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

WorkClout shifts focus to manufacturing performance support and raises $2.3M seed

WorkClout, a graduate of the Y Combinator Winter 2019 cohort, announced today that it has shifted its focus from manufacturing automation to manufacturing performance support and has raised a $2.3 million seed round.

The funding was led by Spider Capital with participation from Y Combinator, Liquid 2, Soma Capital, Pioneer Fund, Mehta Ventures and several individual investors.

When the company launched last year, it was looking at helping customers drive operational efficiency in their processes, but WorkClout founder and CEO Arjun Patel says they were seeing that there was a ceiling in terms of how much efficiency they could squeeze out of work processes using software.

At that point, Patel decided to take a step back and do some research to figure out how WorkClout could best help manufacturing customers with its software-based solutions. After surveying 124 manufacturers, he says that he realized that these companies really needed help training front-line workers, an area he says is called performance support.

“We found that most of the companies were saying that employees are the biggest challenge that they have to face in terms of how to engage them better or how to empower them better, because ultimately they realize people, even if there is automation, are still the driving force for a lot of sectors,” Patel told TechCrunch.

Towards the end of last year, the company built a new tool to help customers train employees for complex front-line tasks. The workers might have a phone or tablet, which shows them how to complete each task, and gives them feedback as they move through a set of tasks. It also enables these workers to communicate with one another and with management about issues they are seeing on the line. Managers can monitor communication and see how workers are doing on a back-end system in the office.

“We gave them the ability to allow employees to capture and share critical information in real time on the factory floor, where the goal is to actually create standardized multimedia and training content for machines, processes and stations, allowing new and existing employees to get better insight into their work, and at the same time, allowing employees to communicate better about problems on the floor and reduce downtime,” he explained.

Patel recognizes that this is a difficult time to pivot, but says he believes it puts the company in a better position to succeed in the long term. He has cut the team from nine to five employees in an effort to run lean for the short term.

He hopes to begin hiring again in the fourth quarter this year or, at the latest, by Q1 next year. He plans to use that time to build out the product and prepare for a big go-to market push whenever the economy begins to rebound.

He sees this money giving him a long runway of 2.5 years with the company’s current burn and revenue rates, and that should give him enough time to wait out the current economic downturn.


By Ron Miller

To make locks touchless, Proxy bluetooth ID raises $42M

We need to go hands-off in the age of coronavirus. That means touching fewer doors, elevators, and sign-in iPads. But once a building is using phone-based identity for security, there’s opportunities to speed up access to WIFI networks and printers, or personalize conference rooms and video call set-ups. Keyless office entry startup Proxy wants to deliver all of this while keeping your phone in your pocket.

The door is just a starting point” Proxy co-founder and CEO Denis Mars tells me. “We’re . . . empowering a movement to take back control of our privacy, our sense of self, our humanity, our individuality.”

With the contagion concerns and security risks of people rubbing dirty, cloneable, stealable key cards against their office doors, investors see big potential in Proxy. Today it’s announcing here a $42 million Series B led by Scale Venture Partners with participation from former funders Kleiner Perkins and Y Combinator plus new additions Silicon Valley Bank and West Ventures.

The raise brings Proxy to $58.8 million in funding so it can staff up at offices across the world and speed up deployments of its door sensor hardware and access control software. “We’re spread thin” says Mars. “Part of this funding is to try to grow up as quickly as possible and not grow for growth sake. We’re making sure we’re secure, meeting all the privacy requirements.”

How does Proxy work? Employers get their staff to install an app that knows their identity within the company, including when and where they’re allowed entry. Buildings install Proxy’s signal readers, which can either integrate with existing access control software or the startup’s own management dashboard.

Employees can then open doors, elevators, turnstiles, and garages with a Bluetooth low-energy signal without having to even take their phone out. Bosses can also opt to require a facial scan or fingerprint or a wave of the phone near the sensor. Existing keycards and fobs still work with Proxy’s Pro readers. Proxy costs about $300 to $350 per reader, plus installation and a $30 per month per reader subscription to its management software.

Now the company is expanding access to devices once you’re already in the building thanks to its SDK and APIs. Wifi router-makers are starting to pre-provision their hardware to automatically connect the phones of employees or temporarily allow registered guests with Proxy installed — no need for passwords written on whiteboards. Its new Nano sensors can also be hooked up to printers and vending machines to verify access or charge expense accounts. And food delivery companies can add the Proxy SDK so couriers can be granted the momentary ability to open doors when they arrive with lunch.

Rather than just indiscriminately beaming your identity out into the world, Proxy uses tokenized credentials so only its sensors know who you are. Users have to approve of new networks’ ability to read their tokens, Proxy has SOC-2 security audit certification, and complies with GDPR. “We feel very strongly about where the biometrics are stored . . . they should stay on your phone” says Mars.

Yet despite integrating with the technology for two-factor entry unlocks, Mars says “We’re not big fans of facial recognition. You don’t want every random company having your face in their database. The face becomes the password you were supposed to change every 30 days.”

Keeping your data and identity safe as we see an explosion of Internet Of Things devices was actually the impetus for starting Proxy. Mars had sold his teleconferencing startup Bitplay to Jive Software where he met his eventually co-founder Simon Ratner, who’d joined after his video annotation startup  Omnisio was acquired by YouTube. Mars was frustrated about every IoT lightbulb and appliance wanting him to download an app, set up a profile, and give it his data.

The duo founded Proxy in 2013 as a universal identity signal. Today it has over 60 customers. While other apps want you to constantly open them, Proxy’s purpose is to work silently in the background and make people more productive. “We believe the most important technologies in the world don’t seek your attention. They work for you, they empower you, and they get out of the way so you can focus your attention on what matters most — living your life.”

Now Proxy could actually help save lives. “The nature of our product is contactless interactions in commercial buildings and workplaces so there’s a bit of an unintended benefit that helps prevent the spread of the virus” Mars explains. “We have seen an uptick in customers starting to set doors and other experiences in longer-range hands-free mode so that users can walk up to an automated door and not have to touch the handles or badge/reader every time.”

The big challenge facing Proxy is maintaining security and dependability since it’s a mission-critical business. A bug or outage could potentially lock employees out of their workplace (when they eventually return from quarantine). It will have to keep hackers out of employee files. Proxy needs to stay ahead of access control incumbents like ADT and Honeywell as well as smaller direct competitors like $10 million-funded Nexkey and $28 million-funded Openpath.

Luckily, Proxy has found a powerful growth flywheel. First an office in a big building gets set up, then they convince the real estate manager to equip the lobby’s turnstiles and elevators with Proxy. Other tenants in the building start to use it, so they buy Proxy for their office. Then they get their offices in other cities on board…starting the flywheel again. That’s why Proxy is doubling down on sales to commercial real estate owners.

The question is when Proxy will start knocking on consumers’ doors. While leveling up into the enterprise access control software business might be tough for home smartlock companies like August, Proxy could go down market if it built more physical lock hardware. Perhaps we’ll start to get smart homes that know who’s home, and stop having to carry pointy metal sticks in our pockets.


By Josh Constine

YC-backed Snapboard is a no-code platform for building internal tools

No code tools are on the rise, and a YC-backed company called Snapboard is looking to join the fight.

Snapboard, led by solo founder Calum Moore, started when Moore decided to build one product a week for a year as a personal challenge. In the second week, he realized just how many apps and services it took not only to build the product, but to post about it on social media.

He wanted a way to manage all those apps and tools from one dashboard. So he built Snapboard.

Snapboard allows users to link together and manage a wide variety of apps and platforms in a single, customizable dashboard. Users can create boards that act as internal tools without getting the product or engineering team involved for an internal project. Moore describes it as “Airtable, but with all of your data already in there.”

Right now, more than 50 apps are available on the Snapboard platform, including Shopify, Dropbox, Google Analytics, MailChimp, MongoDB, MySQL, Trello, Zendesk, and many more. Moore isn’t concerned with onboarding new integrated apps for Snapboard as most of the popular tools used by startups and tech firms are API supported.

The use cases are innumerable, which is just as challenging as it is beneficial. Moore detailed a few examples, including building boards for each individual customer, combining Stripe data with emails sent through Mail Chimp to try and target behavior.

However, the flexibility of the platform means that it can do almost anything, but only if you know what you want to do with it. It can be difficult to evangelize for something that is so nebulous, and can be used so many ways.

Moore says the key is to sprint on building out the template library for Snapboard, offering new users a multitude of options as inspiration.

Snapboard offers a free tier, and then charges $10/month/seat for more advanced features. Thus far, the company has 3,000 registered users and around 230 WAUs.

The company is targeting tech companies but sees the potential for other industries to tap into Snapboard’s internal tool-making platform.

Beyond the difficulty of messaging a platform that can be used in countless ways, Moore identifies UX design as one of the company’s greatest challenges.

“We’re taking something only developers used to be able to do and making it available for everyone else,” said Moore. “If you give a developer a platform, they’ll work their way through it. They’ll find some way to make it work. Whereas, with less technical people, they want products to be very obvious and easy to use. So, for us, it’s about delivering that kind of technical experience in a really non-technical way.”

Snapboard has raised a total of $150K from Y Combinator and will present in the upcoming demo day.


By Jordan Crook

YC-backed Turing uses AI to help speed up the formulation of new consumer packaged goods

One of the more interesting and useful applications of artificial intelligence technology has been in the world of biotechnology and medicine, where now more than 220 startups (not to mention universities and bigger pharma companies) are using AI to accelerate drug discovery by using it to play out the many permutations resulting from drug and chemical combinations, DNA and other factors.

Now, a startup called Turing — which is part of the current cohort at Y Combinator due to present in the next Demo Day on March 22 — is taking a similar principle but applying it to the world of building (and ‘discovering’) new consumer packaged goods products.

Using machine learning to simulate different combinations of ingredients plus desired outcomes to figure out optimal formulations for different goods (hence the “Turing” name, a reference to Alan Turing’s mathematical model, referred to as the Turing machine), Turing is initially addressing the creation of products in home care (eg detergents), beauty, and food and beverage.

Turing’s founders claim that it is able to save companies millions of dollars by reducing the average time it takes to formulate and test new products, from an average of 12 to 24 months down to a matter of weeks.

Specifically, the aim is to reduce all the time that it takes to test combinations, giving R&D teams more time to be creative.

“Right now, they are spending more time managing experiments than they are innovating,” Manmit Shrimali, Turing’s co-founder and CEO, said.

Turing is in theory coming out of stealth today, but in fact it has already amassed an impressive customer list. It is already generating revenues by working with 8 brands owned by one of the world’s biggest CPG companies, and it is also being trialled by another major CPG behemoth (Turing is disclosing their names publicly, but suffice it to say, they and their brands are household names).

Turing is co-founded by Shrimali and Ajith Govind, two specialists in data science that had worked together on a previous startup called Dextro Analytics. Dextro had set out to help businesses use AI and other kinds of business analytics to help with identifying trends and decision making around marketing, business strategy and other operational areas.

While there, they identified a very specific use case for the same principles that was perhaps even more acute: the research and development divisions of CPG companies, which have (ironically, given their focus on the future) often been behind the curve when it comes to the “digital transformation” that has swept up a lot of other corporate departments.

“We were consulting for product companies and realised that they were struggling,” Shirmali said. Add to that the fact that CPG is precisely the kind of legacy industry that is not natively a tech company but can most definitely benefit from implementing better technology, and that spells out an interesting opportunity for how (and where) to introduce artificial intelligence into the mix.

R&D labs play a specific and critical role in the world of CPG.

Before eventually being shipped into production, this is where products are discovered; tested; tweaked in response to input from customers, marketing, budgetary and manufacturing departments and others; then tested again; then tweaked again; and so on. One of the big clients that Turing works with spends close to $400 million in testing alone.

But R&D is under a lot of pressure these days. While these departments are seeing their budgets getting cut, they continue to have a lot of demands. They are still being expected to meet timelines in producing new products (or often more likely, extensions of products) to keep consumers interested. There are a new host of environmental and health concerns around goods with huge lists of unintelligible ingredients, meaning they have to figure out how to simplify and improve the composition of mass-market products. And smaller direct-to-consumer brands are undercutting their larger competitors by getting to market faster with competitive offerings that have met new consumer tastes and preferences.

“In the CPG world, everyone was focused on marketing, and R&D was a blind spot,” Shrimali said, referring to the extensive investments that CPG have made into figuring out how to use digital to track and connect with users, and also how better to distribute their products. “To address how to use technology better in R&D, people need strong domain knowledge, and we are the first in the market to do that.”

Turing’s focus is to speed up the formulation and testing aspects that go into product creation to cut down on some of the extensive overhead that goes into putting new products into the market.

Part of the reason why it can take upwards of years to create a new product is because of all of the permutations that go into building something and making sure it works consistently as a consumer would expect it to (which still being consistent in production and coming in within budget).

“If just one ingredient is changed in a formulation, it can change everything,” Shirmali noted. And so in the case of something like a laundry detergent, this means running hundreds of tests on hundreds of loads of laundry to make sure that it works as it should.

The Turing platform brings in historical data from across a number of past permutations and tests to essentially virtualise all of this: it suggests optimal mixes and outcomes from them without the need to run the costly physical tests, and in turn this teaches the Turing platform to address future tests and formulations. Shrimali said that the Turing platform has already saved one of the brands some $7 million in testing costs.

Turing’s place in working with R&D gives the company some interesting insights into some of the shifts that the wider industry is undergoing. Currently, Shrimali said one of the biggest priorities for CPG giants include addressing the demand for more traceable, natural and organic formulations.

While no single DTC brand will ever fully eat into the market share of any CPG brand, collectively their presence and resonance with consumers is clearly causing a shift. Sometimes that will lead into acquisitions of the smaller brands, but more generally it reflects a change in consumer demands that the CPG companies are trying to meet. 

Longer term, the plan is for Turing to apply its platform to other aspects that are touched by R&D beyond the formulations of products. The thinking is that changing consumer preferences will also lead into a demand for better “formulations” for the wider product, including more sustainable production and packaging. And that, in turn, represents two areas into which Turing can expand, introducing potentially other kinds of AI technology (such as computer vision) into the mix to help optimise how companies build their next generation of consumer goods.


By Ingrid Lunden

Demodesk scores $2.3M seed for sales-focused online meetings

Demodesk, an early stage startup that wants to change how sales meetings are conducted online, announced a $2.3 million seed investment today.

Investors included GFC, FundersClub, Y Combinator, Kleiner Perkins and an unnamed group of angel investors. The company was a member of the Y Combinator Winter 2019 cohort.

CEO and co-founder Veronika Riederle says that the fact it’s so closely focused on sales separates it from other more general meeting tools like Zoom, WebEx or GoToMeeting. “We are building the first intelligent online meeting tool for customer facing conversations. So that is for inside sales and customer service professionals,” Riederle explained.

One of the key pieces of technology is what Riederle calls, “a unique approach to screen sharing.” Whereas most meeting software involves downloading software to use the tool, Demodesk doesn’t do this. You simply click a link and you’re in. The two parties online are seeing a live screen and each can interact with it. It’s not just a show and tell.

What’s more, in a sales scenario with a slide presentation, the customer sees the same live screen as the salesperson, but while the salesperson can see their presentation notes, the customer cannot.

She said while this could work for any number of scenarios from customer service to IT Help desks, at this stage in the company’s development she wants to concentrate on the sales scenario, then expand the vision over time. The service works on a subscription model with tiered-per user pricing starting at $19 per user per month.

When they got to Y Combinator, the company already had a working product and paying customers, but Riederle says that the experience has helped them grow the business to over 100 customers. “YC was extremely important for us because we immediately got access to an extremely valuable network of founders and potential customers, and also just a base for us to really [develop] the business.

Riderle founded the company with CTO Alex Popp in 2017 in Munich. Prior to this seed round, the founders mostly bootstrapped the company,. With the $2.3 million it should be able to hire more people and begin building out the product further, while investing in sales and marketing to expand its customer base.


By Ron Miller

Quilt Data launches from stealth with free portal to access petabytes of public data

Quilt Data‘s founders, Kevin Moore and Aneesh Karve, have been hard at work for the last four years building a platform to search for data quickly across vast repositories on AWS S3 storage. The idea is to give data scientists a way to find data in S3 buckets, and then package that data in forms that a business can use. Today, the company launched out of stealth with a free data search portal that not only proves what they can do, but also provides valuable access to 3.7 petabytes of public data across 23 S3 repositories.

The public data repository includes publicly available Amazon review data along with satellite images and other high-value public information. The product works like any search engine, where you enter a query, but instead of searching the web or an enterprise repository, it finds the results in S3 storage on AWS.

The results not only include the data you are looking for, it also includes all of the information around the data, such as Jupyter notebooks, the standard  workspace that data scientists use to build machine learning models. Data scientists can then use this as the basis for building their own machine learning models.

The public data, which includes over 10 billion objects, is a resource that data scientists should greatly appreciate it, but the company is offering access to this data out of more than pure altruism. It’s doing so because it wants to show what the platform is capable of, and in the process hopes to get companies to use the commercial version of the product.

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Quilt Data search results with data about the data found. Image: Quilt Data

Customers can try Quilt Data for free or subscribe to the product in the Amazon Marketplace. The company charges a flat rate of $550 per month for each S3 bucket. It also offers an enterprise version with priority support, custom features and education and on-boarding for $999 per month for each S3 bucket.

The company was founded in 2015 and was a member of the Y Combinator Summer 2017 cohort. The company has received $4.2 million in seed money so far from Y Combinator, Vertex Ventures, Fuel Capital and Streamlined Ventures along with other unnamed investors.


By Ron Miller

Work Life Ventures raises $5M for debut enterprise SaaS seed fund

Brianne Kimmel had no trouble transitioning from angel investor to general partner.

Initially setting out to garner $3 million in capital commitments, Kimmel, in just two weeks’ time, closed on $5 million for her debut venture capital fund Work Life Ventures. The enterprise SaaS-focused vehicle boasts an impressive roster of limited partners, too, including the likes of Zoom chief executive officer Eric Yuan, InVision CEO Clark Valberg, Twitch co-founder Kevin Lin, Cameo CEO Steven Galanis, Andreessen Horowitz general partners’ Marc Andreessen and Chris Dixon, Initialized Capital GP Garry Tan and fund-of-funds Slow Ventures, Felicis Ventures and NFX.

At the helm of the new fund, Kimmel joins a small group of solo female general partners. Dream Machine’s Alexia Bonatsos is targeting $25 million for her first fund. Day One Ventures’ Masha Drokova raised an undisclosed amount for her debut effort last year. Sarah Cone launched Social Impact Capital, a fund specializing in impact investing, in 2016, among others.

Meanwhile, venture capital fundraising is poised to reach all-time highs in 2019. In the first half of the year, a total of $20.6 billion in new capital was introduced to the startup market across more than 100 funds.

For most, the process of raising a successful venture fund can be daunting and difficult. For well-connected and established investors in the Bay Area, like Kimmel, raising a fund can be relatively seamless. Given the speed and ease of fund one in Kimmel’s case, she plans to raise her second fund with a $25 million target in as little as 12 months.

“The desire for the fund is to take a step back and imagine how do we build great consumer experiences in the workplace,” Kimmel tells TechCrunch.

Kimmel has been an active angel investor for years, sourcing top enterprise deals via SaaS School, an invite-only workshop she created to educate early-stage SaaS founders on SaaS growth, monetization, sales and customer success. Prior to launching SaaS School, which will continue to run twice a year, Kimmel led go-to-market strategy at Zendesk, where she built the Zendesk for Startups program.

 

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“You start by advising, then you start with very small angel checks,” Kimmel explains. “I reached this inflection point and it felt like a great moment to raise my own fund. I had friends like Ryan Hoover, who started Weekend Fund focused on consumer, and Alexia is one of my friends as well and I saw what she was doing with Dream Machine, which is also consumer. It felt like it was the right time to come out with a SaaS-focused fund.”

Emerging from stealth today, Work Life Ventures will invest up to $150,000 per company. To date, Kimmel has backed three companies with capital from the fund: Tandem, Dover and Command E. The first, Tandem, was amongst the most coveted deals in Y Combinator’s latest batch of companies. The startup graduated from the accelerator with millions from Andreessen Horowitz at a valuation north of $30 million.

Dover, another recent YC alum, provides recruitment software and is said to be backed by Founders Fund in addition to Work Life. Command E, currently in beta, is a tool that facilities search across multiple desktop applications. Kimmel is also an angel investor in Webflow, Girlboss, TechCrunch Disrupt 2018 Startup Battlefield winner Forethought, Voyage and others.

Work Life is betting on the consumerization of the enterprise, or the idea that the next best companies for modern workers will be consumer-friendly tools. In her pitch deck to LPs, she cites the success of Superhuman and Notion, a well-designed email tool and a note-taking app, respectively, as examples of the heightened demand for digestible, easy-to-use B2B products.

“The next generation of applications for the workplace sees people spinning out of Uber, Coinbase and Airbnb,” Kimmel said. “They’ve faced these challenges inside their highly efficient tech company so we are seeing more consumer product builders deeply passionate about the enterprise space.”

But Kimmel doesn’t want to bury her thesis in jargon, she says, so you won’t find any B2B lingo on Work Life’s website or Instagram.

She’s focusing her efforts on a more important issue often vacant from conversations surrounding investment in the future of work: diversity & inclusion.

Kimmel meets with every new female hire of her portfolio companies. Though it’s “increasingly non-scalable,” she admits, it’s part of a greater effort to ensure her companies are thoughtful about D&I from the beginning: “Because I have a very focused fund, it’s about maintaining this community and ensuring that people feel like their voices are heard,” she said.

“I want to be mindful that I am a female GP and I feel honored to have that title.”


By Kate Clark