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Announcing the agenda for TechCrunch Sessions: SaaS
On October 27, we’re taking on the ferociously competitive field of software as a service (SaaS), and we’re thrilled to announce our packed agenda, overflowing with some of the biggest names and most exciting startups in the industry. And you’re in luck, because $75 early-bird tickets are still on sale — make sure you book yours so you can enjoy all the agenda has to offer and save $100 bucks before prices go up!
Throughout the day, you can expect to hear from industry experts, and take part in discussions about the potential of new advances in data, open source, how to deal with the onslaught of security threats, investing in early-stage startups and plenty more.
We’ll be joined by some of the biggest names and the smartest and most prescient people in the industry, including Javier Soltero at Google, Kathy Baxter at Salesforce, Jared Spataro at Microsoft, Jay Kreps at Confluent, Sarah Guo at Greylock and Daniel Dines at UiPath.
You’ll be able to find and engage with people from all around the world through world-class networking on our virtual platform — all for $75 and under for a limited time, with even deeper discounts for nonprofits and government agencies, students and up-and-coming founders!
Our agenda showcases some of the powerhouses in the space, but also plenty of smaller teams that are building and debunking fundamental technologies in the industry. We still have a few tricks up our sleeves and will be adding some new names to the agenda over the next month, so keep your eyes open.
In the meantime, check out these agenda highlights:
Survival of the Fittest: Investing in Today’s SaaS Market
with Casey Aylward (Costanoa Ventures), Kobie Fuller (Upfront) and Sarah Guo (Greylock)
- The venture capital world is faster and more competitive than ever. For investors hoping to get into the hottest SaaS deal, things are even crazier. With more nontraditional money pouring into the sector, remote dealmaking now the norm and an increasingly global market for software startups, venture capitalists are being forced to shake up their own operations — and expectations. TechCrunch sits down with three leading investors to discuss how they are fighting for allocation in hot deals, what they’ve changed in their own processes, and what today’s best founders are demanding.
Data, Data Everywhere
with Ali Ghodsi (Databricks)
- As companies struggle to manage and share increasingly large amounts of data, it’s no wonder that Databricks, whose primary product is a data lake, was valued at a whopping $28 billion for its most recent funding round. We’re going to talk to CEO Ali Ghodsi about why his startup is so hot, and what comes next.
SaaS Security, Today and Tomorrow
with Edna Conway (Microsoft), Olivia Rose (Amplitude)
- Enterprises face a constant stream of threats, from nation states to cybercriminals and corporate insiders. After a year where billions worked from home and the cloud reigned supreme, startups and corporations alike can’t afford to stay off the security pulse. Find out what SaaS startups need to know about security now, and in the future.
Automation’s Moment Is Now
with Daniel Dines (UiPath), Laela Sturdy (CapitalG) and Dave Wright (ServiceNow)
- One thing we learned during the pandemic is the importance of automation, and that’s only likely to be more pronounced as we move forward. We’ll be talking to UiPath CEO Daniel Dines, Laela Sturdy, an investor at CapitalG and Dave Wright from ServiceNow about why this is automation’s moment.
Was the Pandemic Cloud Productivity’s Spark
with Javier Soltero (Google)
- One big aspect of SaaS is productivity apps like Gmail, Google Calendar and Google Drive. We’ll talk with executive Javier Soltero about the role Google Workspace plays in the Google cloud strategy.
The Future Is Wide Open
with Abby Kearns (Puppet), Aghi Marietti (Kong) and Jason Warner (Redpoint)
- Many startups today have an open-source component, and it’s no wonder. It builds an audience and helps drive sales. We’ll talk with Abby Kearns from Puppet, Augusto “Aghi” Marietti from Kong and Jason Warner, an investor at Redpoint, about why open source is such a popular way to build a business.
How Microsoft Shifted from On-Prem to the Cloud
with Jared Spataro (Microsoft)
- Jared Spataro has been with Microsoft for over 15 years and he was a part of the shift from strictly on-prem software to that which is dominated by the cloud. Today he runs one of the most successful SaaS products out there, and we’ll talk to him about how Microsoft made that shift and what it’s meant to the company.
How Startups are Turning Data into Software Gold
with Jenn Knight (Agentsync), Barr Moses (Monte Carlo) and Dan Wright (DataRobot)
- The era of big data is behind us. Today’s leading SaaS startups are working with data, instead of merely fighting to help customers collect information. We’ve collected three leaders from three data-focused startups that are forging new markets to get their insight on how today’s SaaS companies are leveraging data to build new companies, attack new problems and, of course, scale like mad.
What Happens After Your Startup Is Acquired
with Jyoti Bansal (Harness), Nick Mehta (GainSight) and Jewel Burkes Solomon (Partpic)
- We’ll speak to three founders about the emotional upheaval of being acquired and what happens after the check clears and the sale closes. Our panel includes Jyoti Bansal who founded AppDynamics, Jewel Burkes Solomon, who founded Partpic and Nick Mehta from GainSight.
Fireside Chat
with Jay Kreps (Confluent)
- Confluent, the streaming platform built on top of Apache Kafka, was born out of a project at LinkedIn, and rode that from startup to IPO. We’ll speak to co-founder and CEO Jay Kreps to learn about what that journey was like.
We’ll have more sessions and names shortly, so stay tuned. But get excited in the meantime, we certainly are.
Pro tip: Keep your finger on the pulse of TC Sessions: SaaS. Get updates when we announce new speakers, add events and offer ticket discounts.
Why should you carve a day out of your hectic schedule to attend TC Sessions: SaaS? This may be the first year we’ve focused on SaaS, but this ain’t our first rodeo. Here’s what other attendees have to say about their TC Sessions experience.
“TC Sessions: Mobility offers several big benefits. First, networking opportunities that result in concrete partnerships. Second, the chance to learn the latest trends and how mobility will evolve. Third, the opportunity for unknown startups to connect with other mobility companies and build brand awareness.” — Karin Maake, senior director of communications at FlashParking.
“People want to be around what’s interesting and learn what trends and issues they need to pay attention to. Even large companies like GM and Ford were there, because they’re starting to see the trend move toward mobility. They want to learn from the experts, and TC Sessions: Mobility has all the experts.” — Melika Jahangiri, vice president at Wunder Mobility.
TC Sessions: SaaS 2021 takes place on October 27. Grab your team, join your community and create opportunity. Don’t wait — jump on the early bird ticket sale right now.
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Shelf.io closes huge $52.5M Series B after posting 4x ARR growth in the last year
Covering public companies can be a bit of a drag. They grow some modest amount each year, and their constituent analysts pester them with questions about gross margin expansion and sales rep efficiency. It can be a little dull. Then there are startups, which grow much more quickly — and are more fun to talk about.
That’s the case with Shelf.io. The company announced an impressive set of metrics this morning, including that from July 2020 to July 2021, it grew its annual recurring revenue (ARR) 4x. Shelf also disclosed that it secured a $52.5 million Series B led by Tiger Global and Insight Partners.
That’s quick growth for a post-Series A startup. Crunchbase reckons that the company raised $8.2 million before its Series B, while PitchBook pegs the number at $6.5 million. Regardless, the company was efficiently expanding from a limited capital base before its latest fundraising event.
What does the company’s software do? Shelf plugs into a company’s information systems, learns from the data and then helps employees respond to queries without forcing them to execute searches or otherwise hunt for information.
The company is starting with customer service as its target vertical. According to Shelf CEO Sedarius Perrotta, Shelf can absorb information from, say, Salesforce, SharePoint, legacy knowledge management platforms and Zendesk. Then, after training models and staff, the company’s software can begin to provide support staff with answers to customer questions as they talk to customers in real time.
The company’s tech can also power responses to customer queries not aimed at a human agent and provide a searchable database of company knowledge to help workers more quickly solve customer issues.
Per Perrotta, Shelf is targeting the sales market next, with others to follow. How might Shelf fit into sales? According to the company, its software may be able to offer staff already written proposals for similar-seeming deals and other related content. The gist is that at companies that have lots of workers doing similar tasks — clicking around in Salesforce, or answering support queries, say — Shelf can learn from the activity and get smarter in helping employees with their tasks. I presume that the software’s learning ability will improve over time, as well.
Shelf, around 100 people today, hopes to double in size by the end of the year, and then double again next year.
That’s where the new capital comes in. Hiring folks in the worlds of machine learning and data science is very expensive. And because the company wants to scale those hires quickly, it will need a large bank balance to lean on.
Quick ARR growth was not the only reason Shelf was able to secure such an outsized Series B, at least when compared to how much capital it had raised before. Per Perrotta, Shelf has 130% net dollar retention and no churn to report, meaning its customers are both sticky and expand organically.
While Shelf is interesting today and has certainly found niches it can sell into in its current form, I am more curious about how far the company can take its machine learning system, called MerlinAI. If its tech can get sufficiently smart, its ability to prompt and help employees could reduce onboarding time and the overall cost of employee training. That would be a huge market.
This is the sort of deal that we expect to see Tiger in — an outsized investment (compared to prior rounds) into a high-growth company that has lots of market room. Whatever price Tiger just paid for the company’s stock, a few years of continued growth should de-risk the investment. By our read, Tiger is really just the market-leading bull on software market growth in the long term. Shelf fits into that thesis neatly.
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Virtual dressing room startup Revery.ai applying computer vision to the fashion industry
Figuring out size and cut of clothes through a website can suck the fun out of shopping online, but Revery.ai is developing a tool that leverages computer vision and artificial intelligence to create a better online dressing room experience.
Under the tutelage of University of Illinois Center for Computer Science advisrr David Forsyth, a team consisting of Ph.D. students Kedan Li, Jeffrey Zhang and Min Jin Chong, is creating what they consider to be the first tool using existing catalog images to process at a scale of over a million garments weekly, something previous versions of virtual dressing rooms had difficulty doing, Li told TechCrunch.
Revery.ai co-founders Jeffrey Zhang, Min Jin Chong and Kedan Li. Image Credits: Revery.ai
California-based Revery is part of Y Combinator’s summer 2021 cohort gearing up to complete the program later this month. YC has backed the company with $125,000. Li said the company already has a two-year runway, but wants to raise a $1.5 million seed round to help it grow faster and appear more mature to large retailers.
Before Revery, Li was working on another startup in the personalized email space, but was challenged in making it work due to free versions of already large legacy players. While looking around for areas where there would be less monopoly and more ability to monetize technology, he became interested in fashion. He worked with a different adviser to get a wardrobe collection going, but that idea fizzled out.
The team found its stride working with Forsyth and making several iterations on the technology in order to target business-to-business customers, who already had the images on their websites and the users, but wanted the computer vision aspect.
Unlike its competitors that use 3D modeling or take an image and manually clean it up to superimpose on a model, Revery is using deep learning and computer vision so that the clothing drapes better and users can also customize their clothing model to look more like them using skin tone, hair styles and poses. It is also fully automated, can work with millions of SKUs and be up and running with a customer in a matter of weeks.
Its virtual dressing room product is now live on many fashion e-commerce platforms, including Zalora-Global Fashion Group, one of the largest fashion companies in Southeast Asia, Li said.
Revery.ai landing page. Image Credits: Revery.ai
“It’s amazing how good of results we are getting,” he added. “Customers are reporting strong conversion rates, something like three to five times, which they had never seen before. We released an A/B test for Zalora and saw a 380% increase. We are super excited to move forward and deploy our technology on all of their platforms.”
This technology comes at a time when online shopping jumped last year as a result of the pandemic. Just in the U.S., the e-commerce fashion industry made up 29.5% of fashion retail sales in 2020, and the market’s value is expected to reach $100 billion this year.
Revery is already in talks with over 40 retailers that are “putting this on their roadmap to win in the online race,” Li said.
Over the next year, the company is focusing on getting more adoption and going live with more clients. To differentiate itself from competitors continuing to come online, Li wants to invest body type capabilities, something retailers are asking for. This type of technology is challenging, he said, due to there not being much in the way of diversified body shape models available.
He expects the company will have to collect proprietary data itself so that Revery can offer the ability for users to create their own avatar so that they can see how the clothes look.
“We might actually be seeing the beginning of the tide and have the right product to serve the need,” he added.
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Y Combinator-backed Adra wants to turn all dentists into cavity-finding ‘super dentists’
Like other areas of healthcare, the dental industry is steadily embracing technology. But while much of it is in the orthodontic realm, other startups, like Adra, are bringing artificial intelligence into a dentist’s day-to-day workflow, particularly in finding cavities, of what will be a $435.08 billion global dental services market this year.
The Singapore-based company was founded in 2021, but was an idea that started last year. Co-founder Hamed Fesharaki has been a dentist for over a decade and owns two clinics in Singapore.
He said dentists learn to read X-rays in dental school, but it can take a few years to get good at it. Dentists also often have just minutes to read them as they hop between patients.
As a result, dentists end up misdiagnosing cavities up to 40% of the time, co-founder Yasaman Nematbakhsh said. Her background is in imaging, where she developed an artificial intelligence machine identifying hard-to-see cancers, something Fesharaki thought could also be applied to dental medicine.
Providing the perspective of a more experienced dentist, Adra’s intent is to make every dentist “a super dentist,” Fesharaki told TechCrunch. Its software detects cavities and other dental problems on dental X-rays faster and 25% more accurately, so that clinics can use that time to better serve patients and increase revenue.
Example of Adra’s software. Image Credits: Adra
“We are coming from the eye of an experienced dentist to help illustrate the problems by turning the X-rays into images to better understand what to look for,” he added. “Ultimately, the dentist has the final say, but we bring the experience element to help them compare and give them suggestions.”
By quickly pointing out the problem and the extent of it, dentists can decide in what way they want to treat it — for example, do a filling, a fluoride treatment or wait.
Along with third co-founder Shifeng Chen, the company is finishing up its time in Y Combinator’s summer cohort and has raised $250,000 so far. Fesharaki intends to do more formalized seed fundraising and wants to bring on more engineers to tackle user experience and add more features.
The company has a few clinics doing pilots and wants to attract more as it moves toward a U.S. Food and Drug Administration clearance. Fesharaki expects it to take six to nine months to receive the clearance, and then Adra will be able to hit the market in late 2022 or early 2023.
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Cardiomatics bags $3.2M for its ECG-reading AI
Poland-based health tech AI startup Cardiomatics has announced a $3.2 million seed raise to expand use of its electrocardiogram (ECG) reading automation technology.
The round is led by Central and Eastern European VC Kaya, with Nina Capital, Nova Capital and Innovation Nest also participating.
The seed raise also includes a $1 million non-equity grant from the Polish National Centre of Research and Development.
The 2017-founded startup sells a cloud tool to speed up diagnosis and drive efficiency for cardiologists, clinicians and other healthcare professionals to interpret ECGs — automating the detection and analysis of some 20 heart abnormalities and disorders with the software generating reports on scans in minutes, faster than a trained human specialist would be able to work.
Cardiomatics touts its tech as helping to democratize access to healthcare — saying the tool enables cardiologists to optimise their workflow so they can see and treat more patients. It also says it allows GPs and smaller practices to offer ECG analysis to patients without needing to refer them to specialist hospitals.
The AI tool has analyzed more than 3 million hours of ECG signals commercially to date, per the startup, which says its software is being used by more than 700 customers in 10+ countries, including Switzerland, Denmark, Germany and Poland.
The software is able to integrate with more than 25 ECG monitoring devices at this stage, and it touts offering a modern cloud software interface as a differentiator versus legacy medical software.
Asked how the accuracy of its AI’s ECG readings has been validated, the startup told us: “The data set that we use to develop algorithms contains more than 10 billion heartbeats from approximately 100,000 patients and is systematically growing. The majority of the data-sets we have built ourselves, the rest are publicly available databases.
“Ninety percent of the data is used as a training set, and 10% for algorithm validation and testing. According to the data-centric AI we attach great importance to the test sets to be sure that they contain the best possible representation of signals from our clients. We check the accuracy of the algorithms in experimental work during the continuous development of both algorithms and data with a frequency of once a month. Our clients check it everyday in clinical practice.”
Cardiomatics said it will use the seed funding to invest in product development, expand its business activities in existing markets and gear up to launch into new markets.
“Proceeds from the round will be used to support fast-paced expansion plans across Europe, including scaling up our market-leading AI technology and ensuring physicians have the best experience. We prepare the product to launch into new markets too. Our future plans include obtaining FDA certification and entering the US market,” it added.
The AI tool received European medical device certification in 2018 — although it’s worth noting that the European Union’s regulatory regime for medical devices and AI is continuing to evolve, with an update to the bloc’s Medical Devices Directive (now known as the EU Medical Device Regulation) coming into application earlier this year (May).
A new risk-based framework for applications of AI — aka the Artificial Intelligence Act — is also incoming and will likely expand compliance demands on AI health tech tools like Cardiomatics, introducing requirements such as demonstrating safety, reliability and a lack of bias in automated results.
Asked about the regulatory landscape it said: “When we launched in 2018 we were one of the first AI-based solutions approved as medical device in Europe. To stay in front of the pace we carefully observe the situation in Europe and the process of legislating a risk-based framework for regulating applications of AI. We also monitor draft regulations and requirements that may be introduced soon. In case of introducing new standards and requirements for artificial intelligence, we will immediately undertake their implementation in the company’s and product operations, as well as extending the documentation and algorithms validation with the necessary evidence for the reliability and safety of our product.”
However it also conceded that objectively measuring efficacy of ECG reading algorithms is a challenge.
“An objective assessment of the effectiveness of algorithms can be very challenging,” it told TechCrunch. “Most often it is performed on a narrow set of data from a specific group of patients, registered with only one device. We receive signals from various groups of patients, coming from different recorders. We are working on this method of assessing effectiveness. Our algorithms, which would allow them to reliably evaluate their performance regardless of various factors accompanying the study, including the recording device or the social group on which it would be tested.”
“When analysis is performed by a physician, ECG interpretation is a function of experience, rules and art. When a human interprets an ECG, they see a curve. It works on a visual layer. An algorithm sees a stream of numbers instead of a picture, so the task becomes a mathematical problem. But, ultimately, you cannot build effective algorithms without knowledge of the domain,” it added. “This knowledge and the experience of our medical team are a piece of art in Cardiomatics. We shouldn’t forget that algorithms are also trained on the data generated by cardiologists. There is a strong correlation between the experience of medical professionals and machine learning.”
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Communication software startup Channels takes on event management with text workflow
Three University of Michigan students are building Channels Inc., a communication software tailored for physical workers, and already racking up some big customers in the event management industry.
Siddharth Kaul, 18, Elan Rosen, 20, and Ibrahim Mohammed, 20, started the company after finding some common ground in retail and events. The company’s customer list boasts names like Marriott Hotels, and it announced a $520,000 seed round, led by Sahra Growth Capital, to give it nearly $570,000 in total funding.
Kaul grew up going to a lot of events in Kuwait and Dubai, but started noticing there was a delay in things that should happen and many processes were being done on pen and paper.
“The technology that was available was inharmonious and made it hard for physical workers to fulfill tasks,” Kaul told TechCrunch. “We saw it happening in the event management space, forcing workers to coordinate across technologies.”
Legacy communication platforms like Slack are aggregating communications, but are better for remote workers; for physical workers, they rely more on text communication, he said. However, the disadvantage with texting is that you have to keep scrolling to get to the new message, and old communication is lost amid all of the replies.
They began developing a platform for small hotels to help them transition to digital and provide communication in a non-chronological order that is easier to access, enables discussion and can be searched. Users of the SaaS platform can build live personnel maps to see where employees are and what the event floor looks like, prioritize alerts and automate tasks while monitoring progress.
Marriott became a customer after one of its employees saw the Channels platform was being tested at an event. He saw employees pulling out their phones and asked the manager why they were doing that, and was told they were testing out the product and referred him to Kaul.
“What they thought was helpful was that it was communication, and though the employees were checking their phones, it was quick and they remained attentive,” Kaul said.
Channels provides a solid platform in terms of analytics and graphical representation, which is a major selling point for customers, leading to initial traction and revenue for the company that Rosen said he expects can occur at the convention level the company is striving for.
The new funding will be used to grow in development and bring additional engineering talent to the team. In addition, it will allow Kaul and Rosen to continue with their studies, while Mohammed will be doing more full-time work. They want to increase their recurring revenue in the Middle East while building up operations in the United States.
Jamal Al-Barrak, managing partner of Sahra Growth Capital, said Channels was on his firm’s radar ever since they won the 2020 Dubai X-Series competition it sponsors. As a result of winning the competition, he was able to see the founders on multiple occasions and hear their growth.
Sahra doesn’t typically invest in companies like Channels, but the firm started a “seed sourcing effort” to make investments of between $200,000 and $800,000 into early-stage companies, Al-Barrak said. Channels is one of the first investments with that effort.
“Channels is one of our first investments in this initiative and they look very promising so far even compared to our investments before we started this initiative,” Al-Barrak said. He liked the founders’ work ethic and their focus on the event industry, which he called, “historically outdated and bereft of technological innovation.”
“Sid, Elan and Ibrahim are some of the youngest yet brightest entrepreneurs I have come across to this day and I have invested in over 25 technology startups,” he said. “Additionally, I enjoyed that they had proof of concept with a prior customer base and revenue. I was most impressed by their vision past their current industry and bounds as they want to encapsulate communication for all physical workers, whether it is events, retail or more.”
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Companies betting on data must value people as much as AI
The Pareto principle, also known as the 80-20 rule, asserts that 80% of consequences come from 20% of causes, rendering the remainder way less impactful.
Those working with data may have heard a different rendition of the 80-20 rule: A data scientist spends 80% of their time at work cleaning up messy data as opposed to doing actual analysis or generating insights. Imagine a 30-minute drive expanded to two-and-a-half hours by traffic jams, and you’ll get the picture.
As tempting as it may be to think of a future where there is a machine learning model for every business process, we do not need to tread that far right now.
While most data scientists spend more than 20% of their time at work on actual analysis, they still have to waste countless hours turning a trove of messy data into a tidy dataset ready for analysis. This process can include removing duplicate data, making sure all entries are formatted correctly and doing other preparatory work.
On average, this workflow stage takes up about 45% of the total time, a recent Anaconda survey found. An earlier poll by CrowdFlower put the estimate at 60%, and many other surveys cite figures in this range.
None of this is to say data preparation is not important. “Garbage in, garbage out” is a well-known rule in computer science circles, and it applies to data science, too. In the best-case scenario, the script will just return an error, warning that it cannot calculate the average spending per client, because the entry for customer #1527 is formatted as text, not as a numeral. In the worst case, the company will act on insights that have little to do with reality.
The real question to ask here is whether re-formatting the data for customer #1527 is really the best way to use the time of a well-paid expert. The average data scientist is paid between $95,000 and $120,000 per year, according to various estimates. Having the employee on such pay focus on mind-numbing, non-expert tasks is a waste both of their time and the company’s money. Besides, real-world data has a lifespan, and if a dataset for a time-sensitive project takes too long to collect and process, it can be outdated before any analysis is done.
What’s more, companies’ quests for data often include wasting the time of non-data-focused personnel, with employees asked to help fetch or produce data instead of working on their regular responsibilities. More than half of the data being collected by companies is often not used at all, suggesting that the time of everyone involved in the collection has been wasted to produce nothing but operational delay and the associated losses.
The data that has been collected, on the other hand, is often only used by a designated data science team that is too overworked to go through everything that is available.
All for data, and data for all
The issues outlined here all play into the fact that save for the data pioneers like Google and Facebook, companies are still wrapping their heads around how to re-imagine themselves for the data-driven era. Data is pulled into huge databases and data scientists are left with a lot of cleaning to do, while others, whose time was wasted on helping fetch the data, do not benefit from it too often.
The truth is, we are still early when it comes to data transformation. The success of tech giants that put data at the core of their business models set off a spark that is only starting to take off. And even though the results are mixed for now, this is a sign that companies have yet to master thinking with data.
Data holds much value, and businesses are very much aware of it, as showcased by the appetite for AI experts in non-tech companies. Companies just have to do it right, and one of the key tasks in this respect is to start focusing on people as much as we do on AIs.
Data can enhance the operations of virtually any component within the organizational structure of any business. As tempting as it may be to think of a future where there is a machine learning model for every business process, we do not need to tread that far right now. The goal for any company looking to tap data today comes down to getting it from point A to point B. Point A is the part in the workflow where data is being collected, and point B is the person who needs this data for decision-making.
Importantly, point B does not have to be a data scientist. It could be a manager trying to figure out the optimal workflow design, an engineer looking for flaws in a manufacturing process or a UI designer doing A/B testing on a specific feature. All of these people must have the data they need at hand all the time, ready to be processed for insights.
People can thrive with data just as well as models, especially if the company invests in them and makes sure to equip them with basic analysis skills. In this approach, accessibility must be the name of the game.
Skeptics may claim that big data is nothing but an overused corporate buzzword, but advanced analytics capacities can enhance the bottom line for any company as long as it comes with a clear plan and appropriate expectations. The first step is to focus on making data accessible and easy to use and not on hauling in as much data as possible.
In other words, an all-around data culture is just as important for an enterprise as the data infrastructure.
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UiPath CEO Daniel Dines is coming to TC Sessions: SaaS to talk RPA and automation
UiPath came seemingly out of nowhere in the last several years, going public last year in a successful IPO during which it raised more than $527 million. It raised $2 billion in private money prior to that with its final private valuation coming in at an amazing $35 billion. UiPath CEO Daniel Dines will be joining us on a panel to discuss automation at TC Sessions: SaaS on October 27th.
The company has been able to capture all this investor attention doing something called robotic process automation (RPA), which provides a way to automate a series of highly mundane tasks. It has become quite popular, especially to help bring a level of automation to legacy systems that might not be able to handle more modern approaches to automation involving artificial intelligence and machine learning. In 2019 Gartner found that RPA was the fastest growing category in enterprise software.
In point of fact, UiPath didn’t actually come out of nowhere. It was founded in 2005 as a consulting company and transitioned to software over the years. The company took its first VC funding, a modest $1.5 million seed round, in 2015, according to Crunchbase data.
As RPA found its market, the startup began to take off, raising gobs of money, including a $568 million round in April 2019 and $750 million in its final private raise in February 2021.
Dines will be appearing on a panel discussing the role of automation in the enterprise. Certainly, the pandemic drove home the need for increased automation as masses of office workers moved to work from home, a trend that is likely to continue even after the pandemic slows.
As the RPA market leader, he is uniquely positioned to discuss how this software and other similar types will evolve in the coming years and how it could combine with related trends like no-code and process mapping. Dines will be joined on the panel by investor Laela Sturdy from CapitalG and ServiceNow’s Dave Wright, where they will discuss the state of the automation market, why it’s so hot and where the next opportunities could be.
In addition to our discussion with Dines, the conference will also include Databricks’ Ali Ghodsi, Salesforce’s Kathy Baxter and Puppet’s Abby Kearns, as well as investors Casey Aylward and Sarah Guo, among others. We hope you’ll join us. It’s going to be a stimulating day.
Buy your pass now to save up to $100. We can’t wait to see you in October!
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Insight Partners leads $30M round into Metabase, developing enterprise business intelligence tools
Open-source business intelligence company Metabase announced Thursday a $30 million Series B round led by Insight Partners.
Existing investors Expa and NEA joined in on the round, which gives the San Francisco-based company a total of $42.5 million in funding since it was founded in 2015. Metabase previously raised $8 million in Series A funding back in 2019, led by NEA.
Metabase was developed within venture studio Expa and spun out as an easy way for people to interact with data sets, co-founder and CEO Sameer Al-Sakran told TechCrunch.
“When someone wants access to data, they may not know what to measure or how to use it, all they know is they have the data,” Al-Sakran said. “We provide a self-service access layer where they can ask a question, Metabase scans the data and they can use the results to build models, create a dashboard and even slice the data in ways they choose without having an analyst build out the database.”
He notes that not much has changed in the business intelligence realm since Tableau came out more than 15 years ago, and that computers can do more for the end user, particularly to understand what the user is going to do. Increasingly, open source is the way software and information wants to be consumed, especially for the person that just wants to pull the data themselves, he added.
George Mathew, managing director of Insight Partners, believes we are seeing the third generation of business intelligence tools emerging following centralized enterprise architectures like SAP, then self-service tools like Tableau and Looker and now companies like Metabase that can get users to discovery and insights quickly.
“The third generation is here and they are leading the charge to insights and value,” Mathew added. “In addition, the world has moved to the cloud, and BI tools need to move there, too. This generation of open source is a better and greater example of all three of those.”
To date, Metabase has been downloaded 98 million times and used by more than 30,000 companies across 200 countries. The company pursued another round of funding after building out a commercial offering, Metabase Enterprise, that is doing well, Al-Sakran said.
The new funding round enables the company to build out a sales team and continue with product development on both Metabase Enterprise and Metabase Cloud. Due to Metabase often being someone’s first business intelligence tool, he is also doubling down on resources to help educate customers on how to ask questions and learn from their data.
“Open source has changed from floppy disks to projects on the cloud, and we think end users have the right to see what they are running,” Al-Sakran said. “We are continuing to create new features and improve performance and overall experience in efforts to create the BI system of the future.
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Employee talent predictor retrain.ai raised another $7M, adds Splunk as strategic investor
Automation will displace 85 million jobs while simultaneously creating 97 million new jobs by 2025, according to the World Economic Forum. Although that sounds like good news, the hard reality is that millions of people will have to retrain in the jobs of the future.
A number of startups are addressing these problems of employee skills, and are looking at talent development, neuroscience-based assessments and prediction technologies for staffing. These include Pymetrics (raised $56.6 million), Eightfold (raised $396.8 million) and EmPath (raised $1 million). But this sector is by no means done yet.
Retrain.ai bills itself as a “Talent Intelligence Platform”, and it’s now closed an additional $7 million from its current investors Square Peg, Hetz Ventures, TechAviv, .406 Ventures and Schusterman Family Investments. It’s also now added Splunk Ventures as a strategic investor. The new round of funding takes its total raised to $20 million.
Retrain.ai says it uses AI and machine learning to help governments and organizations retrain and upskill talent for jobs of the future, enable diversity initiatives, and help employees and jobseekers manage their careers.
Dr. Shay David, co-founder and CEO of retrain.ai said: “We are thrilled to have Splunk Ventures join us on this exciting journey as we use the power of data to solve the widening skills gap in the global labor markets.”
The company says it helps companies tackle future workforce strategies by “analyzing millions of data sources to understand the demand and supply of skill sets.”
The new funding will be used for U.S. expansion, hiring talent and product development.
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