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YC-backed Mutiny helps B2B business personalize their website for each visitor
Mutiny, which is part of the current batch of startups at accelerator Y Combinator, helps business-to-business, software-as-a-service companies present a message that’s customized to each visitor on their website.
Co-founder and CEO Jaleh Rezaei said this concept is alive and well in the analog world: When she was at VMware, sales reps were given materials to help them tailor their pitch for each prospective customer. Then, when she was one of the early employees at HR services startup Gusto, she tried to do something similar online, only to find that existing software wasn’t quite up to the task.
There are landing page optimization tools, but Rezaei asked, “Who wants to create a thousand versions of your website?” And there are A/B testing tools, but Rezaei argued that they’re really designed to test “generic content” and use “very little audience intelligence.” And as for creating your own personalization tools, many companies will find that it requires “way too much engineering effort.”
That’s where Mutiny comes in. It integrates with existing data sources to allow businesses to divide their customers into segments. Then they can use Mutiny’s graphical interface to create personalized elements of the webpage for each segment.
For example, when you visit the homepage of Mutiny customer Amplitude, things like the customer testimonials and the call to action will change depending on the size of your company. Or when Brex customers click through from an email marketing campaign, they’ll see a credit card offer tailored to their name and company.
These kinds of changes might not seem all that significant, but Rezaei said that when someone visits a B2B website, they’re probably interested in the product or service already. If they’re not converting, it’s probably because “they didn’t find what they wanted right away.” Mutiny can help surface the right content or the right message for the right customer.
The startup will also compare the personalized results to the generic webpage to help determine what does and doesn’t improve the bottom line. Rezaei said some of Mutiny’s early customers (who include Gusto, Infusionsoft and Brex) have seen conversion rates improve by 20 to 180 percent.
“That’s not to say that every test performs better, but the nice thing here is that you immediately see how something is performing,” she added.
Eventually, Rezaei is hoping to expand Mutiny’s technology so that it can personalize every aspect of the B2B purchase experience, including email and ad retargeting.
“Our passion as a founding team is growth,” she said. “Progress occurs not when you just build something, but when that product makes it into the hands of the person for whom it was intended to help.”
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YC-backed Sterblue aims to enable smarter drone inspections
As government regulation for commercial drone usage seems to be trending in a very positive direction for the companies involved, there is an ever-growing opportunity for drone startups to utilize artificial intelligence to deliver insights without requiring much human effort.
Sterblue, a French drone software startup that is launching out of Y Combinator’s latest class of companies, is aiming to get off-the-shelf drones inspecting large outdoor structures up close with automated insights that identify anomalies that need a second look.
The startup’s software is specifically focused on enabling drones to easily inspect large power lines or wind turbines with simple automated trajectories that can get a job done much quicker and with less room for human error. The software also allows the drones to get much closer to the large structures they are scanning so the scanned images are as high-quality as possible.
Compared to navigating a tight urban environment, Sterblue has the benefit of there being very few airborne anomalies around these structures, so autonomously flying along certain flight paths is as easy as having a CAD structure available and enough wiggle room to correct for things like wind condition.
Operators basically just have to connect their drones to the Sterblue cloud platform where they can upload photos and view 3D models of the structures they have scanned while letting the startup’s neural net identify any issues that need further attention. All and all, Sterblue says their software can let drones get within three meters of power lines and wind turbines, which allows their AI systems to easily detect anomalies from the photos being taken. Sterblue says their system can detect defects as small as one millimeter in size.
The startup was initially working on their own custom drone hardware but decided that their efforts were best spent supporting off-the-shelf devices from companies like DJI, with their software solution sitting on top. The founding team is composed of former Airbus employees that are focusing early efforts on utility companies, with some of the first customers based in Europe, Africa and Asia.
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Incentivai launches to simulate how hackers break blockchains
Cryptocurrency projects can crash and burn if developers don’t predict how humans will abuse their blockchains. Once a decentralized digital economy is released into the wild and the coins start to fly, it’s tough to implement fixes to the smart contracts that govern them. That’s why Incentivai is coming out of stealth today with its artificial intelligence simulations that test not just for security holes, but for how greedy or illogical humans can crater a blockchain community. Crypto developers can use Incentivai’s service to fix their systems before they go live.
“There are many ways to check the code of a smart contract, but there’s no way to make sure the economy you’ve created works as expected,” says Incentivai’s solo founder Piotr Grudzień. “I came up with the idea to build a simulation with machine learning agents that behave like humans so you can look into the future and see what your system is likely to behave like.”
Incentivai will graduate from Y Combinator next week and already has a few customers. They can either pay Incentivai to audit their project and produce a report, or they can host the AI simulation tool like a software-as-a-service. The first deployments of blockchains it’s checked will go out in a few months, and the startup has released some case studies to prove its worth.
“People do theoretical work or logic to prove that under certain conditions, this is the optimal strategy for the user. But users are not rational. There’s lots of unpredictable behavior that’s difficult to model,” Grudzień explains. Incentivai explores those illogical trading strategies so developers don’t have to tear out their hair trying to imagine them.
Protecting crypto from the human x-factor
There’s no rewind button in the blockchain world. The immutable and irreversible qualities of this decentralized technology prevent inventors from meddling with it once in use, for better or worse. If developers don’t foresee how users could make false claims and bribe others to approve them, or take other actions to screw over the system, they might not be able to thwart the attack. But given the right open-ended incentives (hence the startup’s name), AI agents will try everything they can to earn the most money, exposing the conceptual flaws in the project’s architecture.
“The strategy is the same as what DeepMind does with AlphaGo, testing different strategies,” Grudzień explains. He developed his AI chops earning a masters at Cambridge before working on natural language processing research for Microsoft.
Here’s how Incentivai works. First a developer writes the smart contracts they want to test for a product like selling insurance on the blockchain. Incentivai tells its AI agents what to optimize for and lays out all the possible actions they could take. The agents can have different identities, like a hacker trying to grab as much money as they can, a faker filing false claims or a speculator that cares about maximizing coin price while ignoring its functionality.
Incentivai then tweaks these agents to make them more or less risk averse, or care more or less about whether they disrupt the blockchain system in its totality. The startup monitors the agents and pulls out insights about how to change the system.
For example, Incentivai might learn that uneven token distribution leads to pump and dump schemes, so the developer should more evenly divide tokens and give fewer to early users. Or it might find that an insurance product where users vote on what claims should be approved needs to increase its bond price that voters pay for verifying a false claim so that it’s not profitable for voters to take bribes from fraudsters.
Grudzień has done some predictions about his own startup too. He thinks that if the use of decentralized apps rises, there will be a lot of startups trying to copy his approach to security services. He says there are already some doing token engineering audits, incentive design and consultancy, but he hasn’t seen anyone else with a functional simulation product that’s produced case studies. “As the industry matures, I think we’ll see more and more complex economic systems that need this.”
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Klarity uses AI to strip drudgery from contract review
Klarity, a member of the Y Combinator 2018 Summer class, wants to automate much of the contract review process by applying artificial intelligence, specifically natural language processing.
Company co-founder and CEO Andrew Antos has experienced the pain of contract reviews first hand. After graduating from Harvard Law, he landed a job spending 16 hours a day reviewing contract language, a process he called mind-numbing. He figured there had to be a way to put technology to bear on the problem and Klarity was born.
“A lot of companies are employing internal or external lawyers because their customers, vendors or suppliers are sending them a contract to sign,” Antos explained They have to get somebody to read it, understand it and figure out whether it’s something that they can sign or if it requires specific changes.
You may think that this kind of work would be difficult to automate, but Antos said that contracts have fairly standard language and most companies use ‘playbooks.’ “Think of the playbook as a checklist for NDAs, sales agreements and vendor agreements — what they are looking for and specific preferences on what they agree to or what needs to be changed,” Antos explained.
Klarity is a subscription cloud service that checks contracts in Microsoft Word documents using NLP. It makes suggestions when it sees something that doesn’t match up with the playbook checklist. The product then generates a document, and a human lawyer reviews and signs off on the suggested changes, reducing the review time from an hour or more to 10 or 15 minutes.
Screenshot: Klarity
They launched the first iteration of the product last year and have 14 companies using it with 4 paying customers so far including one of the world’s largest private equity funds. These companies signed on because they have to process huge numbers of contracts. Klarity is helping them save time and money, while applying their preferences in a consistent fashion, something that a human reviewer can have trouble doing.
He acknowledges the solution could be taking away work from human lawyers, something they think about quite a bit. Ultimately though, they believe that contract reviewing is so tedious, it is freeing up lawyers for work that requires a greater level of intellectual rigor and creativity.
Antos met his co-founder and CTO, Nischal Nadhamuni, at an MIT entrepreneurship class in 2016 and the two became fast friends. In fact, he says that they pretty much decided to start a company the first day. “We spent 3 hours walking around Cambridge and decided to work together to solve this real problem people are having.”
They applied to Y Combinator two other times before being accepted in this summer’s cohort. The third time was the charm. He says the primary value of being in YC is the community and friendships they have formed and the help they have had in refining their approach.
“It’s like having a constant mirror that helps you realize any mistakes or any suboptimal things in your business on a high speed basis,” he said.
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DoorDash raises another $250M, nearly triples valuation to $4B
Food delivery startup DoorDash announced this afternoon that it has raised $250 million, just five months since the company announced a $535 million round.
Why raise more money so soon? CEO Tony Xu told Axios that he wasn’t actively looking for additional investment, but was open to investor interest because it could help the company expand more quickly. (Maybe he’ll have more to say about those plans at Disrupt SF next month.)
The new funding was led by Coatue Management and DST Global. It sounds like the terms were pretty appealing too, with the valuation growing from $1.4 billion to $4 billion.
In a blog post, the company said it’s had a good 2018, with deliveries increasing 250 percent year-over-year, restaurant chains like Chipotle and IHOP signing up and last week’s launch of the DashPass subscription service, where you can pay $9.99 per month to get unlimited free deliveries.
“As we grow, we will stay true to our values and our mission of connecting people with possibility — and, trust us, we’re just getting started,” DoorDash wrote.
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Autonomous retail startup Inokyo’s first store feels like stealing
Inokyo wants to be the indie Amazon Go. It’s just launched its prototype cashierless autonomous retail store. Cameras track what you grab from shelves, and with a single QR scan of its app on your way in and out of the store, you’re charged for what you got.
Inokyo‘s first store is now open on Mountain View’s Castro Street selling an array of bougie kombuchas, snacks, protein powders and bath products. It’s sparse and a bit confusing, but offers a glimpse of what might be a commonplace shopping experience five years from now. You can get a glimpse yourself in our demo video below:
“Cashierless stores will have the same level of impact on retail as self-driving cars will have on transportation,” Inokyo co-founder Tony Francis tells me. “This is the future of retail. It’s inevitable that stores will become increasingly autonomous.”
Inokyo (rhymes with Tokyo) is now accepting signups for beta customers who want early access to its Mountain View store. The goal is to collect enough data to dictate the future product array and business model. Inokyo is deciding whether it wants to sell its technology as a service to other retail stores, run its own stores or work with brands to improve their product’s positioning based on in-store sensor data on custom behavior.
“We knew that building this technology in a lab somewhere wouldn’t yield a successful product,” says Francis. “Our hypothesis here is that whoever ships first, learns in the real world and iterates the fastest on this technology will be the ones to make these stores ubiquitous.” Inokyo might never rise into a retail giant ready to compete with Amazon and Whole Foods. But its tech could even the playing field, equipping smaller businesses with the tools to keep tech giants from having a monopoly on autonomous shopping experiences.
It’s about what cashiers do instead
“Amazon isn’t as ahead as we assumed,” Francis remarks. He and his co-founder Rameez Remsudeen took a trip to Seattle to see the Amazon Go store that first traded cashiers for cameras in the U.S. Still, they realized, “This experience can be magical.” The two met at Carnegie Mellon through machine learning classes before they went on to apply that knowledge at Instagram and Uber. The two decided that if they jumped into autonomous retail soon enough, they could still have a say in shaping its direction.
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ext week, Inokyo will graduate from Y Combinator’s accelerator that provided its initial seed funding. In six weeks during the program, they found a retail space on Mountain View’s main drag, studied customer behaviors in traditional stores, built an initial product line and developed the technology to track what users are taking off the shelves.
Here’s how the Inokyo store works. You download its app and connect a payment method, and you get a QR code that you wave in front of a little sensor as you stroll into the shop. Overhead cameras will scan your body shape and clothing without facial recognition in order to track you as you move around the store. Meanwhile, on-shelf cameras track when products are picked up or put back. Combined, knowing who’s where and what’s grabbed lets it assign the items to your cart. You scan again on your way out, and later you get a receipt detailing the charges.
Originally, Inokyo actually didn’t make you scan on the way out, but it got the feedback that customers were scared they were actually stealing. The scan-out is more about peace of mind than engineering necessity. There is a subversive pleasure to feeling like, “well, if Inokyo didn’t catch all the stuff I chose, that’s not my problem.” And if you’re overcharged, there’s an in-app support button for getting a refund.
Inokyo co-founders (from left): Tony Francis and Rameez Remsudeen
Inokyo was accurate in what it charged me despite me doing a few switcharoos with products I nabbed. But there were only about three people in the room at the time. The real test for these kinds of systems are when a rush of customers floods in and cameras have to differentiate between multiple similar-looking people. Inokyo will likely need to be more than 99 percent accurate to be more of a help than a headache. An autonomous store that constantly over- or undercharges would be more trouble than it’s worth, and patrons would just go to the nearest classic shop.
Just because autonomous retail stores will be cashier-less doesn’t mean they’ll have no staff. To maximize cost-cutting, they could just trust that people won’t loot it. However, Inokyo plans to have someone minding the shop to make sure people scan in the first place and to answer questions about the process. But there’s also an opportunity in reassigning labor from being cashiers to concierges that can recommend the best products or find what’s the right fit for the customer. These stores will be judged by the convenience of the holistic experience, not just the tech. At the very least, a single employee might be able to handle restocking, customer support and store maintenance once freed from cashier duties.
The Amazon Go autonomous retail store in Seattle is equipped with tons of overhead cameras
While Amazon Go uses cameras in a similar way to Inokyo, it also relies on weight sensors to track items. There are plenty of other companies chasing the cashierless dream. China’s BingoBox has nearly $100 million in funding and has more than 300 stores, though they use less sophisticated RFID tags. Fellow Y Combinator startup Standard Cognition has raised $5 million to equip old-school stores with autonomous camera-tech. AiFi does the same, but touts that its cameras can detect abnormal behavior that might signal someone is a shoplifter.
The store of the future seems like more and more of a sure thing. The race’s winner will be determined by who builds the most accurate tracking software, easy-to-install hardware and pleasant overall shopping flow. If this modular technology can cut costs and lines without alienating customers, we could see our local brick-and-mortars adapt quickly. The bigger question than if or even when this future arrives is what it will mean for the millions of workers who make their living running the checkout lane.
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Shelf Engine uses machine learning to stop food waste from eating into store margins
Shelf Engine’s team
While running Molly’s, the Seattle-based ready meal wholesaler he founded, Stefan Kalb was upset about its 28 percent food wastage rate. Feeling that the amount was “astronomical,” he began researching how to lower it — and was shocked to discovered Molly’s was actually outperforming the industry average. Confronted by the sheer amount of food wasted by American retailers, Kalb and Bede Jordan, then a Microsoft engineer, began working on an order prediction engine.
The project quickly brought Molly’s percentage of wasted food down to the mid-teens. “It was one of the most fulfilling things I’ve ever done in my career,” Kalb told TechCrunch in an interview. Driven by its success, Kalb and Jordan launched Shelf Engine in 2016 to make the technology available to other companies. Currently participating in Y Combinator, the startup has already raised $800,000 in seed funding from Initialized Capital, the venture capital firm founded by Alexis Ohanian and Gerry Tan, and is now used at more than 180 retail points by clients including WeWork, Bartell Drugs, Natural Grocers and StockBox.
Shelf Engine’s order prediction engine analyzes historical order and sales data and makes recommendations about how much retailers should order to minimize waste and increase margins. The more retailers use Shelf Engine, the more accurate its machine learning model becomes. The system also helps suppliers, because many operate on guaranteed sales, or scan-based trading, which means they agree to take back and refund the purchase price of any products that don’t sell by their expiration date. While running Molly’s, Kalb learned what a huge pain point this is for suppliers. To alleviate that, Shelf Engine itself buys back unsold inventory from the retailers it works with, taking the risk away from their suppliers.
Kalb, Shelf Engine’s CEO, claims the startup’s customers are able to increase their gross margins by 25 percent and reduce food waste from an industry average of 30 percent to about 16-18 percent for items that expire within one to five days. (For items with a shelf life of up to 45 days, the longest that Shelf Engine manages, it can reduce waste to as little as 3-4 percent).
The food industry operates on notoriously tight margins, and Shelf Engine wants to relieve some of the pressure. Running Molly’s, which supplies corporate campuses, including Microsoft, Boeing and Amazon, gave Kalb a firsthand look at the paradox faced by retail managers. Even though a lot of food is wasted, items are also frequently out of stock at stores, annoying customers. Then there is the social and environmental impact of food waste — not only does it raise prices, food rotting in landfills is a major contributor to methane emissions.
A store manager may need to make ordering decisions about thousands of products, leaving little time for analysis. Though there are enterprise resource planning software products for food retail, Kalb says that during store visits he realized a surprisingly high number still rely on Excel spreadsheets or pen and paper to manage reoccurring orders. The process is also highly subjective, with managers ordering products based on their personal preferences, a customer’s suggestion or what they’ve noticed does well at other stores. Sometimes retailers get stuck in a cycle of overcorrecting, because if customers complain about missing out on something, managers order more inventory, only to end up with wastage, then scaling back their next order and so on.
“Americans want selection at all times, we get furious when a product is sold out, but it’s a really hard decision to make about how much challah bread to stock on a Monday,” says Kalb. “Yet we are doing that ad hoc.”
When retailers use Shelf Engine’s prediction engine, it decides how many units they need and then submits those orders to their suppliers. After products reach their sell-by dates, the retailer reports back to Shelf Engine, which only charges them for units they sold, but still pays suppliers for the full order. As time passes, Shelf Engine can make more granular predictions (for example, how precipitation correlates with the sale of specific items like juice or bread).
In addition to providing the impetus for the creation of Shelf Engine, Molly’s also helped Kalb and Jordan, its CTO, build the startup’s distribution network. Kalb says Shelf Engine has benefited from the network effect, because when a retailer signs up, their suppliers will often mention it to other retailers that they serve. Kalb says the startup is currently hiring more engineers and salespeople to help Shelf Engine leverage that and spread through the food retail industry.
“It’s a world I got to know and I came into the world fascinated with healthy food and making delicious grab-and-go meals,” says Kalb. “It turned into a fascination with this crazy market, which is so massive and still has so many opportunities to be maximized.”
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Coinbase acquires Distributed Systems to build ‘Login with Coinbase’
Coinbase wants to be Facebook Connect for crypto. The blockchain giant plans to develop “Login with Coinbase” or a similar identity platform for decentralized app developers to make it much easier for users to sign up and connect their crypto wallets. To fuel that platform, today Coinbase announced it has acquired Distributed Systems, a startup founded in 2015 that was building an identity standard for dApps called the Clear Protocol.
The five-person Distributed Systems team and its technology will join Coinbase. Three of the team members will work with Coinbase’s Toshi decentralized mobile browser team, while CEO Nikhil Srinivasan and his co-founder Alex Kern are forming the new decentralized identity team that will work on the Login with Coinbase product. They’ll be building it atop the “know your customer” anti-money laundering data Coinbase has on its 20 million customers. Srinivasan tells me the goal is to figure out “How can we allow that really rich identity data to enable a new class of applications?”
Distributed Systems had raised a $1.7 million seed round last year led by Floodgate and was considering raising a $4 million to $8 million round this summer. But Srinivasan says, “No one really understood what we’re building,” and it wanted a partner with KYC data. It began talking to Coinbase Ventures about an investment, but after they saw Distributed Systems’ progress and vision, “they quickly tried to move to find a way to acquire us.”
Distributed Systems began to hold acquisition talks with multiple major players in the blockchain space, and the CEO tells me it was deciding between going to “Facebook, or Robinhood, or Binance, or Coinbase,” having been in formal talks with at least one of the first three. Of Coinbase the CEO said, they “were able to convince us they were making big bets, weaving identity across their products.” The financial terms of the deal weren’t disclosed.
Coinbase’s plan to roll out the Login with Coinbase-style platform is an SDK that others apps could integrate, though that won’t necessarily be the feature’s name. That mimics the way Facebook colonized the web with its SDK and login buttons that splashed its brand in front of tons of new and existing users. This turned Facebook into a fundamental identity utility beyond its social network.
Developers eager to improve conversions on their signup flow could turn to Coinbase instead of requiring users to set up whole new accounts and deal with crypto-specific headaches of complicated keys and procedures for connecting their wallet to make payments. One prominent dApp developer told me yesterday that forcing users to set up the MetaMask browser extension for identity was the part of their signup flow where they’re losing the most people.
This morning Coinbase CEO Brian Armstrong confirmed these plans to work on an identity SDK. When Coinbase investor Garry Tan of Initialized Capital wrote that “The main issue preventing dApp adoption is lack of native SDK so you can just download a mobile app and a clean fiat to crypto in one clean UX. Still have to download a browser plugin and transfer Eth to Metamask for now Too much friction,” Armstrong replied “On it :)”
On it 🙂
— Brian Armstrong (@brian_armstrong) August 15, 2018
In effect, Coinbase and Distributed Systems could build a safer version of identity than we get offline. As soon as you give your Social Security number to someone or it gets stolen, it can be used anywhere without your consent, and that leads to identity theft. Coinbase wants to build a vision of identity where you can connect to decentralized apps while retaining control. “Decentralized identity will let you prove that you own an identity, or that you have a relationship with the Social Security Administration, without making a copy of that identity,” writes Coinbase’s PM for identity B. Byrne, who’ll oversee Srinivasan’s new decentralized identity team. “If you stretch your imagination a little further, you can imagine this applying to your photos, social media posts, and maybe one day your passport too.”
Considering Distributed Systems and Coinbase are following the Facebook playbook, they may soon have competition from the social network. It’s spun up its own blockchain team and an identity and single sign-on platform for dApps is one of the products I think Facebook is most likely to build. But given Coinbase’s strong reputation in the blockchain industry and its massive head start in terms of registered crypto users, today’s acquisition well position it to be how we connect our offline identity with the rising decentralized economy.
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Twistlock snares $33 million Series C investment to secure cloud native environments
As the world shifts to a cloud native approach, the way you secure applications as they get deployed is changing too. Twistlock, a company built from the ground up to secure cloud native environments, announced a $33 million Series C round today led by Iconiq Capital.
Previous investors YL Ventures, TenEleven, Rally Ventures, Polaris Partners and Dell Technologies Capital also participated in the round. The company reports it has received a total of $63 million in venture investment to date.
Twistlock is solving a hard problem around securing containers and serverless, which are by their nature ephemeral. They can live for fractions of seconds making it hard track problems when they happen. According to company CEO and co-founder Ben Bernstein, his company came out of the gate building a security product designed to protect a cloud-native environment with the understanding that while containers and serverless computing may be ephemeral, they are still exploitable.
“It’s not about how long they live, but about the fact that the way they live is more predictable than a traditional computer, which could be running for a very long time and might have humans actually using it,” Bernstein said.
Screenshot: Twistlock
As companies move to a cloud native environment using Dockerized containers and managing them with Kubernetes and other tools, they create a highly automated system to deal with the deployment volume. While automation simplifies deployment, it can also leave companies vulnerable to host of issues. For example, if a malicious actor were to get control of the process via a code injection attack, they could cause a lot of problems without anyone knowing about it.
Twistlock is built to help prevent that, while also helping customers recognize when an exploit happens and performing forensic analysis to figure out how it happened.
It’s not a traditional Software as a Service as we’ve come to think of it. Instead, it is a service that gets installed on whatever public or private cloud that the customer is using. So far, they count just over 200 customers including Walgreens and Aetna and a slew of other companies you would definitely recognize, but they couldn’t name publicly.
The company, which was founded in 2015, is based in Portland, Oregon with their R&D arm in Israel. They currently have 80 employees. Bernstein said from a competitive standpoint, the traditional security vendors are having trouble reacting to cloud native, and while he sees some startups working at it, he believes his company has the most mature offering, at least for now.
“We don’t have a lot of competition right now, but as we start progressing we will see more,” he said. He plans to use the money they receive today to help expand their marketing and sales arm to continue growing their customer base, but also engineering to stay ahead of that competition as the cloud-native security market continues to develop.
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To fight the scourge of open offices, ROOM sells rooms
Noisy open offices don’t foster collaboration, they kill it, according to a Harvard study that found the less-private floor plan led to a 73 percent drop in face-to-face interaction between employees and a rise in emailing. The problem is plenty of young companies and big corporations have already bought into the open office fad. But a new startup called ROOM is building a prefabricated, self-assembled solution. It’s the IKEA of office phone booths.
The $3,495 ROOM One is a sound-proofed, ventilated, powered booth that can be built in new or existing offices to give employees a place to take a video call or get some uninterrupted time to focus on work. For comparison, ROOM co-founder Morten Meisner-Jensen says, “Most phone booths are $8,000 to $12,000. The cheapest competitor to us is $6,000 — almost twice as much.” Though booths start at $4,500 from TalkBox and $3,995 from Zenbooth, they tack on $1,250 and $1,650 for shipping, while ROOM ships for free. They’re all dividing the market of dividing offices.
The idea might seem simple, but the booths could save businesses a ton of money on lost productivity, recruitment and retention if it keeps employees from going crazy amidst sales call cacophony. Less than a year after launch, ROOM has hit a $10 million revenue run rate thanks to 200 clients ranging from startups to Salesforce, Nike, NASA and JP Morgan. That’s attracted a $2 million seed round from Slow Ventures that adds to angel funding from Flexport CEO Ryan Petersen. “I am really excited about it since it is probably the largest revenue-generating company Slow has seen at the time of our initial Seed stage investment,” says partner Kevin Colleran.
“It’s not called ROOM because we build rooms,” Meisner-Jensen tells me. “It’s called ROOM because we want to make room for people, make room for privacy and make room for a better work environment.”
Phone booths, not sweatboxes
You might be asking yourself, enterprising reader, why you couldn’t just go to Home Depot, buy some supplies and build your own in-office phone booth for way less than $3,500. Well, ROOM’s co-founders tried that. The result was… moist.
Meisner-Jensen has design experience from the Danish digital agency Revolt that he started before co-founding digital book service Mofibo and selling it to Storytel. “In my old job we had to go outside and take the call, and I’m from Copenhagen, so that’s a pretty cold experience half the year.” His co-founder Brian Chen started Y Combinator-backed smart suitcase company Bluesmart, where he was VP of operations. They figured they could attack the office layout issue with hammers and saws. I mean, they do look like superhero alter-egos.
Room co-founders (from left): Brian Chen and Morten Meisner-Jensen
“To combat the issues I myself would personally encounter with open offices, as well as colleagues, we tried to build a private ‘phone booth’ ourselves,” says Meisner-Jensen. “We didn’t quite understand the specifics of air ventilation or acoustics at the time, so the booth got quite warm — warm enough that we coined it ‘the sweatbox.’ ”
With ROOM, they got serious about the product. The 10-square-foot ROOM One booth ships flat and can be assembled in less than 30 minutes by two people with a hex wrench. All it needs is an outlet to power its light and ventilation fan. Each is built from 1088 recycled plastic bottles for noise cancelling, so you’re not supposed to hear anything from outside. The box is 100 percent recyclable, plus it can be torn down and rebuilt if your startup implodes and you’re being evicted from your office.
The ROOM One features a bar-height desk with outlets and a magnetic bulletin board behind it, though you’ll have to provide your own stool. It’s actually designed not to be so comfy that you end up napping inside, which doesn’t seem like it’d be a problem with this somewhat cramped spot. “To solve the problem with noise at scale you want to provide people with space to take a call but not camp out all day,” Meisner-Jensen notes.
Booths by Zenbooth, Cubicall and TalkBox (from left)
A place to get into flow
Couldn’t office managers just buy noise-cancelling headphones for everyone? “It feels claustrophobic to me,” he laughs, but then outlines why a new workplace trend requires more than headphones. “People are doing video calls and virtual meetings much, much more. You can’t have all these people walking by you and looking at your screen. [A booth is] also giving you your own space to do your own work, which I don’t think you’d get from a pair of Bose. I think it has to be a physical space.”
But with plenty of companies able to construct physical spaces, it will be a challenge for ROOM to convey the subtleties of its build quality that warrant its price. “The biggest risk for ROOM right now are copycats,” Meisner-Jensen admits. “Someone entering our space claiming to do what we’re doing better but cheaper.” Alternatively, ROOM could lock in customers by offering a range of office furniture products. The co-founder hinted at future products, saying ROOM is already receiving demand for bigger multi-person prefab conference rooms and creative room divider solutions.
The importance of privacy goes beyond improved productivity when workers are alone. If they’re exhausted from overstimulation in a chaotic open office, they’ll have less energy for purposeful collaboration when the time comes. The bustle could also make them reluctant to socialize in off-hours, which could lead them to burn out and change jobs faster. Tech companies in particular are in a constant war for talent, and ROOM Ones could be perceived as a bigger perk than free snacks or a ping-pong table that only makes the office louder.
“I don’t think the solution is to go back to a world of cubicles and corner offices,” Meisner-Jensen concludes. It could take another decade for office architects to correct the overenthusiasm for open offices despite the research suggesting their harm. For now, ROOM’s co-founder is concentrating on “solving the issue of noise at scale” by asking, “How do we make the current workspaces work in the best way possible?”
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