Jitsu nabs $2M Seed to build open source data integration platform

Jitsu, a graduate of the Y Combinator Summer 2020 cohort, is developing an open source data integration platform that helps developers send data to a data warehouse. Today, the startup announced a $2 million seed investment.

Costanoa Ventures led the round with participation from YCombintaor, The House Fund and SignalFire.

In addition to the open source version of the software, the company has developed a hosted version that companies can pay to use, which shares the same name as the company. Peter Wysinski, Jitsu’s co-founder and CEO, says a good way to think about his company is an open source Segment, the customer data integration company that was recently sold to Twilio for $3.2 billion.

But he says, it goes beyond what Segment by allowing you to move all kinds of data whether customer data, connected device data or other types. “If you look at the space in general, companies want more granularity. So let’s say for example, a couple years ago you wanted to sync just your transactions from QuickBooks to your data warehouse, now you want to capture every single sale at the point of sale. What Jitsu lets you do is capture essentially all of those events, all of those streams, and send them to your data warehouse,” Wysinski explained.

Among the data warehouses it currently supports include Amazon Redshift, Google BigQuery, PostGres and Snowflake.

The founders built the open source project called EventNative to help solve problems they themselves were having moving data around at their previous jobs. After putting the open source version on GitHub a few months ago, they quickly attained 1000 stars, proving that they had delivered something that solved a common problem for data teams. They then built the hosted version, Jitsu, which went live a couple of weeks ago.

For now, the company is just the two co-founders, Wysinski and CTO Vladimir Klimontovich, but they intend to do some preliminary hiring over the next year to grow the company, most likely adding engineers. As they begin to build out the startup, Wysinski says that being open source will help drive diversity and inclusion in their hiring.

“The goal is essentially to go after that open source community and hire people from anywhere because engineers aren’t just […] one color or one race, they’re everywhere, and being open source, and especially being in a remote world, makes it so so much simpler [to build a diverse workforce], and a lot of companies I feel are going down that road,” he said.

He says along that line, the plan is to be a fully remote company, even after the pandemic ends, as they hire from anywhere. The goal is to have quarterly offsite meetings to check in with employees, but do the majority of the work remotely.


By Ron Miller

Datafold raises seed from NEA to keep improving the lives of data engineers

Data engineering is one of these new disciplines that has gone from buzzword to mission critical in just a few years. Data engineers design and build all the connections between sources of raw data (your payments information or ad-tracking data or what have you) and the ultimate analytics dashboards used by business executives and data scientists to make decisions. As data has exploded, so has their challenge of doing this key work, which is why a new set of tools has arrived to make data engineering easier, faster and better than ever.

One of those tools is Datafold, a YC-backed startup I covered just a few weeks ago as it was preparing for its end-of-summer Demo Day presentation.

Well, that Demo Day presentation and the company’s trajectory clearly caught the eyes of investors, since the startup locked in $2.1 million in seed funding from NEA, the company announced this morning.

As I wrote back in August:

With Datafold, changes made by data engineers in their extractions and transformations can be compared for unintentional changes. For instance, maybe a function that formerly returned an integer now returns a text string, an accidental mistake introduced by the engineer. Rather than wait until BI tools flop and a bunch of alerts come in from managers, Datafold will indicate that there is likely some sort of problem, and identify what happened.

Definitely read our profile if you want to learn more about the product and origin story.

Not a whole heck of a lot has changed over the past few weeks (some new features, some new customers), but with more money in its billfold, Datafold is going to keep on growing, hiring and taking on the world of data engineering.


By Danny Crichton

Mozart Data lands $4M seed to provide out-of-the-box data stack

Mozart Data founders Peter Fishman and Dan Silberman have been friends for over 20 years, working at various startups, and even launching a hot sauce company together along the way. As technologists, they saw companies building a data stack over and over. They decided to provide one for them and Mozart Data was born.

The company graduated from the Y Combinator Summer 2020 cohort in August and announced a $4 million seed round today led by Craft Ventures and Array Ventures with participation from Coelius Capital, Jigsaw VC, Signia VC, Taurus VC and various angel investors.

In spite of the detour into hot sauce, the two founders were mostly involved in data over the years and they formed strong opinions about what a data stack should look like. “We wanted to bring the same stack that we’ve been building at all these different startups, and make it available more broadly,” Fishman told TechCrunch.

They see a modern data stack as one that has different databases, SaaS tools and data sources. They pull it together, process it and make it ready for whatever business intelligence tool you use. “We do all of the parts before the BI tool. So we extract and load the data. We manage a data warehouse for you under the hood in Snowflake, and we provide a layer for you to do transformations,” he said.

The service is aimed mostly at technical people who know some SQL like data analysts, data scientists and sales and marketing operations. They founded the company earlier this year with their own money, and joined Y Combinator in June. Today, they have about a dozen customers and six employees. They expect to add 10-12 more in the next year.

Fishman says they have mostly hired from their networks, but have begun looking outward as they make their next hires with a goal of building a diverse company. In fact, they have made offers to several diverse candidates, who didn’t ultimately take the job, but he believes if you start looking at the top of the funnel, you will get good results. “I think if you spend a lot of energy in terms of top of funnel recruiting, you end up getting a good, diverse set at the bottom,” he said.

The company has been able to start from scratch in the midst of a pandemic and add employees and customers because the founders had a good network to pitch the product to, but they understand that moving forward they will have to move outside of that. They plan to use their experience as users to drive their message.

“I think talking about some of the whys and the rationale is our strategy for adding value to customers […], it’s about basically how would we set up a data stack if we were at this type of startup,” he said.


By Ron Miller

Explo snags $2.3M seed to help build customer-facing BI dashboards

Explo, a member of the Y Combinator Winter 2020 class, which is helping customers build customer-facing business intelligence dashboards, announced a $2.3 million seed round today. Investors included Amplo VC, Soma Capital and Y Combinator along with several individual investors.

The company originally was looking at a way to simplify getting data ready for models or other applications, but as the founders spoke to customers, they saw a big need for a simple way to build dashboards backed by that data and quickly pivoted.

Company CEO and co-founder Gary Lin says the company was able to leverage the core infrastructure, data engineering and production that it had built while at Y Combinator, but the new service they have created is much different from the original idea.

“In terms of the UI and the output, we had to build out the ability for our end users to create dashboards, for them to embed the dashboards and for them to customize the styles on these dashboards, so that it looks and feels as though it was part of their own product,” Lin explained.

While the founders had been working on the original idea since last year, they didn’t actually make the pivot until September. They made the change because they were hearing this was really what customers needed more than the tool they had been building while at Y Combinator. In fact, Chen says that their YC mentors and investors have been highly supportive of the switch.

The company is just getting started with the four original co-founders — Lin, COO Andrew Chen, CTO Rohan Varma and product designer Carly Stanisic — but the plan is to use this money to beef up the engineering team with three to five new hires.

With a diverse founding team, the company wants to continue looking at diversity as it builds the company. “One of the biggest reasons that we think diversity is important is that it allows us to have a bigger perspective and a grander perspective on things. And honestly, it’s in environments where I have personally […] been involved where we’ve actually been able to create the best ideas was by having a larger perspective. And so we definitely are going to be as inclusive as possible and are definitely thinking about that as we hire,” Lin said.

As the company has grown up during the pandemic, the founding core is used to working remotely and the goal moving forward is to be a distributed company. “We will be a remote distributed company so we’re hiring people no matter where they are, which actually makes it a lot easier from a hiring perspective because we’re able to reach a much more diverse and large pool of applicants,” Lin said.

They are in the process of thinking about how they can build a culture as they bring in distributed employees. “I think the way that we’ve started to see it is that working distributed is not a reduced experience, but just a different one and we are thinking about different things like how e organize new people when they on board, and maybe we can meet up as a team and have a retreat where we are located in the same place [when travel allows],” he said.

For now, they will remain remote as they take their first half dozen customers and begin to build the company with the new investment.


By Ron Miller

Leena AI nabs $8M Series as it expands from chatbots to HR service platform

When we covered Leena AI as a member of the Y Combinator Summer 2018 cohort, the young startup was firmly focused on building HR chatbots, but in the intervening years it has expanded the vision to a broader HR policy platform. Today, the company announced an $8 million Series A led by Greycroft with help from several individual industry investors.

Company CEO and co-founder Adit Jain says that in 2018 the company was concentrating on building an intelligent virtual assistant for HR-related questions. It allowed employees to ask the bot questions like how many vacation days they have left or what holidays they have off this year.

Over the last couple of years since leaving Y Combinator, the company has moved into broader HR service delivery. “So I’m talking about having an intelligent case management, knowledge management and document management system, which is backing the virtual assistant as well,” Jain explained.

He says that users should think of it as an entire system where the chatbot is the user interface for employees to interact with the HR information on the back end. For example, he says that the knowledge management component is where the chatbots find the answers to questions, and as employees interact with the chatbot, it grows more intelligent based on the feedback from them.

The document management piece enables HR to write or import HR policies and the case management system comes into play when the situation is too complex for the chatbot to handle and it has to be escalated to a human HR representative.

When we spoke to Jain in September 2018 at the time of his startup’s $2 million seed round, he had 16 customers and hoped to have 50 in the next 12-18 months. Today the company actually has 100 enterprise customers with 300,000 employees using the platform worldwide.

In fact, the pandemic has fueled business with more than half of those customers coming on board this year. He says this is because companies are looking for ways to digitize processes like HR as employees are working from home more.

“This is a trend that’s going to continue as organizations have realized the value of doing things with more and more digital applications taking care of your processes […] especially mundane, repeatable tasks being handed over to technology more and more,” Jain said.

As the business has grown this year, the company has expanded from 30 to 75 employees and he hopes to double that number in the next year. As he does, he has discussed with his lead investor how to build a diverse and inclusive culture at Leena AI .

One thing he is trying to do is raise some money from a diverse group of investors, approximately $400,000, and his hope is that these diverse investors can help him build solid diversity programs as he adds employees to his growing company.

That the startup hasn’t only grown during these turbulent times, but thrived shows that companies are looking to modernize every part of the enterprise technology stack, and that includes HR.


By Ron Miller

YC grad DigitalBrain snags $3.4M seed to streamline customer service tasks

Most startup founders have a tough road to their first round of funding, but the founders of Digital Brain had it a bit tougher than most. The two young founders survived by entering and winning hackathons to pay their rent and put on food on the table. One of the ideas they came up with at those hackathons was DigitalBrain, a layer that sits on top of customer service software like Zendesk to streamline tasks and ease the job of customer service agents.

They ended up in Y Combinator in the Summer 2020 class, and today the company announced a $3.4 million seed investment. This total includes $3 million raised this round, which closed in August, and previously unannounced investments of $250,000 in March from Unshackled Ventures and $150,000 from Y Combinator in May.

The round was led by Moxxie Ventures with help from Caffeinated Capital, Unshackled Ventures, Shrug Capital, Weekend Fund, Underscore VC and Scribble Ventures along with a slew of individual investors.

Company co-founder Kesava Kirupa Dinakaran says that after he and his partner Dmitry Dolgopolov met at hackathon in May 2019, they moved into a community house in San Francisco full of startup founders. They kept hearing from their housemates about the issues their companies faced with customer service as they began scaling. Like any good entrepreneur, they decided to build something to solve that problem.

“DigitalBrain is an external layer that sits on top of existing help desk software to actually help the support agents get through their tickets twice as fast, and we’re doing that by automating a lot of internal workflows, and giving them all the context and information they need to respond to each ticket making the experience of responding to these tickets significantly faster,” Dinakaran told TechCrunch.

What this means in practice is that customer service reps work in DigitalBrain to process their tickets, and as they come upon a problem such as canceling an order or reporting a bug, instead of traversing several systems to fix it, they chose the appropriate action in DigitalBrain, enter the required information, and the problem is resolved for them automatically.  In the case of a bug, it would file a Jira ticket with engineering. In the case of canceling an order, it would take all of the actions and update all of the records required by this request.

As Dinakaran points out they aren’t typical Silicon Valley startup founders. They are 20 year old immigrants from India and Russia respectively, who came to the U.S. with coding skills and a dream of building a company. “We are both outsiders to Silicon Valley. We didn’t go to college. We don’t come from families of means. We wanted to come here and build our initial network from ground up,” he said.

Eventually they met some folks through their housemates, who suggested that they apply to Y Combinator. “As we started to meet people that we met through our community house here, some of them were YC founders and they kept saying I think you guys will love the YC community, not just in terms of your ethos, but also just purely from a perspective of meeting new people and where you are,” he said.

He said while he and his co-founder have trouble wrapping their arms around a number like the amount they have in the bank now, considering it wasn’t that long ago that they struggling to meet expenses every month, they recognize this money buys them an opportunity to help start building a more substantial company.

“What we’re trying to do is really accelerate the development and building of what we’re doing. And we think if we push the gas pedal with the resources we’ve gotten, we’ll be able to accelerate bringing on the next couple of customers, and start onboarding some of the larger companies we’re interested in,” he said.


By Ron Miller

Datasaur snags $3.9M investment to build intelligent machine learning labeling platform

As machine learning has grown, one of the major bottlenecks remains labeling things so the machine learning application understands the data it’s working with. Datasaur, a member of the Y Combinator Winter 2020 batch, announced a $3.9 million investment today to help solve that problem with a platform designed for machine learning labeling teams.

The funding announcement, which includes a pre-seed amount of $1.1 million from last year and $2.8 million seed right after it graduated from Y Combinator in March, included investments from Initialized Capital, Y Combinator and OpenAI CTO Greg Brockman.

Company founder Ivan Lee says that he has been working in various capacities involving AI for seven years. First when his mobile gaming startup, Loki Studios was acquired by Yahoo! in 2013, and Lee was eventually moved to the AI team, and most recently at Apple. Regardless of the company, he consistently saw a problem around organizing machine learning labeling teams, one that he felt he was uniquely situated to solve because of his experience.

“I have spent millions of dollars [in budget over the years] and spent countless hours gathering labeled data for my engineers. I came to recognize that this was something that was a problem across all the companies that I’ve been at. And they were just consistently reinventing the wheel and the process. So instead of reinventing that for the third time at Apple, my most recent company, I decided to solve it once and for all for the industry. And that’s why we started Datasaur last year,” Lee told TechCrunch.

He built a platform to speed up human data labeling with a dose of AI, while keeping humans involved. The platform consists of three parts: a labeling interface, the intelligence component, which can recognize basic things, so the labeler isn’t identifying the same thing over and over, and finally a team organizing component.

He says the area is hot, but to this point has mostly involved labeling consulting solutions, which farm out labeling to contractors. He points to the sale of Figure Eight in March 2019 and to Scale, which snagged $100 million last year as examples of other startups trying to solve this problem in this way, but he believes his company is doing something different by building a fully software-based solution

The company currently offers a cloud and on-prem solution, depending on the customer’s requirements. It has 10 employees with plans to hire in the next year, although he didn’t share an exact number. As he does that, he says he has been working with a partner at investor Initialized on creating a positive and inclusive culture inside the organization, and that includes conversations about hiring a diverse workforce as he builds the company.

“I feel like this is just standard CEO speak but that is something that we absolutely value in our top of funnel for the hiring process,” he said.

As Lee builds out his platform, he has also worried about built-in bias in AI systems and the detrimental impact that could have on society. He says that he has spoken to clients about the role of labeling in bias and ways of combatting that.

“When I speak with our clients, I talk to them about the potential for bias from their labelers and built into our product itself is the ability to assign multiple people to the same project. And I explain to my clients that this can be more costly, but from personal experience I know that it can improve results dramatically to get multiple perspectives on the exact same data,” he said.

Lee believes humans will continue to be involved in the labeling process in some way, even as parts of the process become more automated. “The very nature of our existence [as a company] will always require humans in the loop, […] and moving forward I do think it’s really important that as we get into more and more of the long tail use cases of AI, we will need humans to continue to educate and inform AI, and that’s going to be a critical part of how this technology develops.”


By Ron Miller

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.

Narrative 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 singlehandedly,” 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.)

(And 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.)

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 the 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 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 1000+ 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.”


By Ingrid Lunden

Avo raises $3M for its analytics governance platform

Avo, a startup that helps businesses better manage their data quality across teams, today announced that it has raised a $3 million seed round led by GGV Capital, with participation from  Heavybit, Y Combinator and others.

The company’s founder, Stefania Olafsdóttir, who is currently based in Iceland, was previously the head of data science at QuizUp, which at some point had 100 million users around the world. “I had the opportunity to build up the Data Science Division, and that meant the cultural aspect of helping people ask and answer the right questions — and get them curious about data — but it also meant the technical part of setting up the infrastructure and tools and pipelines, so people can get the right answers when they need it,” she told me. “We were early adopters of self-serve product analytics and culture — and we struggled immensely with data reliability and data trust.”

Image Credits: Avo

As companies collect more data across products and teams, the process tends to become unwieldy and different teams end up using different methods (or just simply different tags), which creates inefficiencies and issues across the data pipeline.

“At first, that unreliable data just slowed down decision making, because people were just like, didn’t understand the data and needed to ask questions,” Olafsdóttir said about her time at QuizUp. “But then it caused us to actually launch bad product updates based on incorrect data.” Over time, that problem only became more apparent.

“Once organizations realize how big this issue is — that they’re effectively flying blind because of unreliable data, while their competition might be like taking the lead on the market — the default is to patch together a bunch of clunky processes and tools that partially increase the level of liability,” she said. And that clunky process typically involves a product manager and a spreadsheet today.

At its core, the Avo team set out to build a better process around this, and after a few detours and other product ideas, Olafsdóttir and her co-founders regrouped to focus on exactly this problem during their time in the Y Combinator program.

Avo gives developers, data scientists and product managers a shared workspace to develop and optimize their data pipelines. “Good product analytics is the product of collaboration between these cross-functional groups of stakeholders,” Olafsdóttir argues, and the goal of Avo is to give these groups a platform for their analytics planning and governance — and to set company-wide standards for how they create their analytics events.

Once that is done, Avo provides developers with typesafe analytics code and debuggers that allows them to take those snippets and add them to their code within minutes. For some companies, this new process can help them go from spending 10 hours on fixing a specific analytics issue to an hour or less.

Most companies, the team argues, know — deep down — that they can’t fully trust their data. But they also often don’t know how to fix this problem. To help them with this, Avo also today released its Inspector product. This tool processes event streams for a company, visualizes them and then highlights potential errors. These could be type mismatches, missing properties or other discrepancies. In many ways, that’s obviously a great sales tool for a service that aims to avoid exactly these problems.

One of Avo’s early customers is Rappi, the Latin American delivery service. “This year we scaled to meet the demand of 100,000 new customers digitizing their deliveries and curbside pickups. The problem with every new software release was that we’d break analytics. It represented 25% of our Jira tickets,” said Rappi’s head of Engineering, Damian Sima. “With Avo we create analytics schemas upfront, identify analytics issues fast, add consistency over time and ensure data reliability as we help customers serve the 12+ million monthly users their businesses attract.”

As most startups at this stage, Avo plans to use the new funding to build out its team and continue to develop its product.

“The next trillion-dollar software market will be driven from the ground up, with developers deciding the tools they use to create digital transformation across every industry. Avo offers engineers ease of implementation while still retaining schemas and analytics governance for product leaders,” said GGV Capital Managing Partner Glenn Solomon. “Our investment in Avo is an investment in software developers as the new kingmakers and product leaders as the new oracles.”


By Frederic Lardinois

YC alum Paragon snags $2.5M seed for low-code app integration platform

Low-code is a hot category these days. It helps companies build workflows or simple applications without coding skills, freeing up valuable engineering resources for more important projects. Paragon, a member of the Y Combinator Winter 2020 cohort, announced a $2.5 million seed round today for its low-code application integration platform.

Investors include Y Combinator, Village Global, Global Founders Capital, Soma Capital and FundersClub.

“Paragon makes it easier for non-technical people to be able to build out integrations using our visual workflow editor. We essentially provide building blocks for things like API requests, interactions with third party APIs and conditional logic. And so users can drag and drop these building blocks to create workflows that describe business logic in their application,” says company co-founder Brandon Foo.

Foo acknowledges there are a lot of low-code workflow tools out there, but many like UIPath, Blue Prism and Automation Anywhere concentrate on Robotic Process Automation (RPA) to automate certain tasks. He says he and co-founder Ishmael Samuel wanted to focus on developers.

“We’re really focused on how can we improve developer efficiency, and how can we bring the benefits of low code to product and engineering teams and make it easier to build products without writing manual code for every single integration, and really be able to streamline the product development process,” Foo told TechCrunch.

The way it works is you can drag and drop one of 1200 predefined connectors for tools like Stripe, Slack and Google Drive into a workflow template, and build connectors very quickly to trigger some sort of action. The company is built on AWS serverless architecture, so you define the trigger action and subsequent actions, and Paragon handles all of the back-end infrastructure requirements for you.

It’s early days for the company. After launching in private beta in January, the company has 80 customers. It currently has 6 employees including Foo, who previously co-founded Polymail and Samuel, who was previously lead engineer at Uber. They plan to hire 4 more employees this year.

With both founders people of color, they definitely are looking to build a diverse team around them. “I think it’s already sort of built into our DNA. As a diverse founding team we have perhaps a broader viewpoint and perspective in terms of hiring the kind of people that we seek to work with. Of course, I think there’s always room for improvements, and so we’re always looking for new ways that we can be more inclusive in our hiring recruiting process [as we grow],” he said.

As far as raising during a pandemic, he says it’s been a crazy time, but he believes they are solving a real problem and that they can succeed in spite of the macro economic conditions of the moment.


By Ron Miller

Recurrency is taking on giants like SAP with a modern twist on ERP

Recurrency, a member of the Summer 2020 Y Combinator cohort, was started by a 21 year old just out of college. He decided to take on a highly established market that is led by giants like SAP, Infor, Oracle and Microsoft, but instead of taking a highly complex area of enterprise software in one big bite, he is starting by helping wholesale businesses.

Sole founder and company CEO Sam Oshay just graduated from the University of Pennsylvania with a dual degree that straddled engineering and business, before joining the summer batch. Oshay is bringing a modern twist to ERP by using machine learning to drive more data-driven decision making.

“What makes us different from other ERPs like SAP, Infor and Evercore is that we can tell the user something that they don’t already know.” He says these traditional ERPs are basically data entry systems. For example, you could enter a pricing list, but you can’t do anything with it in terms of predictions.

“We can scan historical data and make pricing recommendations and predictions. So we are an ERP that not only does data analysis, but also imports external data and matches it to internal data to make recommendations and predictions,” Oshay explained.

While he doesn’t expect to remain confined to just the wholesale side of the business, it makes sense that he started with it because his family has a history of running these kinds of businesses. In fact, his grandfather immigrated to the U.S. after World War II and started a hardware wholesale business that his uncle still runs today. His dad started his own business selling wholesale shipping supplies, and he grew up in the family business, giving him some insight that most recent college grads probably wouldn’t have.

“I learned about the wholesale business at a very deep level. And what I observed is that so many of the issues with my dad’s business came down to issues with his ERP system. It occurred to me that if someone were to build an ERP extension or a better ERP, they could unlock so much of the value that is currently locked inside these legacy systems,” he said.

So he did what good entrepreneurs do, and began building it. For starters, his system plugs into legacy systems like SAP or NetSuite, but the plan is to build a better ERP, one step at a time. For now, it’s about wholesale, but he has a much broader vision for his company.

He originally applied to YC during the Fall 2019 semester of his junior year, and was admitted to the winter batch, but deferred to the Summer 2020 group to complete his studies. He spent his remaining time at UPenn sprinting to early graduation, taking 10 classes to come close to finishing his studies (with just a dissertation standing between him and his degree).

With this batch being delivered remotely, he says that the YC team has taken that into account and is still offering a meaningful experience for the summer group. “All of the events that YC would normally be doing are still happening, just remotely. And to my knowledge, some of the events we’re doing are designed specifically for this weird set of circumstances. The YC team has put quite a bit of thought into making this batch meaningful and I think they’ve succeeded,” he said.

While the pandemic has created new challenges for an early-stage business, he says that in some ways it’s helped him focus better. Instead of going out with friends, he’s home with his head down working on his company with little distraction.

As you would expect, it’s early days for the product, but he has three customers who are operational and two more in the implementation phase. He also has two employees so far, a front end and back end engineer.

For now, he’s going to continue building his product and his business, and he sees the pandemic as a time when businesses might be more open to changing a system like a legacy ERP. “If they want to try something new, and you can make it easier for them to try that, I’ve found that’s a place where you can make a sale,” he said.


By Ron Miller

QuestDB nabs $2.3M seed to build open source time series database

QuestDB, a member of the Y Combinator summer 2020 cohort, is building an open source time series database with speed top of mind. Today the startup announced a $2.3 million seed round.

Episode1 Ventures led the round with assistance from Seedcamp, 7percent Ventures, YCombinator, Kima Ventures and several unnamed angel investors.

The database was originally conceived in 2013 when current CTO Vlad Ilyushchenko was building trading systems for a financial services company and he was frustrated by the performance limitations of the databases available at the time, so he began building a database that could handle large amounts of data and process it extremely fast.

For a number of years, QuestDB was a side project, a labor of love for Ilyushchenko until he met his other co-founders Nicolas Hourcard, who became CEO and Tancrede Collard, who became CPO, and the three decided to build a startup on top of the open source project last year.

“We’re building an open source database for time series data, and time series databases are a multi-billion dollar market because they’re central for financial services, IoT and other enterprise applications. And we basically make it easy to handle explosive amounts of data, and to reduce infrastructure costs massively,” Hourcard told TechCrunch.

He adds that it’s also about high performance. “We recently released a demo that you can access from our website that enables you to query a super large datasets — 1.6 billion rows with sub-second queries, mostly, and that just illustrates how performant the software is,” he said.

He sees open source as a way to build adoption from the bottom up inside organizations, winning the hearts and minds of developers first, then moving deeper in the company when they eventually build a managed cloud version of the product. For now, being open source also helps them as a small team to have a community of contributors help build the database and add to its feature set.

“We’ve got this open source product that is free to use, and it’s pretty important for us to have such a distribution model because we can basically empower developers to solve their problems, and we can ask for contributions from various communities. […] And this is really a way to spur adoption,” Hourcard said.

He says that working with YC has allowed them to talk to other companies in the ecosystem who have built similar open source-based startups and that’s been helpful, but it has also helped them learn to set and meet goals and have access to some of the biggest names in Silicon Valley, including Marc Andreessen, who delivered a talk to the cohort the same day we spoke.

Today the company has 7 employees including the three founders, spread out across the US, EU and South America. He sees this geographic diversity helping when it comes to building a diverse team in the future. “We definitely want to have more diverse backgrounds to make sure that we keep having a diverse team and we’re very strongly committed to that.”

For the short term, the company wants to continue building its community, working on continuing to improve the open source product, while working on the managed cloud product.


By Ron Miller

FeaturePeek moves beyond Y Combinator with $1.8M seed

FeaturePeek’s founders graduated from Y Combinator in Summer 2019, which for an early stage startup must seem like a million years ago right now. Despite the current conditions though, the company announced a $1.8 million seed investment today.

The round was led by Matrix Partners with some unnamed Angel investors also participating.

The startup has built a solution to allow teams to review front-end designs throughout the development process instead of waiting until the end when the project has been moved to staging, co-founder Eric Silverman explained.

FeaturePeek is designed to give front end capabilities that enable developers to get feedback from all their different stakeholders at every stage in the development process and really fill in the missing gaps of the review cycle,” he said.

He added, “Right now, there’s no dedicated place to give feedback on that new work until it hits their staging environment, and so we’ll spin up ad hoc deployment previews, either on commit or on pull requests and those fully running environments can be shared with the team. On top of that, we have our overlay where you can file bugs you can annotate screenshots, record video or leave comments.”

Since last summer, the company has remained lean with three full time employees, but it has continued to build out the product. In addition to the funding, the company also announced a free command line version of the product for single developers in addition to the teams product it has been building since the Y Combinator days.

Ilya Sukhar, partner at Matrix Partners says as a former engineer, he had experienced this kind of problem first hand, and he knew that there was a lack of tooling to help. That’s what attracted him to FeaturePeek.

“I think FeaturePeek is kind of a company that’s trying to change that and try to bring all of these folks together in an environment where they can review running code in a way that really wasn’t possible before, and I certainly have been frustrated on both ends of this where as an engineer, you’re kind of like okay I wrote it, are you ever going to look at it,” he said.

Sukhar recognizes these are trying times to launch a startup, and nobody really knows how things are going to play out, but he encourages these companies not to get too caught up in the macro view at this stage.

Silverman knows that he needs to adapt his go to market strategy for the times, and he says the founders are making a concerted effort to listen to users and find ways to improve the product while finding ways to communicate with the target audience.


By Ron Miller

UpKeep raises $36 million Series B to help facilities and maintenance teams go mobile

UpKeep, a mobile-first platform for maintenance and operations collaboration, has today announced the close of a $36 million Series B financing round. The round was led by Insight Partners, with participation from existing investors Emergence Capital, Battery Ventures, Y Combinator, Mucker Capital and Fundersclub.

UpKeep was founded by Ryan Chan. Chan worked at Trisep Corporation, a chemical manufacturing company, before founding UpKeep and saw first-hand how plant maintenance was handled. Despite the fact that the plant had purchased software for facilities maintenance and operations, most of the data was written down on pen and paper before being input into the system because that software was desktop only.

The idea for UpKeep was born.

UpKeep meets maintenance workers where they are, which could be just about anywhere.

With any maintenance job, from changing a lightbulb in an office building to repairing a complicated piece of machinery on the floor of a manufacturing plant, there are usually three parties involved: the requester, the facilities manager, and the technician.

Before UpKeep, the requester would either send an email to the facilities manager or perhaps use some other software to let them know of the problem. The facilities manager would prioritize the various requests of the day and send out technicians to resolve them.

Technicians have to log plenty of information when they’re out on the job, but this usually involved writing this info down on paper and then returning to a desk to input the data into the system.

With UpKeep, the requester can use the app itself to notify the facilities manager of problems, or send an email that flows directly into the UpKeep system. Facilities managers use UpKeep to prioritize and assign issues to their team of technicians, who then receive the work orders right on UpKeep.

Instead of logging information on paper, these technicians can take pictures of the problem and note the parts they need or other details of the job right in the app. No duplication of effort.

UpKeep operates on a freemium model, allowing technicians to manage their own work for free. Collaborative use of the product across an organization costs on a per user on both an annual or monthly basis. The company offers various tiers, from a Starter Plan ($35/month/user) to an Enterprise Plan ($180/month/user).

Higher tier plans offer more in-depth reporting and analysis around the work that gets done. Chan explained that these reports are not necessarily about tracking people, though.

“Yes, we track technicians and it’s a tool to manage work done by people,” said Chan. “But a manufacturing facility really cares much more about the equipment. They can use UpKeep to manage things like how many hours of downtime a piece of equipment has, etc. It’s more targeted toward the actual asset and the equipment versus the person completing their work.”

Chan said that around 80 percent of the company’s 400,000 users are on the free version of the app. Some brands on the app include Unilever, Siemens, DHL, McDonald’s, and Jet.com. Chan said UpKeep saw a 206 percent increase in revenue in 2019.

Important to the company’s future, UpKeep is working with OSHA and a group called SQF (Safe Quality Food) to offer templates around best practices during the pandemic. Now, maintenance workers and facilities staffs have a whole new checklist around sanitation and safety that many businesses are just getting up to speed on. UpKeep is working to make these new practices easier to adopt by providing those checklists directly to facilities managers.

This latest funding round brings UpKeep’s total funding to $48.8 million.


By Jordan Crook

VC’s largest funds make big bets on vertical B2B marketplaces

During the waning days of the first dot-com boom, some of the biggest names in venture capital invested in marketplaces and directories whose sole function was to consolidate information and foster transparency in industries that had remained opaque for decades.

The thesis was that thousands of small businesses were making specialized products consumed by larger businesses in huge industries, but the reach of smaller players was limited by their dependence on a sales structure built on conferences and personal interactions.

Companies making pharmaceuticals, chemicals, construction materials and medical supplies represented trillions in sales, but those huge aggregate numbers hide how fragmented these supply chains are — and how difficult it is for buyers to see the breadth of sellers available.

Now, similar to the way business models popularized by Kozmo.com and Webvan in decades past have since been reincarnated as Postmates and DoorDash, the B2B directory and marketplace rises from the investment graveyard.

The first sign of life for the directory model came with the success of GoodRX back in 2011. The company proved that when information about pricing in a previously opaque industry becomes available, it can unleash a torrent of new demand.


By Jonathan Shieber