Sramana Mitra: In a way, you’re doing PaaS, but I’m talking about something that’s slightly different. For example, Shopify is doing a very good job of this. They have a core e-commerce...

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Startups have done some wild things to get the attention of VCs. In fact, Instacart founder Apoorva Mehta sent YC partner (at the time) Garry Tan a six-pack of beer through the service after missing the deadline for Y Combinator by two months.

Yesterday, the ingenuity of startups struck again.

Tadabase.io, an enterprise startup that offers no-code tools to help businesses automate their processes, has had an ad running that was… well, hyper targeted.

ProductHunt founder and WeekendFund investor Ryan Hoover discovered the ad and shared it on Twitter.

Hoover told TechCrunch he was Googling Mark Suster to facilitate an introduction between Suster and one of Hoover’s portfolio companies. Instead, he found a Google ad directed squarely at Suster from Tadabase.io.

“Mark Suster, you haven’t invested in nocode” read the paid listing. “Therefore, we put this ad here to get your attention. If you’re not Mark, please don’t click here and save us some money.”

I reached out to Suster, managing partner at UpFront Ventures, to see what he thought of the ad. He told me he “loved it” and has already contacted the CEO to set up a call for next week.

Whether this clever Google ad will result in an actual investment is yet to be determined. Also unclear: will Ryan Hoover get in on the deal?

I reached out to Tadabase founder and CEO Moe Levine via email to ask about the ad, how they went about targeting, and how he feels about his upcoming phone call next week. He hasn’t responded yet. I’ll update if/when he does.

I’m publishing this series on LinkedIn called Colors to explore a topic that I care deeply about: the Renaissance Mind. I am just as passionate about entrepreneurship, technology, and business, as I...

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Sramana Mitra: Put in perspective what’s happening in the set of companies like Shopify and BigCommerce that enable individual e-commerce sites. They seem to be doing very well, which means their...

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It’s amazing that in this day and age, the best way to search for new clothes is to click a few check boxes and then scroll through endless pictures. Why can’t you search for “green patterned scoop neck dress” and see one? Glisten is a new startup enabling just that by using computer vision to understand and list the most important aspects of the products in any photo.

Now, you may think this already exists. In a way, it does — but not a way that’s helpful. Co-founder Sarah Wooders encountered this while working on a fashion search project of her own while going to MIT.

“I was procrastinating by shopping online, and I searched for v-neck crop shirt, and only like two things came up. But when I scrolled through there were 20 or so,” she said. “I realized things were tagged in very inconsistent ways — and if the data is that gross when consumers see it, it’s probably even worse in the backend.”

As it turns out, computer vision systems have been trained to identify, really quite effectively, features of all kinds of images, from identifying dog breeds to recognizing facial expressions. When it comes to fashion and other relatively complex products, they do the same sort of thing: Look at the image and generate a list of features with corresponding confidence levels.

So for a given image, it would produce a sort of tag list, like this:

As you can imagine, that’s actually pretty useful. But it also leaves a lot to be desired. The system doesn’t really understand what “maroon” and “sleeve” really mean, except that they’re present in this image. If you asked the system what color the shirt is, it would be stumped unless you manually sorted through the list and said, these two things are colors, these are styles, these are variations of styles, and so on.

That’s not hard to do for one image, but a clothing retailer might have thousands of products, each with a dozen pictures, and new ones coming in weekly. Do you want to be the intern assigned to copying and pasting tags into sorted fields? No, and neither does anyone else. That’s the problem Glisten solves, by making the computer vision engine considerably more context-aware and its outputs much more useful.

Here’s the same image as it might be processed by Glisten’s system:

Better, right?

“Our API response will be actually, the neckline is this, the color is this, the pattern is this,” Wooders said.

That kind of structured data can be plugged far more easily into a database and queried with confidence. Users (not necessarily consumers, as Wooders explained later) can mix and match, knowing that when they say “long sleeves” the system has actually looked at the sleeves of the garment and determined that they are long.

The system was trained on a growing library of around 11 million product images and corresponding descriptions, which the system parses using natural language processing to figure out what’s referring to what. That gives important contextual clues that prevent the model from thinking “formal” is a color or “cute” is an occasion. But you’d be right in thinking that it’s not quite as easy as just plugging in the data and letting the network figure it out.

Here’s a sort of idealized version of how it looks:

“There’s a lot of ambiguity in fashion terms and that’s definitely a problem,” Wooders admitted, but far from an insurmountable one. “When we provide the output for our customers we sort of give each attribute a score. So if it’s ambiguous, whether it’s a crew neck or a scoop neck, if the algorithm is working correctly it’ll put a lot of weight on both. If it’s not sure, it’ll give a lower confidence score. Our models are trained on the aggregate of how people labeled things, so you get an average of what people’s opinion is.”

The model was initially aimed at fashion and clothing in general, but with the right training data it can apply to plenty of other categories as well — the same algorithms could find the defining characteristics of cars, beauty products and so on. Here’s how it might look for a shampoo bottle — instead of sleeves, cut and occasion you have volume, hair type and paraben content.

Although shoppers will likely see the benefits of Glisten’s tech in time, the company has found that its customers are actually two steps removed from the point of sale.

“What we realized over time was that the right customer is the customer who feels the pain point of having messy unreliable product data,” Wooders explained. “That’s mainly tech companies that work with retailers. Our first customer was actually a pricing optimization company, another was a digital marketing company. Those are pretty outside what we thought the applications would be.”

It makes sense if you think about it. The more you know about the product, the more data you have to correlate with consumer behaviors, trends and such. Knowing summer dresses are coming back, but knowing blue and green floral designs with 3/4 sleeves are coming back is better.

Competition is mainly internal tagging teams (the manual review we established none of us would like to do) and general-purpose computer vision algorithms, which don’t produce the kind of structured data Glisten does.

Even ahead of Y Combinator’s demo day next week the company is already seeing five figures of monthly recurring revenue, with their sales process limited to individual outreach to people they thought would find it useful. “There’s been a crazy amount of sales these past few weeks,” Wooders said.

Soon Glisten may be powering many a product search engine online, though ideally you won’t even notice — with luck you’ll just find what you’re looking for that much easier.

(This article originally had Alice Deng quoted throughout when in fact it was Wooders the whole time — a mistake in my notes. It has also been updated to better reflect that the system is applicable to products beyond fashion.)

Y Combinator’s Demo Day is a key soirée on the startup calendar. This year, however, instead of a packed room replete with short pitches and lackluster catering, Demo Day has gone virtual. Even more, it was moved up by a week, pushing the public debut of a host of companies to this coming Monday.

TechCrunch will be covering it closely, so make sure to stick around the site for notes and interviews. But who wants to wait that long? I’ve gotten to know one of the pitching startups, FitnessAI, over the past few weeks. Let’s take a look at its business.

FitnessAI

FitnessAI is a mobile application that helps users lift weights, helping them set new goals, gain strength over time and avoid frustration while dodging burnout.

The company was founded by Jake Mor in 2019, leveraging a workout data set that Mor had previously collected. Mor told TechCrunch that in college he built a tool called Lift Log (you can find it on the App Store here). That app was “just a very simple weightlifting tracking tool,” Mor said, but “over the course of three years over 40,000 users logged 6 million workouts.”

That huge set of workout data points helped Mor construct FitnessAI’s core weight-lifting algorithm.

Peering through his mountain of data, Mor said that he was able to discern “the perfect rate of progression for each exercise.” Regular progression isn’t a nice to have, according to Mor, but a key way to keep people in the gym (and using his service), saying that he’s found that breaking personal records “makes working out a little bit more addicting, and more motivating to keep going back to the gym.”

But data isn’t the full story to FitnessAI, despite it featuring “AI” in its name. During the life of his company, Mor found that a human touch was key to keeping users engaged. He told TechCrunch that lots of folks are self-conscious about going to the gym and working out in its environment, so while he was “so busy working on [the] algorithm” that powers the company’s service, “what users cared most about was tutorials.”

The helping hand of crafted guides and human outreach work together with the app’s code to keep people engaged. According to Mor, his team “will reach out to every single user if you don’t go to the gym,” adding that “half of fitness AI is the human touch.”

So from a data set to an algorithm to a mobile app to a guided weight-lifting experience, FitnessAI has gotten a lot done in the last year or so. And it has done so while largely self-funding.

Money

To date, the company told TechCrunch that it has bootstrapped, apart from its standard Y Combinator check. That said, it’s looking to raise during the Demo Day cycle.

How has it gotten to where it is today on such little capital? By growing its revenues and paying for its own development. Indeed, the mobile app company is now north of $100,000 monthly recurring revenue (MRR), giving it annual recurring revenue (ARR) of more than $1.2 million. For a team of four today that was 1.5 not too long ago, that’s lots of cash.

But with more money comes more opportunities for product improvements, and go-to-market work. What FitnessAI has shown so far is that people need help lifting, and they are willing to pay for assistance. (FitnessAI has a number of price points, but costs a little less than $100 yearly, looking at its App Store listing today.)

More after Demo Day if FitnessAI raises the round it’s hunting for.

When concerns about the novel coronavirus — and subsequent changes in activity — are not bringing productivity to a halt (and perhaps especially in times of needing to be as efficient as possible), one of the bigger IT trends has been a push to streamline how people work by creating better integrations between the different apps that they use. Today, a startup out of Berlin, Germany is announcing seed funding to help it enter the fray of those that are helping make those integrations happen seamlessly and more reliably.

n8n, a Berlin-based company that has built a “fair code” workflow automation platform to let developers quickly integrate any of the apps that they use to work together automatically — from standard third-party APIs to internal tools created by developers themselves — has picked up a seed round of $1.5 million to continue building out its service, and specifically to introduce its first commercial elements after announcing its existence last October and meeting an unexpected surge of interest.

“I was surprised, but it seems like people were waiting for me,” Jan Oberhauser, n8n’s founder and CEO, said in an interview, who added that n8n has picked up “a lot of traction” so far.

The investment is being co-led by UK’s firstminute Capital and Sequoia, with participation also from Runa Capital, Tiny VC and System.One, as well as Kevin Hartz, co-founder of Eventbrite & Xoom, Ilkka Paananen, co-founder of Supercell, and Nan Li and Daniel Liem of Obvious Ventures (individually, not via Obvious).

Within that pretty impressive list, investment represents a significant step in particular for Sequoia, as it is the storied firm’s first seed investment in Germany amid a much bigger push into the region. The Silicon Valley VC has been quietly putting down roots in the European market over the last several months, including scouting for talent and local deals. The first hire in that process was announced this week: Luciana Lixandru, poached after years at Accel, is the firm’s first European partner, but for now this isn’t extending to raising a local fund.

According to a source familiar with the matter, Sequoia will continue to invest in Europe out of its U.S. funds and doesn’t have any plans to launch any funds in Europe at this time.

There are a number of other firms, startups as well as much bigger outfits, that have identified the opportunity for making tools to help developers and others who are less technical to stitch together disparate apps. They include other startups like Zapier, RapidAPI, and Tray.io, as well as companies that have well and truly transitioned out of the startup phase of life, such as MuleSoft (acquired by Salesforce for the princely sum of $6.5 billion).

Oberhauser is well aware of all of these, because he is a developer himself who has tried them all — and found them all lacking, for a number of reasons. Either they were too pricey, or not flexible or robust enough to use in the wide variety of niche applications that he was using in his previous life in film production, or required a ton of reading of arcane documentation, or lacked the ability to scale or operate on his own company’s infrastructure rather than in the cloud. His answer was to build n8n, first for his own purposes and then to consider how it might be something that could be turned into a service for others.

[gallery type="slideshow" ids="1958989,1958987,1958986"]

One of the unique things about n8n is that it’s not “open source” per se, but is built on a model that is somewhat akin to it that is referred to as “fair code”.

The idea here is to take some of the free and flexible aspects of building (and third-party developers building upon) open source, while also trying to create a model that lets the original developer of the code make money off of it — either by offering services around it (similar to the kind of integration and other work that has sprouted around open source) — or, indeed, by charging for it when the user passes a certain size, or wants to use it in a different format, such as on a SaaS model.

Oberhauser is not only a user of fair code, but has become something of a pioneering entrepreneur in the space, also helping to run a site, appropriately called Fair-Code.io to encourage more fair code developers.

“Free and sustainable; open but pragmatic; community oriented; meritocratic and fair” is how n8n describes it, although there are definitely plans for n8n to bring in monetising elements into the mix.

The current version is one that can be hosted by a user locally — which in itself is a key part of the proposition for companies to meet certain data protection compliance, or to ensure themselves against any changes that might happen with n8n over time — and that will remain free to use.

“If the company goes bust or changes policy, you are in trouble,” Oberhauser said of platforms that don’t freely share their code. “That means they can never go to insurance or government organizations, for example. And people really like and care about data privacy, and are getting like that more every day. They want to own it and change it. Developers want to have access to the the code that is underlying and extend it really easily. What we have built you can integrate and use forever.”

But n8n also plans to launch a version under a SaaS model that be charged on a typical SaaS subscription model, which is due to launch next month. “If you want to run it on our cloud, you pay a fee,” Oberhauser said.

The second way it plans to make money is through consulting, support and integration services, which will take another year likely to launch (remember the startup is only five months old).

The third area for making money will be through licensing fees for larger users (a size which it has yet to determine) but even now the service as it stands “can be deployed to 1 million people” and still be free, Oberhauser said.

Right place, right time

Oberhauser, pictured here, said his startup came to the attention of Sequoia and London firm firstminute (the London VC co-founded by Brent Hoberman, Spencer Crawley and Henry Lane-Fox that specialises in early stage investments and counts VCs like Atomico as partners) through the responses that he got to his short post on HackerNews, and then subsequent hunt on Product Hunt.

n8n had been invited to Y Combinator to be a part of its cohort but declined because Oberhauser didn’t want to relocate from Berlin, where he has a young family to help support and where he intended to found the company (joining YC would have included incorporation in Delaware, which also didn’t interest Oberhauser). In fact, he built all of n8n bootstrapped as a side hustle while working part-time at other places, such was the need for income before this seed round.

That kind of grit, combined with identifying and fixing a clear gap in the market addressing what a defined audience (in this case, developers) needs, in a scalable way, with the proof being immediate interest and take-up from said target market, seemed to make the startup a no-brainer for funding.

“As talent is becoming more scarce, every organization is looking to get more from the great people they have,” Matthew Miller, a partner at Sequoia who has also worked closely with Docker, Confluent, Tessian, and Graphcore, said in a statement. “This is driving a surge in automation solutions in every industry. We were impressed by n8n’s early adoption in the open source community and Jan’s vision to build an open and flexible solution in this space, and we’re thrilled to have n8n as our first seed investment in Germany.”

Although Sequoia has yet to set up a full-fledged outpost here, sources have told us (and there have been reports) that this is intention, with the timeline being to set it up later this year. This is with the caveat of recent events related to the Novel Coronavirus pandemic, which have included a huge drop in the stock market and a major reassessment of business activities, which could materially change that course.

But more generally, having Sequoia — which has been involved some of the most high-profile startup exits of recent years, perhaps most famously Facebook’s $19 billion acquisition of WhatsApp — operating a bigger office in Europe would represent a big vote of confidence in the region. European VC firm Atomico projected in November 2019 that there would be $35 billion of investment this year in European technology, a high water mark for the region. That represents an opportunity both in terms simply more startups but also later rounds for the biggest of these, both areas where Sequoia would want to be more active, is my guess.

Although Sequoia hasn’t announced any Europe-specific fund yet, the firm seems to currently have no shortage in raising money. It was reported last month that the VC is currently raising a fresh $1.3 billion, earmarked for Asia. And as recently as late December, it filed papers to raise $1 billion for US growth rounds and $2.4 billion for China.

Without committing (‘at this time’) to any region-specific funds, Sequoia is getting increasingly active in Europe anyway.

Even before hiring Lixandru (a hire it had been working on since last year, we understand), the firm had been making later-stage investments in Europe for years, including investments in Skyscanner (acquired by Ctrip), Wunderlist (acquired by Microsoft) and more recently Tessian.

This latest funding in n8n signals how now it is diversifying into a wider set of investment opportunities. These include not just earlier rounds like this first seed investment in Germany. But also newer technologies: for example, as part of the investor group putting $12 million into cryptocurrency wallet Argent earlier this week.

From long payment cycles to antiquated processes on how to bill workers, the hefty inefficiencies of the construction industry are long overdue for innovation. 

Enter startups such as the large venture-backed Katerra and recently public companies such as Procore. Still, independent contractors or workers from small family businesses often can’t afford hefty fees from SaaS platforms promising better management. Or, they don’t have a parent company behind them to foot the bill. 

To help the Bob’s Plumbings and Nicky Roofings of the world get paid on time, Handle.com has raised $4.5 million in known venture capital funding and $20 million in debt financing. The startup was a YC grad, born from a trio of founders: Blake Robertson, Chris Woodard and Patrick Hogan.

The startup uses a mix of software and a financing line to help construction workers get paid on time, a weakness in the current industry, per co-founder Hogan. 

“Construction is one of the largest operations in the country in terms of amount spent,” co-founder Hogan said. “We have a contractor that we work with, that if he does a job for Hilton Hotels and has a $200,000 invoice, it takes over one year for them to pay him back. The impact on his business is substantial.” 

In the construction industry, workers often have to submit their own billing, which is lengthy, and there’s room for error. Using software, the startup helps workers automate invoices to limit mistakes, and get documentation to clients on time. 

In a legacy industry, oftentimes it’s hard to get both parties to adopt. So that’s why Handle.com made it so only the workers need to use the platform. Along with small businesses, it also helps larger contractors handle massive influxes of invoices. 

“It’s not a two-way street: it only requires the party who is going to be receiving the payment to use it,” Hogan said. “If you have to get two parties to agree to use a solution, it’s very difficult, because you have a two-sided marketplace type of problem. In construction, one party has more leverage than the other party. You may have reasons for one party to not have things more efficient.”

Now on to Handle.com’s financing side of its business. As every startup ever becomes a bank, Handle.com differs from the group in that it had a software fintech mix since launching out of YC. And in this case, Handle.com secured $20 million in debt equity so credit financing could be part of its business model. 

Handle.com uses a credit line to become a lender to construction workers who are waiting for a check to process and need capital before they can head to their next project. The startup claims that construction workers traditionally have a hard time securing capital loans from banks. “Contractors and subcontractors, Woodard said, “don’t have access [to capital], and it’s the ceiling on their business because they can only grow as fast as they’re getting paid back.”

The startup says that of the customers that use its software, “a growing portion” use the financing option too. 

As for growth, when Handle.com left YC it was six weeks in and collected $22,800 in monthly revenue. The startup declined to share revenue and growth statistics on the cuff of this funding round, beyond that it has been increasing its customer base by “an average of 30% month over month over the past year.”  

In case you missed it, you can listen to the recording here: 476th 1Mby1M Roundtable March 12, 2020: With Matt Carbonara, Citi Ventures

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Sramana Mitra: Are your clients mostly in France or are they elsewhere as well? Grégoire Chauvin: Half of our clients would be in the US and half in Western Europe.  Sramana Mitra: Can you talk a bit...

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Entrepreneurs are invited to the 477th FREE online 1Mby1M mentoring roundtable on Thursday, March 19, 2020, at 8 a.m. PDT/11 a.m. EDT/4 p.m. CET/8:30 p.m. India IST. If you are a serious...

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CEO Taylor Tyng has bootstrapped Wiredrive over a 17-year period to about $10 million. Today, he has options ahead to grow organically or raise money. Either way, an interesting journey. Sramana...

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In Asia, where I work as a partner at an early-stage VC firm, startups are regularly rolling out a minimum viable product (MVP) and then transacting on messaging apps.

Companies like shoe brand Portblue, AI e-commerce company Sorabel and Sama, an online recruitment platform for migrant workers, all started life using WhatsApp and Facebook Messenger to communicate with customers, onboard users and raise brand awareness.

For many years, WeChat has been the default app for daily life and business in China. It’s estimated that more than 30% of all internet traffic in China is through WeChat, and in 2017 they introduced “mini-programs,” where businesses could build apps inside WeChat. Now you never have to download any apps or go to a browser to access millions of services and businesses in WeChat.

We now see a similar trend in Southeast Asia. Here, WhatsApp is the dominant social platform and, while it has not built the same infrastructure for building apps, startups have found a way around that and now run many services on top of WhatsApp, validating with customers quickly and cheaply. These companies are not only mobile-first, but they are also WhatsApp-first.

Sampingan, an Antler portfolio company founded here in Singapore, provides an on-demand workforce to businesses in Indonesia. The first version of the product was on WhatsApp. The team sourced and managed more than 2,000 blue-collar workers in Indonesia who completed 25,000 jobs in the company’s first three months.

Corona Virus looms over everything we do right now. We had extensive discussion on the subject today. My primary strategy for all our entrepreneurs is that biz dev should go on. Do it online. Do it...

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According to a BlueWeaveConsulting report, the global payroll and HR solutions and services market is expected to grow at 9.5% CAGR to $43 billion by 2026 driven by the continued adoption of...

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Hello and welcome back to Equity, TechCrunch’s venture capital-focused podcast, where we unpack the numbers behind the headlines.

Today was something a bit special. We’d originally hoped to have this episode in person, as a group, but the world isn’t flying as much right now so we had to make do. Regardless, please say hello and welcome Natasha Mascarenhas to the Equity crew.

Natasha has worked for the Boston Globe, the SF Chronicle and, most recently, covering venture capital for Crunchbase News. TechCrunch is lucky to have her, and the Equity team is stoked that she’s coming aboard our hosting team. When she’s not podcasting, she will be reporting on early-stage startups and venture capital trends for TechCrunch and Extra Crunch.

Don’t worry, Danny and Alex aren’t going anywhere. Equity is now, happily, back to its original three-part hosting crew. This means we can do a better job week in, and week out.

Alright! Enough of all that, let’s talk news. Here’s what we went over today:

Equity has been busy lately. We put together a huge interview with Jason Lemkin, and held a live chat this week. We’re tinkering with new things as we try to do more, and better for you all. Chat you all Monday morning!

Equity drops every Monday at 7:00 AM PT and Friday at 6:00 am PT, so subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts.

Sramana Mitra: How did you acquire this very fragmented customer base? Stefan Batory: Initially, we weren’t been thinking about that because we were supposed to work with that partner from southeast...

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