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YC-backed Queenly launches a marketplace for formalwear

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Queenly, a marketplace for formalwear, launched into a world where its core product of dresses and gowns had a massive competitor, bigger and more elusive than Poshmark: quarantine.

The coronavirus pandemic has caused the fancy in-person events that one might attend, such as award shows, pageants, proms and weddings to be canceled to limit spread. But despite the fact that you might be rocking sweats over slacks, Queenly co-founders Trisha Bantigue and Kathy Zhou say that they had half a million in sales last year, and over 100,000 people visit their website everyday.

“So many women bought dresses to just dress up and feel normal at home, when everything else around the world was not,” Bantigue said. “It helped them feel grounded and stabilize themselves in this crazy chaotic pandemic environment.” The canceled events have also found new homes, such as Zoom weddings, Twitch pageants, socially distant proms and graduation car parades. The co-founder added that content creators on TikTok and YouTube have also bought Queenly dresses.

Pandemic growth added a surprising dimension to Queenly’s business, and the Bay Area startup is currently partaking in the Y Combinator winter cohort to navigate it. So far, it has raised $800,000 to date from investors including Mike Smith, former COO of Stitch Fix, Thuan Pham, former CTO of Uber, and Kelly Thompson, former COO of Samsclub.com and Walmart.com. The goal, the co-founders tell me, is to become the StockX for formalwear.

Queenly is a marketplace for buying and selling formal dresses, from wedding dresses to pageant gowns. The 50,000 dresses on the platform are either new or resale, and sellers get paid 80% of the price that the gowns go for.

Part of the company’s biggest sell, according to the co-founders, is its algorithm that matches buyers to dresses. Before Queenly, Zhou was a former software engineer at Pinterest who helped build content creation flows and the back end of the platform. She took the same focus that her and her Pinterest co-workers had on data-driven search and development and applied it to Queenly.

The search engine can go deeper than a normal dress search on Macy’s can, which might create options based on size, color and cut. In contrast, Queenly can help offer more diverse insights with a larger range of sizes, silhouette options and different shades of the same color.

Last week, a seller sold her wedding dress with a tag that says the dark mesh on the dress is for a darker skin tone. Queenly is beta-testing a feature that lets you search medium skin tone sheer options or dark skin tone sheer options. The team says that skin-tone filters are one of the important long-term goals of their search engine.

“These are just some things that we know because we’re women, and we know how to build this product for women,” Zhou said. “As opposed to if this was a male founder, they would not know that that would even be something that women would search for.”

Currently, there are over 50,000 dresses for sale on the Queenly platform, ranging from $70 to $4,000 and going up to size 32.

Image Credits: Queenly

With these search insights, Queenly says that it is able to sell dresses within two weeks, claiming that some users say that their same dresses spent five months on the Poshmark platform.

The diversity of dresses, from a price and range perspective, is one of the ways that Queenly stays competitive with large retail brands like Nordstrom.

“Buying and carrying inventory is very capital intensive for any startup,” Bantigue said. “As female minority founders it was hard for us to raise in the beginning.” As a result, the startup doesn’t keep a physical inventory of dresses, but instead relies on users to help get dresses from owner to buyer. If a dress is under $200, Queenly sends a prepaid shipping label to the seller to mail directly to the end buyer. If a dress is over $200, Queenly gets the dress sent directly to the company, does light dry cleaning and authentication, and then sends it right to the user.

Bringing the users into the transaction process adds a layer of risk because it depends on people to do things for the startup to be successful. The incentive here is that sellers make 80% of their sale price, and Queenly pockets the other 20%.

The startup’s biggest cost is shipping. To limit these costs, Queenly currently doesn’t accept or honor any returns, unless the dress upon arrival is not what was described in the sales post.

While this is a sensical business decision, it could be a hurdle for the startups’ clientele. Sizes are complicated and inconsistent, so the inability to return a dress might stifle a customer’s appetite to buy in the first place.

“We were actually worried about this before, but for two years now we [have not] had a complaint about sizing,” Bantigue said.

The co-founders say that many buyers are comfortable tailoring a dress post-purchase, and sellers are required to post pictures so expectations are set pre-purchase. There have been no cases of counterfeit brands to date, Bantigue said.

Queenly’s next plan is to bring on boutique stores and dress designers for Queenly partners, a program started to help small boutique businesses digitize their inventory through the Queenly platform.

“For years, the formalwear industry has been mostly offline, with only big name players being available online,” Bantigue said. “We want to change this.”

Lyron Foster is a Hawaii based African American Musician, Author, Actor, Blogger, Filmmaker, Philanthropist and Multinational Serial Tech Entrepreneur.

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MealMe raises $900,000 for its food search engine

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This morning MealMe.ai, a food search engine, announced that it has closed a $900,000 pre-seed round. Palm Drive Capital led the round, with participation from Slow Ventures and CP Ventures.

TechCrunch first became familiar with MealMe when it presented as part of the Techstars Atlanta demo day last October, mentioning it in a roundup of favorite startups from a group of the accelerator’s startup cohorts.

The company’s product allows users to search for food, or a restaurant. It then displays price points from various food-delivery apps for what the user wants to eat and have delivered. And, notably, MealMe allows for in-app checkout, regardless of the selected provider.

The service could boost pricing and delivery-speed transparency amongst the different apps that help folks eat, like DoorDash and Uber Eats. But Mealme didn’t start out looking to build a search engine. Instead it took a few changes in direction to get there.

From social network to search engine

MealMe is an example of a startup whose first idea proved only directionally correct. The company began life as a food-focused social network, co-founder Matthew Bouchner told TechCrunch. That iteration of the service allowed users to view posted food pictures, and then find ordering options for what they saw.

While still operating as a social network, MealMe applied to both Y Combinator and Techstars, but wasn’t accepted at either.

The startup discovered that some of its users were posting food pics simply to get the service to tell them which delivery services would be able to bring them what they wanted. From that learning the company focused on building a food search engine, allowing users to search for restaurants, and then vet various delivery options and prices. That iteration of the product got the company into Techstars Atlanta, eventually leading to the demo day that TechCrunch reviewed.

During its time in Techstars, the company adjusted its model to not merely link to DoorDash and others, but to handle checkout inside of its own application. This captures more gross merchandize value (GMV) inside of MealMe, Bouchner explained in an interview. The capability was rolled out in September of 2020.

Since then the company has seen rapid growth, which it measures at around 20% week-on-week. During TechCrunch’s interview with MealMe, the company said that it had reached a GMV run rate of more than $500,000, and was scaling toward the $1 million mark. In the intervening weeks the company passed the $1 million GMV run-rate threshold.

MealMe was slightly coy on its business model, but it appears to make margin between what it charges users for orders and the total revenue it passes along to food delivery apps.

TechCrunch was curious about platform risk at MealMe; could the company get away with offering price comparison and ordering across multiple third-party delivery services without raising the ire of the companies behind those apps? At the time of our interview, Bouchner said that his company had not seen pushback from the services it sends users to. His company’s goal is to grow quickly, become a useful revenue source for the DoorDashes of the world, and then reach out for some of formal agreement, he explained.

“We continue to be a powerful revenue generator and drive thousands of orders to food delivery services per week,” the co-founder said in a written statement. Certainly MealMe found investors more excited by its growth than concerned about Uber Eats or other apps cutting the startup off from their service.

What first caught my eye about MealMe was the realization of how much I would have used it in my early 20s. Perhaps the company can find enough users like my younger self to help it scale to sufficient size that it can go to the major food ordering companies and demand a cut, not merely avoid being cut off.

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Apple supplier Foxconn reaches tentative agreement to build Fisker’s next electric car

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Apple supplier Foxconn Technology Group has reached a tentative agreement with electric vehicle startup-turned-SPAC Fisker to develop and eventually manufacture an EV that will be sold in North America, Europe, China and India.

Fisker and Foxconn said Wednesday that a memorandum of understanding agreement has been signed. Discussions between the two companies will continue with the expectation that a formal partnership agreement will be reached during the second quarter of this year. 

Under the agreement, Foxconn will begin production in the fourth quarter of 2023 with a projected annual volume of more than 250,000 vehicles. The electric vehicle will carry the Fisker brand.

Foxconn Technology Group Chairman Young-way Liu touted the company’s vertically integrated global supply chain and accumulated engineering capabilities, noting that it gives the company two major advantages in the development and manufacturing of the key elements of an EV, which includes the electric motor, electric control module and battery.

That supply chain and ability to scale engineering quickly will be critical for Foxconn if it hopes to meet its production target.

“The collaboration between our firms means that it will only take 24 months to produce the next Fisker vehicle — from research and development to production, reducing half of the traditional time required to bring a new vehicle to market,” Young-way Liu said in a statement.

Fisker said production of the Ocean SUV — its first EV and one that is supposed to be built by contract manufacturer Magna — will begin in the fourth quarter of 2022. The company said it plans to unveil a production-intent prototype of the Ocean later this year.

This is not Foxconn’s first foray into electric vehicle manufacturing.

Foxconn announced in January 2020 that it had formed a joint venture with Fiat Chrysler Automobiles to build electric vehicles in China. Under that agreement, each party will own 50% of the venture to develop and manufacture electric vehicles and engage in an IOV, what Foxconn parent company Hon Hai calls the “internet of vehicles” business.

Last month, Foxconn and Chinese automaker Zhejiang Geely Holding Group agreed to form a joint venture focused on contract manufacturing for automakers, with a specific focus on electrification, connectivity and autonomous driving technology as well as vehicles designed for sharing.

The joint venture between Foxconn and Geely will provide consulting services on whole vehicles, parts, intelligent drive systems and other automotive ecosystem platforms to automakers as well as ridesharing companies. Geely said it will bring its experience in the automotive fields of design, engineering, R&D, intelligent manufacturing, supply chain management and quality control while Foxconn will bring its manufacturing and Information and Communication Technology (ICT) know-how.

 

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Select Star raises seed to automatically document datasets for data scientists

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Back when I was a wee lad with a very security-compromised MySQL installation, I used to answer every web request with multiple “SELECT *” database requests — give me all the data and I’ll figure out what to do with it myself.

Today in a modern, data-intensive org, “SELECT *” will kill you. With petabytes of information, tens of thousands of tables (on the small side!), and millions and perhaps billions of calls flung at the database server, data science teams can no longer just ask for all the data and start working with it immediately.

Big data has led to the rise of data warehouses and data lakes (and apparently data lake houses), infrastructure to make accessing data more robust and easy. There is still a cataloguing and discovery problem though — just because you have all of your data in one place doesn’t mean a data scientist knows what the data represents, who owns it, or what that data might affect in the myriad of web and corporate reporting apps built on top of it.

That’s where Select Star comes in. The startup, which was founded about a year ago in March 2020, is designed to automatically build out metadata within the context of a data warehouse. From there, it offers a full-text search that allows users to quickly find data as well as “heat map” signals in its search results which can quickly pinpoint which columns of a dataset are most used by applications within a company and have the most queries that reference them.

The product is SaaS, and it is designed to allow for quick onboarding by connecting to a customer’s data warehouse or business intelligence (BI) tool.

Select Star’s interface allows data scientists to understand what data they are looking at. Photo via Select Star.

Shinji Kim, the sole founder and CEO, explained that the tool is a solution to a problem she has seen directly in corporate data science teams. She formerly founded Concord Systems, a real-time data processing startup that was acquired by Akamai in 2016. “The part that I noticed is that we now have all the data and we have the ability to compute, but now the next challenge is to know what the data is and how to use it,” she explained.

She said that “tribal knowledge is starting to become more wasteful [in] time and pain in growing companies” and pointed out that large companies like Facebook, Airbnb, Uber, Lyft, Spotify and others have built out their own homebrewed data discovery tools. Her mission for Select Star is to allow any corporation to quickly tap into an easy-to-use platform to solve this problem.

The company raised a $2.5 million seed round led by Bowery Capital with participation from Background Capital and a number of prominent angels including Spencer Kimball, Scott Belsky, Nick Caldwell, Michael Li, Ryan Denehy and TLC Collective.

Data discovery tools have been around in some form for years, with popular companies like Alation having raised tens of millions of VC dollars over the years. Kim sees an opportunity to compete by offering a better onboarding experience and also automating large parts of the workflow that remain manual for many alternative data discovery tools. With many of these tools, “they don’t do the work of connecting and building the relationship,” between data she said, adding that “documentation is still important, but being able to automatically generate [metadata] allows data teams to get value right away.”

Select Star’s team, with CEO and founder Shinji Kim in top row, middle. Photo via Select Star.

In addition to just understanding data, Select Star can help data engineers begin to figure out how to change their databases without leading to cascading errors. The platform can identify how columns are used and how a change to one may affect other applications or even other datasets.

Select Star is coming out of private beta today. The company’s team currently has seven people, and Kim says they are focused on growing the team and making it even easier to onboard users by the end of the year.

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