Extra Crunch roundup: Inside Sprinklr’s IPO filing, how digital transformation is reshaping markets

Despite a recent history of uneven cash flow and moderate growth, SaaS customer experience management platform Sprinklr has filed to go public.

In today’s edition of The Exchange, Alex Wilhelm pores over the New York-based unicorn’s S-1 to better understand exactly what Sprinklr offers: “Marketing and comms software, with some machine learning built in.”

Despite 19% growth in revenue over the last fiscal year, its deficits increased during the same period. But with more than $250 million in cash available, “Sprinklr is not going public because it needs the money,” says Alex.

Since we were off yesterday for Memorial Day, today’s roundup is brief, but we’ll have much more to recap on Friday. Thanks very much for reading Extra Crunch!

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist


Full Extra Crunch articles are only available to members.
Use discount code ECFriday to save 20% off a one- or two-year subscription.


Once a buzzword, digital transformation is reshaping markets

Digital transformation concept. Binary code. AI (Artificial Intelligence).

Image Credits: metamorworks / Getty Images

The changes brought by a global shift to remote work and schooling are myriad, but in the business realm, they have yielded a change in corporate behavior and consumer expectations — changes that showed up in a bushel of earnings reports last week.

Startups have told us for several quarters that their markets are picking up momentum as customers shake up buying behavior with a distinct advantage for companies helping users move into the digital realm.

Public company results are now confirming the startups’ perspective. The accelerating digital transformation is real, and we have the data to prove it.

3 views on the future of meetings

In a recent episode of TechCrunch Equity, hosts Danny Crichton, Natasha Mascarenhas and Alex Wilhelm connected the dots between multiple funding rounds to sketch out three perspectives on the future of workplace meetings.

Each agreed that the traditional meeting is broken, so we gathered their perspectives about where the industry is heading and which aspects are ripe for disruption:

  • Alex Wilhelm: Faster information throughput, please.
  • Natasha Mascarenhas: Meetings should be ongoing, not in calendar invites.
  • Danny Crichton: Redesign meetings for flow.


By Walter Thompson

Breinify announces $11M seed to bring data science to the marketing team

Breinify is a startup working to apply data science to personalization, and do it in a way that makes it accessible to nontechnical marketing employees to build more meaningful customer experiences. Today the company announced a funding round totaling $11 million.

The investment was led by Gutbrain Ventures and PBJ Capital with participation from Streamlined Ventures, CXO Fund, Amino Capital, Startup Capital Ventures and Sterling Road.

Breinify co-founder and CEO Diane Keng says that she and co-founder and CTO Philipp Meisen started the company to bring predictive personalization based on data science to marketers with the goal of helping them improve a customer’s experience by personalizing messages tailored to individual tastes.

“We’re big believers that the world, especially consumer brands, really need strong predictive personalization. But when you think about consumer big brands or the retailers that you buy from, most of them aren’t data scientists, nor do they really know how to activate [machine learning] at scale,” Keng told TechCrunch.

She says that she wanted to make this type of technology more accessible by hiding the complexity behind the algorithms powering the platform. “Instead of telling you how powerful the algorithms are, we show you [what that means for the] consumer experience, and in the end what that means for both the consumer and you as a marketer individually,” she said.

That involves the kind of customizations you might expect around website messaging, emails, texts or whatever channel a marketer might be using to communicate with the buyer. “So the AI decides you should be shown these products, this offer, this specific promotion at this time, [whether it’s] the web, email or SMS. So you’re not getting the same content across different channels, and we do all that automatically for you, and that’s [driven by the algorithms],” she said.

Breinify launched in 2016 and participated in the TechCrunch Disrupt Startup Battlefield competition in San Francisco that year. She said it was early days for the company, but it helped them focus their approach. “I think it gave us a huge stage presence. It gave us a chance to test out the idea just to see where the market was in regards to needing a solution like this. We definitely learned a lot. I think it showed us that people were interested in personalization,” she said. And although the company didn’t win the competition, it ended up walking away with a funding deal.

Today the startup is growing fast and has 24 employees, up from 10 last year. Keng, who is an Asian woman, places a high premium on diversity.

“We partner with about four different kinds of diversity groups right now to source candidates, but at the end of the day, I think if you are someone that’s eager to learn, and you might not have all the skills yet, and you’re [part of an under-represented] group we encourage everyone to apply as much as possible. We put a lot of work into trying to create a really well-rounded group,” she said.


By Ron Miller

DataRobot expands platform and announces Zepl acquisition

DataRobot, the Boston-based automated machine learning startup, had a bushel of announcements this morning as it expanded its platform to give technical and non-technical users alike something new. It also announced it has acquired Zepl, giving it an advanced development environment where data scientists can bring their own code to DataRobot. The two companies did not share the acquisition price.

Nenshad Bardoliwalla, SVP of Product at DataRobot says that his company aspires to be the leader in this market and it believes the path to doing that is appealing to a broad spectrum of user requirements from those who have little data science understanding to those who can do their own machine learning coding in Python and R.

“While people love automation, they also want it to be [flexible]. They don’t want just automation, but then you can’t do anything with it. They also want the ability to turn the knobs and pull the levers,” Bardoliwalla explained.

To resolve that problem, rather than building a coding environment from scratch, it chose to buy Zepl and incorporate its coding notebook into the platform in a new tool called Composable ML. “With Composable ML and with the Zepl acquisition, we are now providing a really first class environment for people who want to code,” he said.

Zepl was founded in 2016 and raised $13 million along the way, according to Crunchbase data. The company didn’t want to reveal the number of employees or the purchase price, but the acquisition gives it advanced capabilities, especially a notebook environment to call its own to attract those more advanced users to the platform.The company plans to incorporate the Zepl functionality into the platform, while also leaving the stand-alone product in place.

Bardoliwalla said that they see the Zepl acquisition as an extension of the automated side of the house, where these tools can work in conjunction with one another with machines and humans working together to generate the best models. “This [generates an] organic mixture of the best of what a system can generate using DataRobot AutoML and the best of what human beings can do and kind of trying to compose those together into something really interesting […],” Bardoliwalla said.

The company is also introducing a no-code AI app builder that enables non-technical users to create apps from the data set with drag and drop components. In addition, it’s adding a tool to monitor the accuracy of the model over time. Sometimes, after a model is in production for a time, the accuracy can begin to break down as the data the model is based is no longer valid. This tool monitors the model data for accuracy and warns the team when it’s starting to fall out of compliance.

Finally the company is announcing a model bias monitoring tool to help root out model bias that could introduce racist, sexist or other assumptions into the model. To avoid this, the company has built a tool to identify when it sees this happening both in the model building phase and in production. It warns the team of potential bias, while providing them with suggestions to tweak the model to remove it.

DataRobot is based in Boston and was founded in 2012. It has raised over $750 million and has a valuation of over $2.8 billion, according to Pitchbook.


By Ron Miller

Tellius announces $8M Series A to build ML-fueled business data query tool

Getting actionable business information into the hands of users who need it has always been a challenge. If you have to wait for experts to help you find the answers, chances are you’re going to be too late. Enter Tellius, an early stage startup building a solution to help business users find the information they need when they need it.

Today the company announced an $8 million Series A led by Sands Capital Ventures with participation from Grotech. Today’s investment brings the total raised to $17 million, according to the company.

CEO and founder Ajay Khanna says the company is attempting to marry two technologies that have traditionally lived in silos: business intelligence and artificial intelligence. He believes that bringing them together can lead to greater wisdom and help close the insight gap.

“Tellius is an AI-driven decision intelligence platform, and what we do is we combine machine learning — AI-driven automation — with a Google-like natural language interface, so combining the left brain and the right brain to enable business teams to get insights on the data,” Khanna told me.

The idea is to let the machine learning teams and the business analysts continue to do their thing, but provide an application where business users can put all of that to work. “We believe that to go from data to decisions, you need to know not only what happened, but why things change and how you can improve your company,” he said.

The product takes aim at three employee groups. The first is the business user, who can simply query the data with a natural language question to get results. The second is a data analyst, who can get more granular by choosing a specific model to base the query on, and finally a data scientist who can enhance the query with Python or Spark code.

It connects to various data sources including Salesforce and Google Analytics, data lakes like Snowflake, csv files to take advantage of Excel data or cloud storage tools like Amazon S3. It comes in two versions: one that the customer can connect to the cloud infrastructure provider of choice, and one which they run as a service and manage for the customers.

Khanna says that as companies struggled to change the way they do business in during the pandemic, they needed the kind of insights his company provides and business grew 300% last year as a result.

The startup launched in 2016 after Khanna sold a previous company, which allowed him to bootstrap while in stealth. They spent a couple of years building the product and brought the first version of Tellius to market in Q3 2018. That’s when they took a $7.5 million seed round.


By Ron Miller

Cape Privacy announces $20M Series A to help companies securely share data

Cape Privacy, the early stage startup that wants to make it easier for companies to share sensitive data in a secure and encrypted way, announced a $20 million Series A today.

Evolution Equity Partners led the round with participation from new investors Tiger Global Management, Ridgeline Partners and Downing Lane. Existing investors Boldstart Ventures, Version One Ventures, Haystack, Radical Ventures and a slew of individual investors also participated. The company has now raised approximately $25 million including a $5 million seed investment we covered last June..

Cape Privacy CEO Ché Wijesinghe says that the product has evolved quite a bit since we last spoke. “We have really focused our efforts on encrypted learning, which is really the core technology, which was fundamental to allowing the multi-party compute capabilities between two organizations or two departments to work and build machine learning models on encrypted data,” Wijesinghe told me.

Wijesinghe says that a key business case involves a retail company owned by a private equity firm sharing data with a large financial services company, which is using the data to feed its machine learning models. In this case, sharing customer data, it’s essential to do it in a secure way and that is what Cape Privacy claims is its primary value prop.

He said that while the data sharing piece is the main focus of the company, it has data governance and compliance components to be sure that entities sharing data are doing so in a way that complies with internal and external rules and regulations related to the type of data.

While the company is concentrating on financial services for now because Wijesinghe has been working with these companies for years, he sees uses cases far beyond a single vertical including pharmaceuticals, government, healthcare telco and manufacturing.

“Every single industry needs this and so we look at the value of what Cape’s encrypted learning can provide as really being something that can be as transformative and be as impactful as what SSL was for the adoption of the web browser,” he said.

Richard Seewald, founding and managing partner at lead investor Evolution Equity Partners likes that ability to expand the product’s markets. “The application in Financial Services is only the beginning. Cape has big plans in life sciences and government where machine learning will help make incredible advances in clinical trials and counter-terrorism for example. We anticipate wide adoption of Cape’s technology across many use cases and industries,” he said.

The company has recently expanded to 20 people and Wijesinghe, who is half Asian, takes DEI seriously. “We’ve been very, very deliberate about our DEI efforts, and I think one of the things that we pride ourselves in is that we do foster a culture of acceptance, that it’s not just about diversity in terms of color, race, gender, but we just hired our first non binary employee,” he said,

Part of making people feel comfortable and included involves training so that fellow employees have a deeper understanding of the cultural differences. The company certainly has diversity across geographies with employees in 10 different time zones.

The company is obviously remote with a spread like that, but once the pandemic is over, Wijesinghe sees bringing people together on occasion with New York City as the hub for the company where people from all over the world can fly in and get together.


By Ron Miller

Tecton teams with founder of Feast open source machine learning feature store

Tecton, the company that pioneered the notion of the machine learning feature store, has teamed up with the founder of the open source feature store project called Feast. Today the company announced the release of version 0.10 of the open source tool.

The feature store is a concept that the Tecton founders came up with when they were engineers at Uber. Shortly thereafter an engineer named Willem Pienaar read the founder’s Uber blog posts on building a feature store and went to work building Feast as an open source version of the concept.

“The idea of Tecton [involved bringing] feature stores to the industry, so we build basically the best in class, enterprise feature store. […] Feast is something that Willem created, which I think was inspired by some of the early designs that we published at Uber. And he built Feast and it evolved as kind of like the standard for open source feature stores, and it’s now part of the Linux Foundation,” Tecton co-founder and CEO Mike Del Balso explained.

Tecton later hired Pienaar, who is today an engineer at the company where he leads their open source team. While the company did not originally start off with a plan to build an open source product, the two products are closely aligned, and it made sense to bring Pienaar on board.

“The products are very similar in a lot of ways. So I think there’s a similarity there that makes this somewhat symbiotic, and there is no explicit convergence necessary. The Tecton product is a superset of what Feast has. So it’s an enterprise version with a lot more advanced functionality, but at Feast we have a battle-tested feature store that’s open source,” Pienaar said.

As we wrote in a December 2020 story on the company’s $35 million Series B, it describes a feature store as “an end-to-end machine learning management system that includes the pipelines to transform the data into what are called feature values, then it stores and manages all of that feature data and finally it serves a consistent set of data.”

Del Balso says that from a business perspective, contributing to the open source feature store exposes his company to a different group of users, and the commercial and open source products can feed off one another as they build the two products.

“What we really like, and what we feel is very powerful here, is that we’re deeply in the Feast community and get to learn from all of the interesting use cases […] to improve the Tecton product. And similarly, we can use the feedback that we’re hearing from our enterprise customers to improve the open source project. That’s the kind of cross learning, and ideally that feedback loop involved there,” he said.

The plan is for Tecton to continue being a primary contributor with a team inside Tecton dedicated to working on Feast. Today, the company is releasing version 0.10 of the project.


By Ron Miller

DataJoy raises $6M seed to help SaaS companies track key business metrics

Every business needs to track fundamental financial information, but the data typically lives in a variety of silos making it a constant challenge to understand a company’s overall financial health. DataJoy, an early stage startup, wants to solve that issue. The company announced a $6 million seed round today led by Foundation Capital with help from Quarry VC, Partech Partners, IGSB, Bow Capital and SVB.

Like many startup founders, CEO Jon Lee has experienced the frustration first hand of trying to gather this financial data, and he decided to start a company to deal with it once and for all. “The reason why I started this company was that I was really frustrated at Copper, my last company because it was really hard just to find the answers to simple business questions in my data,” he told me.

These include basic questions like how the business is doing this quarter, if there are any surprises that could throw the company off track and where are the best places to invest in the business to accelerate more quickly.

The company has decided to concentrate its efforts for starters on SaaS companies and their requirements. “We basically focus on taking the work out of revenue intelligence, and just give you the insights that successful companies in the SaaS vertical depend on to be the largest and fastest growing in the market,” Lee explained.

The idea is to build a product with a way to connect to key business systems, pull the data and answer a very specific set of business questions, while using machine learning to provide more proactive advice.

While the company is still in the process of building the product and is pre-revenue, it has begun developing the pieces to ultimately help companies answer these questions. Eventually it will have a set of connectors to various key systems like Salesforce for CRM, HubSpot and Marketo for marketing, Netsuite for ERP, Gainsight for customer experience and Amplitude for product intelligence.

Lee says the set of connectors will be as specific as the questions themselves and based on their research with potential customers and what they are using to track this information. Ashu Garg, general partner at lead investor Foundation Capital says that he was attracted to the founding team’s experience, but also to the fact they were solving a problem he sees all the time sitting on the boards of various SaaS startups.

“I spend my life in the board meetings. It’s what I do, and every CEO, every board is looking for straight answers for what should be obvious questions, but they require this intersection of data,” Garg said. He says to an extent, it’s only possible now due to the evolution of technology to pull this all together in a way that simplifies this process.

The company currently has 11 employees with plans to double that by the middle of this year. As a long-time entrepreneur, Lee says that he has found that building a diverse workforce is essential to building a successful company. “People have found diversity usually [results in a company that is] more productive, more creative and works faster,” Lee said. He said that that’s why it’s important to focus on diversity from the earliest days of the company, while being proactive to make that happen. For example, ensuring you have a diverse set of candidates to choose from when you are reviewing resumes.

For now, the company is 100% remote. In fact, Lee and his co-founder Chief Product Officer Ken Lee, who was previously at Tableau, have yet to meet in person, but they are hoping that changes soon. The company will eventually have a presence in Vancouver and San Mateo whenever offices start to open.


By Ron Miller

TigerGraph raises $105M Series C for its enterprise graph database

TigerGraph, a well-funded enterprise startup that provides a graph database and analytics platform, today announced that it has raised a $105 million Series C funding round. The round was led by Tiger Global and brings the company’s total funding to over $170 million.

“TigerGraph is leading the paradigm shift in connecting and analyzing data via scalable and native graph technology with pre-connected entities versus the traditional way of joining large tables with rows and columns,” said TigerGraph found and CEO, Yu Xu. “This funding will allow us to expand our offering and bring it to many more markets, enabling more customers to realize the benefits of graph analytics and AI.”

Current TigerGraph customers include the likes of Amgen, Citrix, Intuit, Jaguar Land Rover and UnitedHealth Group. Using a SQL-like query language (GSQL), these customers can use the company’s services to store and quickly query their graph databases. At the core of its offerings is the TigerGraphDB database and analytics platform, but the company also offers a hosted service, TigerGraph Cloud, with pay-as-you-go pricing, hosted either on AWS or Azure. With GraphStudio, the company also offers a graphical UI for creating data models and visually analyzing them.

The promise for the company’s database services is that they can scale to tens of terabytes of data with billions of edges. Its customers use the technology for a wide variety of use cases, including fraud detection, customer 360, IoT, AI, and machine learning.

Like so many other companies in this space, TigerGraph is facing some tailwind thanks to the fact that many enterprises have accelerated their digital transformation projects during the pandemic.

“Over the last 12 months with the COVID-19 pandemic, companies have embraced digital transformation at a faster pace driving an urgent need to find new insights about their customers, products, services, and suppliers,” the company explains in today’s announcement. “Graph technology connects these domains from the relational databases, offering the opportunity to shrink development cycles for data preparation, improve data quality, identify new insights such as similarity patterns to deliver the next best action recommendation.”


By Frederic Lardinois

Base Operations raises $2.2 million to modernize physical enterprise security

Typically when we talk about tech and security, the mind naturally jumps to cybersecurity. But equally important, especially for global companies with large, multinational organizations, is physical security – a key function at most medium-to-large enterprises, and yet one that to date, hasn’t really done much to take advantage of recent advances in technology. Enter Base Operations, a startup founded by risk management professional Cory Siskind in 2018. Base Operations just closed their $2.2 million seed funding round, and will use the money to capitalize on its recent launch of a street-level threat mapping platform for use in supporting enterprise security operations.

The funding, led by Good Growth Capital and including investors like Magma Partners, First In Capital, Gaingels and First Round Capital founder Howard Morgan, will be used primarily for hiring, as Base Operations looks to continue its team growth after doubling its employe base this past month. It’ll also be put to use extending and improving the company’s product, and growing the startup’s global footprint. I talked to Siskind about her company’s plans on the heels of this round, as well as the wider opportunity and how her company is serving the market in a novel way.

“What we do at Base Operations is help companies keep their people in operation secure with ‘Micro Intelligence,’ which is street-level threat assessments that facilitate a variety of routine security tasks in the travel security, real estate and supply chain security buckets,” Siskind explained. “Anything that the Chief Security Officer would be in charge of, but not cyber – so anything that intersects with the physical world.”

Siskind has first-hand experience about the complexity and challenges that enter into enterprise security, since she began her career working for global strategic risk consultancy firm Control Risks in Mexico City. Because of her time in the industry, she’s keenly aware of just how far physical and political security operations lag behind their cybersecurity counterparts. It’s an often-overlooked aspect of corporate risk management, particularly since in the past it’s been something that most employees at North American companies only ever encounter periodically, when their roles involve frequent travel. The events of the past couple of years have changed that, however.

“This was the last bastion of a company that hadn’t been optimized by a SaaS platform, basically, so there was some resistance and some allegiance to legacy players,” Siskind told me. “However, the events of 2020 sort of turned everything on its head, and companies realized that the security department ,and what happens in the physical world, is not just about compliance – it’s actually a strategic advantage to invest in those sort of services, because it helps you maintain business continuity.”

The COVID-19 pandemic, increased frequency and severity of natural disasters, and global political unrest all had significant impact on businesses worldwide in 2020, and Siskind says that this has proven a watershed moment in how enterprises consider physical security in their overall risk profile and strategic planning cycles.

“[Companies] have just realized that if you don’t invest and how to keep your operations running smoothly in the face of rising catastrophic events, you’re never going to achieve the the profits that you need, because it’s too choppy, and you have all sorts of problems,” she said.

Base Operations addresses this problem by taking available data from a range of sources and pulling it together to inform threat profiles. Their technology is all about making sense of the myriad stream of information we encounter daily – taking the wash of news that we sometimes associate with ‘doom-scrolling’ on social media, for instance, and combining it with other sources using machine learning to extrapolate actionable insights.

Those sources of information include “government statistics, social media, local news, data from partnerships, like NGOs and universities,” Siskind said. That data set powers their Micro Intelligence platform, and while the startup’s focus today is on helping enterprises keep people safe, while maintaining their operations, you can easily see how the same information could power everything from planning future geographical expansion, to tailoring product development to address specific markets.

Siskind saw there was a need for this kind of approach to an aspect of business that’s essential, but that has been relatively slow to adopt new technologies. From her vantage point two years ago, however, she couldn’t have anticipated just how urgent the need for better, more scalable enterprise security solutions would arise, and Base Operations now seems perfectly positioned to help with that need.


By Darrell Etherington

Pinecone lands $10M seed for purpose-built machine learning database

Pinecone, a new startup from the folks who helped launch Amazon SageMaker, has built a vector database that generates data in a specialized format to help build machine learning applications faster, something that was previously only accessible to the largest organizations. Today the company came out of stealth with a new product and announced a $10 million seed investment led by Wing Venture Capital.

Company co-founder Edo Liberty says that he started the company because of this fundamental belief that the industry was being held back by the lack of wider access to this type of database. “The data that a machine learning model expects isn’t a JSON record, it’s a high dimensional vector that is either a list of features or what’s called an embedding that’s a numerical representation of the items or the objects in the world. This [format] is much more semantically rich and actionable for machine learning,” he explained.

He says that this is a concept that is widely understood by data scientists, and supported by research, but up until now only the biggest and technically superior companies like Google or Pinterest could take advantage of this difference. Liberty and his team created Pinecone to put that kind of technology in reach of any company.

The startup spent the last couple of years building the solution, which consists of three main components. The main piece is a vector engine to convert the data into this machine-learning ingestible format. Liberty says that this is the piece of technology that contains all the data structures and algorithms that allow them to index very large amounts of high dimensional vector data, and search through it in an efficient and accurate way.

The second is a cloud hosted system to apply all of that converted data to the machine learning model, while handling things like index lookups along with the pre- and post-processing — everything a data science team needs to run a machine learning project at scale with very large workloads and throughputs. Finally, there is a management layer to track all of this and manage data transfer between source locations.

One classic example Liberty uses is an eCommerce recommendation engine. While this has been a standard part of online selling for years, he believes using a vectorized data approach will result in much more accurate recommendations and he says the data science research data bears him out.

“It used to be that deploying [something like a recommendation engine] was actually incredibly complex, and […] if you have access to a production grade database, 90% of the difficulty and heavy lifting in creating those solutions goes away, and that’s why we’re building this. We believe it’s the new standard,” he said.

The company currently has 10 people including the founders, but the plan is to double or even triple that number, depending on how the year goes. As he builds his company as an immigrant founder — Liberty is from Israel — he says that diversity is top of mind. He adds that it’s something he worked hard on at his previous positions at Yahoo and Amazon as he was building his teams at those two organizations. One way he is doing that is in the recruitment process. “We have instructed our recruiters to be proactive [in finding more diverse applicants], making sure they don’t miss out on great candidates, and that they bring us a diverse set of candidates,” he said.

Looking ahead to post-pandemic, Liberty says he is a bit more traditional in terms of office versus home, and that he hopes to have more in-person interactions. “Maybe I’m old fashioned but I like offices and I like people and I like to see who I work with and hang out with them and laugh and enjoy each other’s company, and so I’m not jumping on the bandwagon of ‘let’s all be remote and work from home’.”


By Ron Miller

Extra Crunch roundup: 2 VC surveys, Tesla’s melt up, The Roblox Gambit, more

This has been quite a week.

Instead of walking backward through the last few days of chaos and uncertainty, here are three good things that happened:

  • Google employee Sara Robinson combined her interest in machine learning and baking to create AI-generated hybrid treats.
  • A breakthrough could make water desalination 30%-40% more effective.
  • Bianca Smith will become the first Black woman to coach a professional baseball team.

Despite many distractions in our first full week of the new year, we published a full slate of stories exploring different aspects of entrepreneurship, fundraising and investing.

We’ve already gotten feedback on this overview of subscription pricing models, and a look back at 2020 funding rounds and exits among Israel’s security startups was aimed at our new members who live and work there, along with international investors who are seeking new opportunities.

Plus, don’t miss our first investor surveys of 2021: one by Lucas Matney on social gaming, and another by Mike Butcher that gathered responses from Portugal-based investors on a wide variety of topics.

Thanks very much for reading Extra Crunch this week. I hope we can all look forward to a nice, boring weekend with no breaking news alerts.

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist


Full Extra Crunch articles are only available to members
Use discount code ECFriday to save 20% off a one- or two-year subscription


The Roblox Gambit

In February 2020, gaming platform Roblox was valued at $4 billion, but after announcing a $520 million Series H this week, it’s now worth $29.5 billion.

“Sure, you could argue that Roblox enjoyed an epic 2020, thanks in part to COVID-19,” writes Alex Wilhelm this morning. “That helped its valuation. But there’s a lot of space between $4 billion and $29.5 billion.”

Alex suggests that Roblox’s decision to delay its IPO and raise an enormous Series H was a grandmaster move that could influence how other unicorns will take themselves to market. “A big thanks to the gaming company for running this experiment for us.”

I asked him what inspired the headline; like most good ideas, it came to him while he was trying to get to sleep.

“I think that I had “The Queen’s Gambit somewhere in my head, so that formed the root of a little joke with myself. Roblox is making a strategic wager on method of going public. So, ‘gambit’ seems to fit!”

8 investors discuss social gaming’s biggest opportunities

girl playing games on desktop computer

Image Credits: Erik Von Weber (opens in a new window) / Getty Images

For our first investor survey of the year, Lucas Matney interviewed eight VCs who invest in massively multiplayer online games to discuss 2021 trends and opportunities:

  • Hope Cochran, Madrona Venture Group
  • Daniel Li, Madrona Venture Group
  • Niko Bonatsos, General Catalyst
  • Ethan Kurzweil, Bessemer Venture Partners
  • Sakib Dadi, Bessemer Venture Partners
  • Jacob Mullins, Shasta Ventures
  • Alice Lloyd George, Rogue
  • Gigi Levy-Weiss, NFX

Having moved far beyond shooters and sims, platforms like Twitch, Discord and Fortnite are “where culture is created,” said Daniel Li of Madrona.

Rep. Alexandria Ocasio-Cortez uses Twitch to explain policy positions, major musicians regularly perform in-game concerts on Fortnite and in-game purchases generated tens of billions last year.

“Gaming is a unique combination of science and art, left and right brain,” said Gigi Levy-Weiss of NFX. “It’s never just science (i.e., software and data), which is why many investors find it hard.”

How to convert customers with subscription pricing

Giant hand and magnet picking up office and workers

Image Credits: C.J. Burton (opens in a new window) / Getty Images

Startups that lack insight into their sales funnel have high churn, low conversion rates and an inability to adapt or leverage changes in customer behavior.

If you’re hoping to convert and retain customers, “reinforcing your value proposition should play a big part in every level of your customer funnel,” says Joe Procopio, founder of Teaching Startup.

What is up with Tesla’s value?

Elon Musk, founder of SpaceX and chief executive officer of Tesla Inc., arrives at the Axel Springer Award ceremony in Berlin, Germany, on Tuesday, Dec. 1, 2020. Tesla Inc. will be added to the S&P 500 Index in one shot on Dec. 21, a move that will ripple through the entire market as money managers adjust their portfolios to make room for shares of the $538 billion company. Photographer: Liesa Johannssen-Koppitz/Bloomberg via Getty Images

Image Credits: Bloomberg (opens in a new window) / Getty Images

Alex Wilhelm followed up his regular Friday column with another story that tries to find a well-grounded rationale for Tesla’s sky-high valuation of approximately $822 billion.

Meanwhile, GM just unveiled a new logo and tagline.

As ever, I learned something new while editing: A “melt up” occurs when investors start clamoring for a particular company because of acute FOMO (the fear of missing out).

Delivering 500,000 cars in 2020 was “impressive,” says Alex, who also acknowledged the company’s ability to turn GAAP profits, but “pride cometh before the fall, as does a melt up, I think.”

Note: This story has Alex’s original headline, but I told him I would replace the featured image with a photo of someone who had very “richest man in the world” face.

How Segment redesigned its core systems to solve an existential scaling crisis

Abstract glowing grid and particles

Image Credits: piranka / Getty Images

On Tuesday, enterprise reporter Ron Miller covered a major engineering project at customer data platform Segment called “Centrifuge.”

“Its purpose was to move data through Segment’s data pipes to wherever customers needed it quickly and efficiently at the lowest operating cost,” but as Ron reports, it was also meant to solve “an existential crisis for the young business,” which needed a more resilient platform.

Dear Sophie: Banging my head against the wall understanding the US immigration system

Image Credits: Sophie Alcorn

Dear Sophie:

Now that the U.S. has a new president coming in whose policies are more welcoming to immigrants, I am considering coming to the U.S. to expand my company after COVID-19. However, I’m struggling with the morass of information online that has bits and pieces of visa types and processes.

Can you please share an overview of the U.S. immigration system and how it works so I can get the big picture and understand what I’m navigating?

— Resilient in Romania

The first “Dear Sophie” column of each month is available on TechCrunch without a paywall.

Revenue-based financing: The next step for private equity and early-stage investment

Shot of a group of people holding plants growing out of soil

Image Credits: Hiraman (opens in a new window) / Getty Images

For founders who aren’t interested in angel investment or seeking validation from a VC, revenue-based investing is growing in popularity.

To gain a deeper understanding of the U.S. RBI landscape, we published an industry report on Wednesday that studied data from 134 companies, 57 funds and 32 investment firms before breaking out “specific verticals and business models … and the typical profile of companies that access this form of capital.”

Lisbon’s startup scene rises as Portugal gears up to be a European tech tiger

Man using laptop at 25th of April Bridge in Lisbon, Portugal

Image Credits: Westend61 (opens in a new window)/ Getty Images

Mike Butcher continues his series of European investor surveys with his latest dispatch from Lisbon, where a nascent startup ecosystem may get a Brexit boost.

Here are the Portugal-based VCs he interviewed:

  • Cristina Fonseca, partner, Indico Capital Partners
  • Pedro Ribeiro Santos, partner, Armilar Venture Partners
  • Tocha, partner, Olisipo Way
  • Adão Oliveira, investment manager, Portugal Ventures
  • Alexandre Barbosa, partner, Faber
  • António Miguel, partner, Mustard Seed MAZE
  • Jaime Parodi Bardón, partner, impACT NOW Capital
  • Stephan Morais, partner, Indico Capital Partners
  • Gavin Goldblatt, managing partner, Portugal Gateway

How late-stage edtech companies are thinking about tutoring marketplaces

Life Rings flying out beneath storm clouds are a metaphor for rescue, help and aid.

Image Credits: John Lund (opens in a new window)/ Getty Images

How do you scale online tutoring, particularly when demand exceeds the supply of human instructors?

This month, Chegg is replacing its seven-year-old marketplace that paired students with tutors with a live chatbot.

A spokesperson said the move will “dramatically differentiate our offerings from our competitors and better service students,” but Natasha Mascarenhas identified two challenges to edtech automation.

“A chatbot won’t work for a student with special needs or someone who needs to be handheld a bit more,” she says. “Second, speed tutoring can only work for a specific set of subjects.”

Decrypted: How bad was the US Capitol breach for cybersecurity?

Image Credits: Treedeo (opens in a new window) / Getty Images

While I watched insurrectionists invade and vandalize the U.S. Capitol on live TV, I noticed that staffers evacuated so quickly, some hadn’t had time to shut down their computers.

Looters even made off with a laptop from Senator Jeff Merkley’s office, but according to security reporter Zack Whittaker, the damages to infosec wasn’t as bad as it looked.

Even so, “the breach will likely present a major task for Congress’ IT departments, which will have to figure out what’s been stolen and what security risks could still pose a threat to the Capitol’s network.”

Extra Crunch’s top 10 stories of 2020

On New Year’s Eve, I made a list of the 10 “best” Extra Crunch stories from the previous 12 months.

My methodology was personal: From hundreds of posts, these were the 10 I found most useful, which is my key metric for business journalism.

Some readers are skeptical about paywalls, but without being boastful, Extra Crunch is a premium product, just like Netflix or Disney+. I know, we’re not as entertaining as a historical drama about the reign of Queen Elizabeth II or a space western about a bounty hunter. But, speaking as someone who’s worked at several startups, Extra Crunch stories contain actionable information you can use to build a company and/or look smart in meetings — and that’s worth something.


By Walter Thompson

How artificial intelligence will be used in 2021

Scale AI CEO Alexandr Wang doesn’t need a crystal ball to see where artificial intelligence will be used in the future. He just looks at his customer list.

The four-year-old startup, which recently hit a valuation of more than $3.5 billion, got its start supplying autonomous vehicle companies with the labeled data needed to train machine learning models to develop and eventually commercialize robotaxis, self-driving trucks and automated bots used in warehouses and on-demand delivery.

The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses.

In 2020, that changed as e-commerce, enterprise automation, government, insurance, real estate and robotics companies turned to Scale’s visual data labeling platform to develop and apply artificial intelligence to their respective businesses. Now, the company is preparing for the customer list to grow and become more varied.

How 2020 shaped up for AI

Scale AI’s customer list has included an array of autonomous vehicle companies including Alphabet, Voyage, nuTonomy, Embark, Nuro and Zoox. While it began to diversify with additions like Airbnb, DoorDash and Pinterest, there were still sectors that had yet to jump on board. That changed in 2020, Wang said.

Scale began to see incredible use cases of AI within the government as well as enterprise automation, according to Wang. Scale AI began working more closely with government agencies this year and added enterprise automation customers like States Title, a residential real estate company.

Wang also saw an increase in uses around conversational AI, in both consumer and enterprise applications as well as growth in e-commerce as companies sought out ways to use AI to provide personalized recommendations for its customers that were on par with Amazon.

Robotics continued to expand as well in 2020, although it spread to use cases beyond robotaxis, autonomous delivery and self-driving trucks, Wang said.

“A lot of the innovations that have happened within the self-driving industry, we’re starting to see trickle out throughout a lot of other robotics problems,” Wang said. “And so it’s been super exciting to see the breadth of AI continue to broaden and serve our ability to support all these use cases.”

The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses, Wang said, adding that advancements in natural language processing of text, improved offerings from cloud companies like AWS, Azure and Google Cloud and greater access to datasets helped sustain this trend.

“We’re finally getting to the point where we can help with computational AI, which has been this thing that’s been pitched for forever,” he said.

That slow burn heated up with the COVID-19 pandemic, said Wang, noting that interest has been particularly strong within government and enterprise automation as these entities looked for ways to operate more efficiently.

“There was this big reckoning,” Wang said of 2020 and the effect that COVID-19 had on traditional business enterprises.

If the future is mostly remote with consumers buying online instead of in-person, companies started to ask, “How do we start building for that?,” according to Wang.

The push for operational efficiency coupled with the capabilities of the technology is only going to accelerate the use of AI for automating processes like mortgage applications or customer loans at banks, Wang said, who noted that outside of the tech world there are industries that still rely on a lot of paper and manual processes.


By Kirsten Korosec

Arthur.ai snags $15M Series A to grow machine learning monitoring tool

At a time when more companies are building machine learning models, Arthur.ai wants to help by ensuring the model accuracy doesn’t begin slipping over time, thereby losing its ability to precisely measure what it was supposed to. As demand for this type of tool has increased this year, in spite of the pandemic, the startup announced a $15 million Series A today.

The investment was led by Index Ventures with help from new comers Acrew and Plexo Capital along with previous investors Homebrew, AME Ventures and Work-Bench.The round comes almost exactly a year after its $3.3 million seed round.

As CEO and co-founder Adam Wenchel explains, data scientists build and test machine learning models in the lab under ideal conditions, but as these models are put into production, the performance can begin to deteriorate under real world scrutiny. Arthur.AI is designed to root out when that happens.

Even as COVID has wreaked havoc throughout much of this year, the company has grown revenue 300% in the last six months smack dab in the middle of all that. “Over the course of 2020, we have begun to open up more and talk to [more] customers. And so we are starting to get some really nice initial customer traction, both in traditional enterprises as well as digital tech companies,” Wenchel told me. With 15 customers, the company is finding that the solution is resonating with companies.

It’s interesting to note that AWS announced a similar tool yesterday at re:Invent called SageMaker Clarify, but Wenchel sees this as more of a validation of what his startup has been trying to do, rather than an existential threat. “I think it helps create awareness, and because this is our 100% focus, our tools go well beyond what the major cloud providers provide,” he said.

Investor Mike Volpi from Index certainly sees the value proposition of this company. “One of the most critical aspects of the AI stack is in the area of performance monitoring and risk mitigation. Simply put, is the AI system behaving like it’s supposed to?,” he wrote in a blog post announcing the funding.

When we spoke a year ago, the company had 8 employees. Today it has 17 and it expects to double again by the end of next year. Wenchel says that as a company whose products looks for different types of bias, it’s especially important to have a diverse workforce. He says that starts with having a diverse investment team and board makeup, which he has been able to achieve, and goes from there.

“We’ve sponsored and work with groups that focus on both general sort of coding for different underrepresented groups as well as specifically AI, and that’s something that we’ll continue to do. And actually I think when we can get together for in person events again, we will really go out there and support great organizations like AI for All and Black Girls Code,” he said. He believes that by working with these groups, it will give the startup a pipeline to underrepresented groups, which they can draw upon for hiring as the needs arise.

Wenchel says that when he can go back to the office, he wants to bring employees back, at least for part of the week for certain kinds of work that will benefit from being in the same space.


By Ron Miller

AWS announces SageMaker Clarify to help reduce bias in machine learning models

As companies rely increasingly on machine learning models to run their businesses, it’s imperative to include anti-bias measures to ensure these models are not making false or misleading assumptions. Today at AWS re:Invent, AWS introduced Amazon SageMaker Clarify to help reduce bias in machine learning models.

“We are launching Amazon SageMaker Clarify. And what that does is it allows you to have insight into your data and models throughout your machine learning lifecycle,” Bratin Saha, Amazon VP and general manager of machine learning told TechCrunch.

He says that it is designed to analyze the data for bias before you start data prep, so you can find these kinds of problems before you even start building your model.

“Once I have my training data set, I can [look at things like if I have] an equal number of various classes, like do I have equal numbers of males and females or do I have equal numbers of other kinds of classes, and we have a set of several metrics that you can use for the statistical analysis so you get real insight into easier data set balance,” Saha explained.

After you build your model, you can run SageMaker Clarify again to look for similar factors that might have crept into your model as you built it. “So you start off by doing statistical bias analysis on your data, and then post training you can again do analysis on the model,” he said.

There are multiple types of bias that can enter a model due to the background of the data scientists building the model, the nature of the data and how they data scientists interpret that data through the model they built. While this can be problematic in general it can also lead to racial stereotypes being extended to algorithms. As an example, facial recognition systems have proven quite accurate at identifying white faces, but much less so when it comes to recognizing people of color.

It may be difficult to identify these kinds of biases with software as it often has to do with team makeup and other factors outside the purview of a software analysis tool, but Saha says they are trying to make that software approach as comprehensive as possible.

“If you look at SageMaker Clarify it gives you data bias analysis, it gives you model bias analysis, it gives you model explainability it gives you poor inference explainability it gives you a global explainability,” Saha said.

Saha says that Amazon is aware of the bias problem and that is why it created this tool to help, but he recognizes that this tool alone won’t eliminate all of the bias issues that can crop up in machine learning models, and they offer other ways to help too.

“We are also working with our customers in various ways. So we have documentation, best practices, and we point our customers to how to be able to architect their systems and work with the system so they get the desired results,” he said.

SageMaker Clarify is available starting to day in multiple regions.


By Ron Miller

Tecton.ai nabs $35M Series B as it releases machine learning feature store

Tecton.ai, the startup founded by three former Uber engineers who wanted to bring the machine learning feature store idea to the masses, announced a $35 million Series B today, just seven months after announcing their $20 million Series A.

When we spoke to the company in April, it was working with early customers in a beta version of the product, but today, in addition to the funding they are also announcing the general availability of the platform.

As with their Series A, this round has Andreessen Horowitz and Sequoia Capital coming back to co-lead the investment. The company has now raised $60 million.

The reason these two firms are so committed to Tecton is the specific problem around machine learning the company is trying to solve. “We help organizations put machine learning into production. That’s the whole goal of our company, helping someone build an operational machine learning application, meaning an application that’s powering their fraud system or something real for them […] and making it easy for them to build and deploy and maintain,” company CEO and co-founder Mike Del Balso explained.

They do this by providing the concept of a feature store, an idea they came up with and which is becoming a machine learning category unto itself. Just last week, AWS announced the Sagemaker Feature store, which the company saw as major validation of their idea.

As Tecton defines it, a feature store is an end-to-end machine learning management system that includes the pipelines to transform the data into what are called feature values, then it stores and manages all of that feature data and finally it serves a consistent set of data.

Del Balso says this works hand-in-hand with the other layers of a machine learning stack. “When you build a machine learning application, you use a machine learning stack that could include a model training system, maybe a model serving system or an MLOps kind of layer that does all the model management, and then you have a feature management layer, a feature store which is us — and so we’re an end-to-end lifecycle for the data pipelines,” he said.

With so much money behind the company it is growing fast, going from 17 employees to 26 since we spoke in April with plans to more than double that number by the end of next year. Del Balso says he and his co-founders are committed to building a diverse and inclusive company, but he acknowledges it’s not easy to do.

“It’s actually something that we have a primary recruiting initiative on. It’s very hard, and it takes a lot of effort, it’s not something that you can just make like a second priority and not take it seriously,” he said. To that end, the company has sponsored and attended diversity hiring conferences and has focused its recruiting efforts on finding a diverse set of candidates, he said.

Unlike a lot of startups we’ve spoken to, Del Balso wants to return to an office setup as soon as it is feasible to do so, seeing it as a way to build more personal connections between employees.


By Ron Miller