event of a decision going one way or the other, and so on.
He didn’t say this outright, but I’m sure one of the points of contention was the fact that the first ikea.com site didn’t actually have any e-commerce in it, just a virtual window display of sorts. That was because Ikea wanted to keep people shopping in its stores, away from the efficiency of just buying the one thing you actually need and not the 10 you do not. Yes, there are plenty of ways now of recirculating people to buy more when you select one item for a shopping cart — something the likes of Amazon has totally mastered — but this was years ago when there was still even more opportunities for innovation than there are now. All of this is to say that you might very reasonably argue that had there been better project managing and resource planning tools to give forecasts of potential outcomes of one or another route taken, people advocating for a different approach could have made their case better. And maybe Ikea would have jumped on board with digital commerce far sooner than it did.
“Typically you get a lot of spreadsheets, people scattered across different tools that include accounting, CRM, Gitlab and more,” Kayser said.
That became the impetus for trying to build something that can take all of that into account and make a project management tool that — rather than just being a way of accounting to a higher-up, or reflecting only what someone can be bothered to update in the system — something that can help a team.
“Connecting everything into our engine, we leverage data to understand what they are working on and what is the right thing to be working on, what the finances are looking like,” he continued. “So if you work in product, you can plan out who is where, and what resourcing you need, what kind of people and skills you require.” This is a more dynamic progression of some of the other newer tools that are being used for project management today, targeting, in his words, “people who graduate from Monday and Asana who need something more robust, either because they have too many people working on a project or because it’s too complicated, there is just too much stuff to handle.”
More legacy tools he said that are used include Oracle “to some degree” and Mavenlink, which he describes as possibly Forecast’s closest competitor, “but its platform is aging.”
Currently the Forecast platform has some 26 integrations of popular tools used for projects to produce its insights and intelligence, including Salesforce, Gitlab, Google Calendar, and, as it happens, Asana. But given how fragmented the market is, and the signals one might gain from any number of other resources and apps, I suspect that this list will grow as and when its customers need more supported, or Forecast works out what can be gleaned from different places to paint an even more accurate picture.
The result may not ever replace an actual human project manager, but certainly starts to then look like a “digital twin” (a phrase I have been hearing more and more these days) that will definitely help that person, and the rest of the team, work in a smarter way.
“We are really excited to be an early investor in Forecast,” said James Wise, a partner at Balderton Capital, in a statement. “We share their belief that the next generation of SaaS products will be more than just collaboration tools, but use machine learning to actively solve problems for their users. The feedback we got from Forecast’s customers was quite incredible, both in their praise for the platform and in how much of a difference it had already made to their operations. We look forward to supporting the company to scale this impact going forward.”
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Everything Google announced at I/O today
This year’s I/O event from Google was heavy on the “we’re building something cool” and light on the “here’s something you can use or buy tomorrow.” But there were also some interesting surprises from the semi-live event held in and around the company’s Mountain View campus. Read on for all the interesting bits.
Android 12 gets a fresh new look and some quality of life features
We’ve known Android 12 was on its way for months, but today was our first real look at the next big change for the world’s most popular operating system. A new look, called Material You (yes), focuses on users, apps, and things like time of day or weather to change the UI’s colors and other aspects dynamically. Some security features like new camera and microphone use indicators are coming, as well as some “private compute core” features that use AI processes on your phone to customize replies and notifications. There’s a beta out today for the adventurous!
Wow, Android powers 3 billion devices now
Subhed says it all (but read more here). Up from 2 billion in 2017.
Smart Canvas smushes Docs, productivity, and video calls together
Millions of people and businesses use Google’s suite of productivity and collaboration tools, but the company felt it would be better if they weren’t so isolated. Now with Smart Canvas you can have a video call as you work on a shared doc together and bring in information and content from your Drive and elsewhere. Looks complicated, but potentially convenient.
AI conversations get more conversational with LaMDA
It’s a little too easy to stump AIs if you go off script, asking something in a way that to you seems normal but to the language model is totally incomprehensible. Google’s LaMDA is a new natural language processing technique that makes conversations with AI models more resilient to unusual or unexpected queries, making it more like a real person and less like a voice interface for a search function. They demonstrated it by showing conversations with anthropomorphized versions of Pluto and a paper airplane. And yes, it was exactly as weird as it sounds.
Google built a futuristic 3D video calling booth
One of the most surprising things at the keynote had to be Project Starline, a high-tech 3D video call setup that uses Google’s previous research and Lytro DNA to show realistic 3D avatars of people on both sides of the system. It’s still experimental but looks very promising.
Wear OS gets a revamp and lots of health-focused apps
Image Credits: Google
Few people want to watch a movie on their smartwatch, but lots of people like to use it to track their steps, meditation, and other health-related practices. Wear OS is getting a bunch of Fitbit DNA infused, with integrated health tracking stuff and a lot of third party apps like Calm and Flo.
Samsung and Google announce a unified smartwatch platform
These two mobile giants have been fast friends in the phone world for years, but when it comes to wearables, they’ve remained rivals. In the face of Apple’s utter dominance in the smartwatch space, however, the two have put aside their differences and announced they’ll work on a “unified platform” so developers can make apps that work on both Tizen and Wear OS.
And they’re working together on foldables too
Apparently Google and Samsung realized that no one is going to buy foldable devices unless they do some really cool things, and that collaboration is the best way forward there. So the two companies will also be working together to improve how folding screens interact with Android.
Android TV hits 80 million devices and adds phone remote
Image Credits: Google
The smart TV space is a competitive one, and after a few starts Google has really made it happen with Android TV, which the company announced had reached 80 million monthly active devices — putting it, Roku, and Amazon (the latter two with around 50 million monthly active accounts) all in the same league. The company also showed off a powerful new phone-based remote app that will (among other things) make putting in passwords way better than using the d-pad on the clicker. Developers will be glad to hear there’s a new Google TV emulator and Firebase Test Lab will have Android TV support.
Your Android phone is now (also) your car key
Well, assuming you have a really new Android device with a UWB chip in it. Google is working with BMW first, and other automakers soon most likely, to make a new method for unlocking the car when you get near it, or exchanging basic commands without the use of a fob or Bluetooth. Why not Bluetooth you ask? Well, Bluetooth is old. UWB is new.
Vertex collects machine learning development tools in one place
Google and its sibling companies are both leaders in AI research and popular platforms for others to do their own AI work. But its machine learning development tools have been a bit scattershot — useful but disconnected. Vertex is a new development platform for enterprise AI that puts many of these tools in one place and integrates closely with optional services and standards.
There’s a new generation of Google’s custom AI chips
Google does a lot of machine learning stuff. Like, a LOT a lot. So they are constantly working to make better, more efficient computing hardware to handle the massive processing load these AI systems create. TPUv4 is the latest, twice as fast as the old ones, and will soon be packaged into 4,096-strong pods. Why 4,096 and not an even 4,000? The same reason any other number exists in computing: powers of 2.
And they’re powering some new Photos features including one that’s horrifying
NO THANK YOU
Google Photos is a great service, and the company is trying to leverage the huge library of shots most users have to find patterns like “selfies with the family on the couch” and “traveling with my lucky hat” as fun ways to dive back into the archives. Great! But they’re also taking two photos taken a second apart and having an AI hallucinate what comes between them, leading to a truly weird looking form of motion that shoots deep, deep into the uncanny valley, from which hopefully it shall never emerge.
Forget your password? Googlebot to the rescue
Google’s “AI makes a hair appointment for you” service Duplex didn’t exactly set the world on fire, but the company has found a new way to apply it. If you forget your password, Duplex will automatically fill in your old password, pick a new one and let you copy it before submitting it to the site, all by interacting with the website’s normal reset interface. It’s only going to work on Twitter and a handful of other sites via Chrome for now, but hey, if it happens to you a lot, maybe it’ll save you some trouble.
Enter the Shopping Graph
Image Credits: Google I/O 2021
The aged among our readers may remember Froogle, Google’s ill-fated shopping interface. Well, it’s back… kind of. The plan is to include lots of product information, from price to star rating, availability and other info, right in the Google interface when you search for something. It sucks up this information from retail sites, including whether you have something in your cart there. How all this benefits anyone more than Google is hard to imagine, but naturally they’re positioning it as wins all around. Especially for new partner Shopify. (Me, I use DuckDuckGo.)
Flutter cross-platform devkit gets an update
A lot of developers have embraced Google’s Flutter cross-platform UI toolkit. The latest version, announced today, adds some safety settings, performance improvements, and workflow updates. There’s lots more coming, too.
Firebase gets an update too
Popular developer platform Firebase got a bunch of new and updated features as well. Remote Config gets a nice update allowing developers to customize the app experience to individual user types, and App Check provides a basic level of security against external threats. There’s plenty here for devs to chew on.
The next version of Android Studio is Arctic Fox
Image Credits: Google
The beta for the next version of Google’s Android Studio environment is coming soon, and it’s called Arctic Fox. It’s got a brand new UI building toolkit called Jetpack Compose, and a bunch of accessibility testing built in to help developers make their apps more accessible to people with disabilities. Connecting to devices to test on them should be way easier now too. Oh, and there’s going to be a version of Android Studio for Apple Silicon.
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Dabbel gets $4.4M to cut CO2 by automating HVAC for commercial buildings
Düsseldorf-based proptech startup Dabbel is using AI to drive energy efficiency savings in commercial buildings.
It’s developed cloud-based self-learning building management software that plugs into the existing building management systems (BMS) — taking over control of heating and cooling systems in a way that’s more dynamic than legacy systems based on fixed set-point resets.
Dabbel says its AI considers factors such as building orientation and thermal insulation, and reviews calibration decisions every five minutes — meaning it can respond dynamically to changes in outdoor and indoor conditions.
The 2018-founded startup claims this approach of layering AI-powered predictive modelling atop legacy BMS to power next-gen building automation is able to generate substantial energy savings — touting reductions in energy consumption of up to 40%.
“Every five minutes Dabbel reviews its decisions based on all available data,” explains CEO and co-founder, Abel Samaniego. “With each iteration, Dabbel improves or adapts and changes its decisions based on the current circumstances inside and outside the building. It does this by using cognitive artificial intelligence to drive a Model-Based Predictive Control (MPC) System… which can dynamically adjust all HVAC setpoints based on current/future conditions.”
In essence, the self-learning system predicts ahead of time the tweaks that are needed to adapt for future conditions — saving energy vs a pre-set BMS that would keep firing the boilers for longer.
The added carrot for commercial building owners (or tenants) is that Dabbel squeezes these energy savings without the need to rip and replace legacy systems — nor, indeed, to install lots of IoT devices or sensor hardware to create a ‘smart’ interior environment; the AI integrates with (and automatically calibrates) the existing heating, ventilation, and air conditioning (HVAC) systems.
All that’s needed is Dabbel’s SaaS — and less than a week for the system to be implemented (it also says installation can be done remotely).
“There are no limitations in terms of Heating and Cooling systems,” confirms Samaniego, who has a background in industrial engineering and several years’ experience automating high tech plants in Germany. “We need a building with a Building Management System in place and ideally a BACnet communication protocol.”
Average reductions achieved so far across the circa 250,000m² of space where its AI is in charge of building management systems are a little more modest but a still impressive 27%. (He says the maximum savings seen at some “peak times” is 42%.)
The touted savings aren’t limited to a single location or type of building/client, according to Dabbel, which says they’ve been “validated across different use cases and geographies spanning Europe, the U.S., China, and Australia”.
Early clients are facility managers of large commercial buildings — Commerzbank clearly sees potential, having incubated the startup via its early-stage investment arm — and several schools.
A further 1,000,000m² is in the contract or offer phase — slated to be installed “in the next six months”.
Dabbel envisages its tech being useful to other types of education institutions and even other use-cases. (It’s also toying with adding a predictive maintenance functionality to expand its software’s utility by offering the ability to alert building owners to potential malfunctions ahead of time.)
And as policymakers around the global turn their attention to how to achieve the very major reductions in carbon emissions that are needed to meet ambitious climate goals the energy efficiency of buildings certainly can’t be overlooked.
“The time for passive responses to addressing the critical issue of carbon emission reduction is over,” said Samaniego in a statement. “That is why we decided to take matters into our own hands and develop a solution that actively replaces a flawed human-based decision-making process with an autonomous one that acts with surgical precision and thanks to artificial intelligence, will only improve with each iteration.”
If the idea of hooking your building’s heating/cooling up to a cloud-based AI sounds a tad risky for Internet security reasons, Dabbel points out it’s connecting to the BMS network — not the (separate) IT network of the company/building.
It also notes that it uses one-way communication via a VPN tunnel — “creating an end-to-end encrypted connection under high market standards”, as Samaniego puts it.
The startup has just closed a €3.6 million (~$4.4M) pre-Series A funding round led by Target Global, alongside main incubator (Commerzbank’s early-stage investment arm), SeedX, plus some strategic angel investors.
Commenting in a statement, Dr. Ricardo Schaefer, partner at Target Global, added: “We are enthusiastic to work with the team at Dabbel as they offer their clients a tangible and frictionless way to significantly reduce their carbon footprint, helping to close the gap between passive measurement and active remediation.”
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