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Are high churn rates depressing earnings for app developers?
Jacob Eiting is CEO of RevenueCat, a platform for managing cross-platform in-app purchases, products and subscribers and analyzing in-app-purchase data.
Ever since Apple opened up subscription monetization to more apps in 2016 — and enticed developers with an 85/15 split on revenue from customers that remain subscribed for more than a year — subscription monetization and retention has felt like the Holy Grail for app developers. So much so that Google quickly followed suit in what appeared to be an example of healthy competition for developers in the mobile OS duopoly.
But how does that split actually work out for most apps? Turns out, the 85/15 split — which Apple is keen to mention anytime developers complain about the App Store rev share — doesn’t have a meaningful impact for most developers. Because churn.
No matter how great an app is, subscribers are going to churn. Sometimes it’s because of a credit card expiring or some other billing issue. And sometimes it’s more of a pause, and the user comes back after a few months. But the majority of churn comes from subscribers who, for whatever reason, decide that the app just isn’t worth paying for anymore. If a subscriber churns before the one-year mark, the developer never sees that 85% split. And even if the user resubscribes, Apple and Google reset the clock if a subscription has lapsed for more than 60 days. Rather convenient… for Apple and Google.
Top mobile apps like Netflix and Spotify report churn rates in the low single digits, but they are the outliers. According to our data, the median churn rate for subscription apps is around 13% for monthly subscriptions and around 50% for annual. Monthly subscription churn is generally a bit higher in the first few months, then it tapers off. But an average churn of 13% leaves just 20% of subscribers crossing that magical 85/15 threshold.
In practice, what this means is that, for all the hype around the 85/15 split, very few developers are going to see a meaningful increase in revenue:
Image Credits: RevenueCat (opens in a new window)
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Narrator raises $6.2M for a new approach to data modelling that replaces star schema
Snowflake went public this week, and in a mark of the wider ecosystem that is evolving around data warehousing, a startup that has built a completely new concept for modelling warehoused data is announcing funding. Narrator — which uses an 11-column ordering model rather than standard star schema to organise data for modelling and analysis — has picked up a Series A round of $6.2 million, money that it plans to use to help it launch and build up users for a self-serve version of its product.
The funding is being led by Initialized Capital along with continued investment from Flybridge Capital Partners and Y Combinator — where the startup was in a 2019 cohort — as well as new investors, including Paul Buchheit.
Narrator has been around for three years, but its first phase was based around providing modelling and analytics directly to companies as a consultancy, helping companies bring together disparate, structured data sources from marketing, CRM, support desks and internal databases to work as a unified whole. As consultants, using an earlier build of the tool that it’s now launching, the company’s CEO Ahmed Elsamadisi said he and others each juggled queries “for eight big companies single-handedly,” while deep-dive analyses were done by another single person.
Having validated that it works, the new self-serve version aims to give data scientists and analysts a simplified way of ordering data so that queries, described as actionable analyses in a story-like format — or “Narratives,” as the company calls them — can be made across that data quickly — hours rather than weeks — and consistently. (You can see a demo of how it works below provided by the company’s head of data, Brittany Davis.)
The new data-as-a-service is also priced in SaaS tiers, with a free tier for the first 5 million rows of data, and a sliding scale of pricing after that based on data rows, user numbers and Narratives in use.
Image Credits: Narrator
Elsamadisi, who co-founded the startup with Matt Star, Cedric Dussud and Michael Nason, said that data analysts have long lived with the problems with star schema modelling (and by extension the related format of snowflake schema), which can be summed up as “layers of dependencies, lack of source of truth, numbers not matching and endless maintenance,” he said.
“At its core, when you have lots of tables built from lots of complex SQL, you end up with a growing house of cards requiring the need to constantly hire more people to help make sure it doesn’t collapse.”
(We)Work Experience
It was while he was working as lead data scientist at WeWork — yes, he told me, maybe it wasn’t actually a tech company, but it had “tech at its core” — that he had a breakthrough moment of realising how to restructure data to get around these issues.
Before that, things were tough on the data front. WeWork had 700 tables that his team was managing using a star schema approach, covering 85 systems and 13,000 objects. Data would include information on acquiring buildings, to the flows of customers through those buildings, how things would change and customers might churn, with marketing and activity on social networks, and so on, growing in line with the company’s own rapidly scaling empire. All of that meant a mess at the data end.
“Data analysts wouldn’t be able to do their jobs,” he said. “It turns out we could barely even answer basic questions about sales numbers. Nothing matched up, and everything took too long.”
The team had 45 people on it, but even so it ended up having to implement a hierarchy for answering questions, as there were so many and not enough time to dig through and answer them all. “And we had every data tool there was,” he added. “My team hated everything they did.”
The single-table column model that Narrator uses, he said, “had been theorised” in the past but hadn’t been figured out.
The spark, he said, was to think of data structured in the same way that we ask questions, where — as he described it — each piece of data can be bridged together and then also used to answer multiple questions.
“The main difference is we’re using a time-series table to replace all your data modelling,” Elsamadisi explained. “This is not a new idea, but it was always considered impossible. In short, we tackle the same problem as most data companies to make it easier to get the data you want but we are the only company that solves it by innovating on the lowest-level data modelling approach. Honestly, that is why our solution works so well. We rebuilt the foundation of data instead of trying to make a faulty foundation better.”
Narrator calls the composite table, which includes all of your data reformatted to fit in its 11-column structure, the Activity Stream.
Elsamadisi said using Narrator for the first time takes about 30 minutes, and about a month to learn to use it thoroughly. “But you’re not going back to SQL after that, it’s so much faster,” he added.
Narrator’s initial market has been providing services to other tech companies, and specifically startups, but the plan is to open it up to a much wider set of verticals. And in a move that might help with that, longer term, it also plans to open source some of its core components so that third parties can build data products on top of the framework more quickly.
As for competitors, he says that it’s essentially the tools that he and other data scientists have always used, although “we’re going against a ‘best practice’ approach (star schema), not a company.” Airflow, DBT, Looker’s LookML, Chartio’s Visual SQL, Tableau Prep are all ways to create and enable the use of a traditional star schema, he added. “We’re similar to these companies — trying to make it as easy and efficient as possible to generate the tables you need for BI, reporting and analysis — but those companies are limited by the traditional star schema approach.”
So far the proof has been in the data. Narrator says that companies average around 20 transformations (the unit used to answer questions) compared to hundreds in a star schema, and that those transformations average 22 lines compared to 1,000+ lines in traditional modelling. For those that learn how to use it, the average time for generating a report or running some analysis is four minutes, compared to weeks in traditional data modelling.
“Narrator has the potential to set a new standard in data,” said Jen Wolf, Initialized Capital COO and partner and new Narrator board member, in a statement. “We were amazed to see the quality and speed with which Narrator delivered analyses using their product. We’re confident once the world experiences Narrator this will be how data analysis is taught moving forward.”
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Data virtualization service Varada raises $12M
Varada, a Tel Aviv-based startup that focuses on making it easier for businesses to query data across services, today announced that it has raised a $12 million Series A round led by Israeli early-stage fund MizMaa Ventures, with participation by Gefen Capital.
“If you look at the storage aspect for big data, there’s always innovation, but we can put a lot of data in one place,” Varada CEO and co-founder Eran Vanounou told me. “But translating data into insight? It’s so hard. It’s costly. It’s slow. It’s complicated.”
That’s a lesson he learned during his time as CTO of LivePerson, which he described as a classic big data company. And just like at LivePerson, where the team had to reinvent the wheel to solve its data problems, again and again, every company — and not just the large enterprises — now struggles with managing their data and getting insights out of it, Vanounou argued.
Image Credits: Varada
The rest of the founding team, David Krakov, Roman Vainbrand and Tal Ben-Moshe, already had a lot of experience in dealing with these problems, too, with Ben-Moshe having served at the chief software architect of Dell EMC’s XtremIO flash array unit, for example. They built the system for indexing big data that’s at the core of Varada’s platform (with the open-source Presto SQL query engine being one of the other cornerstones).
Image Credits: Varada
Essentially, Varada embraces the idea of data lakes and enriches that with its indexing capabilities. And those indexing capabilities is where Varada’s smarts can be found. As Vanounou explained, the company is using a machine learning system to understand when users tend to run certain workloads, and then caches the data ahead of time, making the system far faster than its competitors.
“If you think about big organizations and think about the workloads and the queries, what happens during the morning time is different from evening time. What happened yesterday is not what happened today. What happened on a rainy day is not what happened on a shiny day. […] We listen to what’s going on and we optimize. We leverage the indexing technology. We index what is needed when it is needed.”
That helps speed up queries, but it also means less data has to be replicated, which also brings down the cost. As MizMaa’s Aaron Applbaum noted, since Varada is not a SaaS solution, the buyers still get all of the discounts from their cloud providers, too.
In addition, the system can allocate resources intelligently so that different users can tap into different amounts of bandwidth. You can tell it to give customers more bandwidth than your financial analysts, for example.
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“Data is growing like crazy: in volume, in scale, in complexity, in who requires it and what the business intelligence uses are, what the API uses are,” Applbaum said when I asked him why he decided to invest. “And compute is getting slightly cheaper, but not really, and storage is getting cheaper. So if you can make the trade-off to store more stuff, and access things more intelligently, more quickly, more agile — that was the basis of our thesis, as long as you can do it without compromising performance.”
Varada, with its team of experienced executives, architects and engineers, ticked a lot of the company’s boxes in this regard, but he also noted that unlike some other Israeli startups, the team understood that it had to listen to customers and understand their needs, too.
“In Israel, you have a history — and it’s become less and less the case — but historically, there’s a joke that it’s ‘ready, fire, aim.’ You build a technology, you’ve got this beautiful thing and you’re like, ‘alright, we did it,’ but without listening to the needs of the customer,” he explained.
The Varada team is not afraid to compare itself to Snowflake, which at least at first glance seems to make similar promises. Vananou praised the company for opening up the data warehousing market and proving that people are willing to pay for good analytics. But he argues that Varada’s approach is fundamentally different.
“We embrace the data lake. So if you are Mr. Customer, your data is your data. We’re not going to take it, move it, copy it. This is your single source of truth,” he said. And in addition, the data can stay in the company’s virtual private cloud. He also argues that Varada isn’t so much focused on the business users but the technologists inside a company.
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Airtable’s Howie Liu has no interest in exiting, even as the company’s valuation soars
In the middle of a pandemic, Airtable, the low-code startup, has actually had an excellent year. Just the other day, the company announced it had raised $185 million on a whopping $2.585 billion valuation. It also announced some new features that take it from the realm of pure no-code and deeper into low-code territory, which allows users to extend the product in new ways.
Airtable CEO and co-founder Howie Liu was a guest today at TechCrunch Disrupt, where he was interviewed by TechCrunch News Editor Frederic Lardinois.
Liu said that the original vision that has stayed pretty steady since the company launched in 2013 was to democratize software creation. “We believe that more people in the world should become software builders, not just software users, and pretty much the whole time that we’ve been working on this company we’ve been charting our course towards that end goal,” he said.
But something changed recently, where Liu saw people who needed to do a bit more with the tool than that original vision allowed.
“So, the biggest shift that’s happening today with our fundraise and our launch announcement is that we’re going from being a no-code product, a purely no-code solution where you don’t have to use code, but neither can you use code to extend the product to now being a low-code solution, and one that also has a lot more extensibility with other features like automation, allowing people to build logic into Airtable without any technical knowledge,” he said.
In addition, the company, with 200,00 customers, has created a marketplace where users can share applications they’ve built. As the pandemic has taken hold, Liu says that he’s seen a shift in the types of deals he’s been seeing. That’s partly due to small businesses, which were once his company’s bread and butter, suffering more economic pain as a result of COVID.
But he has seen larger enterprise customers fill the void, and it’s not too big a stretch to think that the new extensibility features could be a nod to these more lucrative customers, who may require a bit more power than a pure no-code solution would provide.
“On the enterprise side of our business we’ve seen, for instance this summer, a 5x increase in enterprise deal closing velocity from the prior summer period, and this incredible appetite from enterprise signings with dozens of six-figure deals, some seven-figure deals and thousands of new paid customers overall,” he said.
In spite of this great success, the upward trend of the business and the fat valuation, Liu was in no mood to talk about an IPO. In his view, there is plenty of time for that, and in spite of being a seven-year-old company with great momentum, he says he’s simply not thinking about it.
Nor did he express any interest in being acquired, and he says that his investors weren’t putting any pressure on him to exit.
“It’s always been about finding investors who are really committed and aligned to the long-term goals and approach that we have to this business that matters more to us than the actual valuation numbers or any other kind of technical aspects of the round,” he said.
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Snyk bags another $200M at $2.6B valuation 9 months after last raise
When we last reported on Snyk in January, eons ago in COVID time, the company announced $150 million investment on a valuation of over $1 billion. Today, barely nine months later, it announced another $200 million and its valuation has expanded to $2.6 billion.
The company is obviously drawing some serious investor attention, and even a pandemic is not diminishing that interest. Addition led today’s round, bringing the total raised to $450 million with $350 million coming this year alone.
Snyk has a unique approach to security, building it into the development process instead of offloading it to a separate security team. If you want to build a secure product, you need to think about it as you’re developing the product, and that’s what Snyk’s product set is designed to do — check for security as you’re committing your build to your git repository.
With an open-source product at the top of funnel to drive interest in the platform, CEO Peter McKay says the pandemic has only accelerated the appeal of the company. In fact, the startup’s annual recurring revenue (ARR) is growing at a remarkable 275% year over year.
McKay says even with the pandemic his company has been accelerating, adding 100 employees in the last 12 months to take advantage of the increasing revenue. “When others were kind of scaling back we invested and it worked out well because our business never slowed down. In fact, in a lot of the industries it really picked up,” he said.
That’s because as many other founders have pointed out, COVID is speeding up the rate at which many companies are moving to the cloud, and that’s working to Snyk’s favor. “We’ve just capitalized on this accelerated shift to the cloud and modern cloud-native applications,” he said.
The company currently has 375 employees, with plans to add 100 more in the next year. As it grows, McKay says that he is looking to build a diverse and inclusive culture, something he learned about as he moved through his career at VMware and Veeam.
He says one of the keys at Snyk is putting every employee through unconscious bias training to help limit bias in the hiring process, and the executive team has taken a pledge to make the company’s hiring practices more diverse. Still, he recognizes it takes work to achieve these goals, and it’s always easy for an experienced team to go back to the network instead of digging deeper for a more diverse candidate pool.
“I think we’ve put all the pieces in place to get there, but I think like a lot of companies, there’s still a long way to go,” he said. But he recognizes the sooner you embed diversity into the company culture, the better because it’s hard to go back after the fact and do it.
Addition founder Lee Fixel says he sees a company that’s accelerating rapidly and that’s why he was willing to pour in so big an investment. “Snyk’s impressive growth is a signal that the market is ready to embrace a change from traditional security and empower developers to tackle the new security risk that comes with a software-driven digital world,” he said in a statement.
Snyk was founded in 2015. The founders brought McKay on board for some experienced leadership in 2018 to help lead the company through its rapid growth. Prior to the $350 million in new money this year, the company raised $70 million in 2019.
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Hasura raises $25 million Series B and adds MySQL support to its GraphQL service
Hasura, a service that provides developers with an open-source engine that provides them a GraphQL API to access their databases, today announced that it has raised a $25 million Series B round led by Lightspeed Venture Partners. Previous investors Vertex Ventures US, Nexus Venture Partners, Strive VC and SAP.iO Fund also participated in this round.
The new round, which the team raised after the COVID-19 pandemic had already started, comes only six months after the company announced its $9.9 million Series A round. In total, Hasura has now raised $36.5 million.
“We’ve been seeing rapid enterprise traction in 2020. We’ve wanted to accelerate our efforts investing in the Hasura community and our cloud product that we recently launched and to ensure the success of our enterprise customers. Given the VC inbound interest, a fundraise made sense to help us step on the gas pedal and give us room to grow comfortably,” Hasura co-founder and CEO Tanmai Gopal told me.
In addition to the new funding, Hasura also today announced that it has added support for MySQL databases. Until now, the company’s service only worked with PostgreSQL databases.
Rajoshi Ghosh, co-founder and COO (left) and Tanmai Gopal, co-founder and CEO (right). Image Credits: Hasura
As the company’s CEO and co-founder Tanmai Gopal told me, MySQL support has long been at the top of the most requested features by the service’s users. Many of these users — who are often in the healthcare and financial services industry — are also working with legacy systems they are trying to connect to modern applications and MySQL plays an important role there, given how long it has been around.
In addition to adding MySQL support, Hasura is also adding support for SQL Server to its lineup, but for now, that’s in early access.
“For MySQL and SQL Server, we’ve seen a lot of demand from our healthcare and financial services / fin-tech users,” Gopal said. “They have a lot of existing online data, especially in these two databases, that they want to activate to build new capabilities and use while modernizing their applications.
Today’s announcement also comes only a few months after the company launched a fully managed cloud service for its service, which complements its existing paid Pro service for enterprises.
“We’re very impressed by how developers have taken to Hasura and embraced the GraphQL approach to building applications,” said Gaurav Gupta, partner at Lightspeed Venture Partners and Hasura board member. “Particularly for front-end developers using technologies like React, Hasura makes it easy to connect applications to existing databases where all the data is without compromising on security and performance. Hasura provides a lovely bridge for re-platforming applications to cloud-native approaches, so we see this approach being embraced by enterprise developers as well as front-end developers more and more.”
The company plans to use the new funding to add support for more databases and to tackle some of the harder technical challenges around cross-database joins and the company’s application-level data caching system. “We’re also investing deeply in company building so that we can grow our GTM and engineering in tandem and making some senior hires across these functions,” said Gopal.
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Progress snags software automation platform Chef for $220M
Progress, a Boston-area developer tool company, boosted its offerings in a big way today when it announced it was acquiring software automation platform Chef for $220 million.
Chef, which went 100% open source last year, had annual recurring revenue (ARR) of $70 million from the commercial side of the house. Needless to say, Progress CEO Yogesh Gupta was happy to bring the company into the fold and gain not only that revenue, but a set of highly skilled employees, a strong developer community and an impressive customer list.
Gupta said that Chef fits with his company’s acquisition philosophy. “This acquisition perfectly aligns with our growth strategy and meets the requirements that we’ve previously laid out: a strong recurring revenue model, technology that complements our business, a loyal customer base and the ability to leverage our operating model and infrastructure to run the business more efficiently,” he said in a statement.
Chef CEO Barry Crist offered a typical argument for an acquired company; that Progress offered a better path to future growth, while sending a message to the open-source community and customers that Progress would be a good steward of the startup’s vision.
“For Chef, this acquisition is our next chapter, and Progress will help enhance our growth potential, support our Open Source vision, and provide broader opportunities for our customers, partners, employees and community,” Crist said in a statement.
Chef’s customer list is certainly impressive, and includes tech industry stalwarts like Facebook, IBM and SAP, as well as non-tech companies like Nordstrom, Alaska Airlines and Capital One.
The company was founded in 2008 and had raised $105 million, according to Crunchbase data. It hadn’t raised any funds since 2015, when it raised a $40 million Series E led by DFJ Growth. Other investors along the way included Battery Ventures, Ignition Partners and Scale Venture Partners.
The transaction is expected to close next month, pending normal regulatory approvals.
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Google Cloud launches its Business Application Platform based on Apigee and AppSheet
Unlike some of its competitors, Google Cloud has recently started emphasizing how its large lineup of different services can be combined to solve common business problems. Instead of trying to sell individual services, Google is focusing on solutions and the latest effort here is what it calls its Business Application Platform, which combines the API management capabilities of Apigee with the no-code application development platform of AppSheet, which Google acquired earlier this year.
As part of this process, Google is also launching a number of new features for both services today. The company is launching the beta of a new API Gateway, built on top of the open-source Envoy project, for example. This is a fully managed service that is meant to make it easier for developers to secure and manage their API across Google’s cloud computing services and serverless offerings like Cloud Functions and Cloud Run. The new gateway, which has been in alpha for a while now, offers all the standard features you’d expect, including authentication, key validation and rate limiting.
As for its low-code service AppSheet, the Google Cloud team is now making it easier to bring in data from third-party applications thanks to the general availability to Apigee as a data source for the service. AppSheet already supported standard sources like MySQL, Salesforce and G Suite, but this new feature adds a lot of flexibility to the service.
With more data comes more complexity, so AppSheet is also launching new tools for automating processes inside the service today, thanks to the early access launch of AppSheet Automation. Like the rest of AppSheet, the promise here is that developers won’t have to write any code. Instead, AppSheet Automation provides a visual interface, that, according to Google, “provides contextual suggestions based on natural language inputs.”
“We are confident the new category of business application platforms will help empower both technical and line of business developers with the core ability to create and extend applications, build and automate workflows, and connect and modernize applications,” Google notes in today’s announcement. And indeed, this looks like a smart way to combine the no-code environment of AppSheet with the power of Apigee .
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Lokalise raises $6 million to make it easier to localize your product
Meet Lokalise, a Latvian startup that focuses on translation and localization of apps, websites, games and more. The company provides a software-as-a-service product that helps you improve your workflow and processes when you need to update text in different languages in your product.
The company just raised a $6 million funding round led by Mike Chalfen, with capital300, Andrey Khusid, Nicolas Dessaigne, Des Traynor, Matt Robinson and others also participating.
When it’s time to ship an update, many companies waste time at the last minute as they still need to translate new buttons and new text in other languages. It’s often a manual process that involves sending and incorporating files with long lists of text strings in different languages.
“As a matter of fact, the most popular tools used in localisation processes are still Excel and Google Sheets. Next come internally-built scripts and tools,” co-founder and CEO Nick Ustinov told me.
Lokalise is all about speeding up that process. You can either manually upload your language files or integrate directly with GitHub or GitLab so that it automatically fetches changes.
You can then browse each sentence in different languages from the service. Your team of translators can edit text in the Lokalise interface. As a web-based service, everybody remains on the same page.
Image Credits: Lokalise
Some productivity features let you collaborate with other team members. You can comment and mention other people. You can assign tasks and trigger events based on completed tasks. For instance, Lokalise can notify a reviewer when a translation is done.
When everything is completed, you can use Lokalise to dynamically deliver language files to your mobile apps using SDKs and an API, or you can simply upload to an object storage bucket so that your app can fetch the latest language file from a server.
If you’re a small company and don’t have a team of translators, Lokalise lets you use Google Translate or a marketplace of professional translators. It works with Gengo or Lokalise’s own marketplace. There are some built-in spelling and grammar features to help you spot the most obvious errors.
“Most customers work with internal or external individual translators or language service providers (LSPs) directly,” Ustinov said. “The SaaS product generates 90% of our revenue — the revenue breakdown between the SaaS product and the marketplace of translation services is 90%/10%.”
The startup now has 1,500 customers, such as Revolut, Yelp, Virgin Mobile and Notion. It currently generates $4 million in annual recurring revenue.
Overall, Lokalise solves a very specific need. It is probably overkill for many companies. But if you ship often and you have customers all around the world, it could speed up the process a little bit.
Image Credits: Lokalise
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Transposit scores $35M to build data-driven runbooks for faster disaster recovery
Transposit is a company built by engineers to help engineers, and one big way to help them is to get systems up and running faster when things go wrong — as they always will at some point. Transposit has come up with a way to build runbooks for faster disaster recovery, while using data to update them in an automated fashion.
Today, the company announced a $35 million Series B investment led by Altimeter Capital, with participation from existing investors Sutter Hill Ventures, SignalFire and Unusual Ventures. Today’s investment brings the total raised to $50.4 million, according to the company.
Company CEO Divanny Lamas and CTO and founder Tina Huang see technology issues as less an engineering problem and more as a human problem, because it’s humans who have to clean up the messes when things go wrong. Huang says forgetting the human side of things is where she thinks technology has gone astray.
“We know that the real superpower of the product is that we focus on the human and the user side of things. And as a result, we’re building an engineering culture that I think is somewhat differentiated,” Huang told TechCrunch.
Transposit is a platform that at its core helps manage APIs, connections to other programs, so it starts with a basic understanding of how various underlying technologies work together inside a company. This is essential for a tool that is trying to help engineers in a moment of panic figure out how to get back to a working state.
When it comes to disaster recovery, there are essentially two pieces: getting the systems working again, then figuring out what happened. For the first piece, the company is building data-driven runbooks. By being data-driven, they aren’t static documents. Instead, the underlying machine learning algorithms can look at how the engineers recovered and adjust accordingly.
Image Credits: Transposit
“We realized that no one was focusing on what we realize is the root problem here, which is how do I have access to the right set of data to make it easier to reconstruct that timeline, and understand what happened? We took those two pieces together, this notion that runbooks are a critical piece of how you spread knowledge and spread process, and this other piece, which is the data, is critical,” Huang said.
Today the company has 26 employees, including Huang and Lamas, who Huang brought on board from Splunk last year to be CEO. The company is somewhat unique having two women running the organization, and they are trying to build a diverse workforce as they build their company to 50 people in the next 12 months.
The current make-up is 47% female engineers, and the goal is to remain diverse as they build the company, something that Lamas admits is challenging to do. “I wish I had a magic answer, or that Tina had a magic answer. The reality is that we’re just very demanding on recruiters. And we are very insistent that we have a diverse pipeline of candidates, and are constantly looking at our numbers and looking at how we’re doing,” Lamas said.
She says being diverse actually makes it easier to recruit good candidates. “People want to work at diverse companies. And so it gives us a real edge from a kind of culture perspective, and we find that we get really amazing candidates that are just tired of the status quo. They’re tired of the old way of doing things and they want to work in a company that reflects the world that they want to live in,” she said.
The company, which launched in 2016, took a few years to build the first piece, the underlying API platform. This year it added the disaster recovery piece on top of that platform, and has been running its beta since the beginning of the summer. They hope to add additional beta customers before making it generally available later this year.
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