Sep
29
2020
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Datasaur snags $3.9M investment to build intelligent machine learning labeling platform

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

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

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

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

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

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

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

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

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

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

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

Sep
16
2020
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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.

Narrator 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 single-handedly,” 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.)

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.

Image Credits: Narrator

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 that 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 build 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 1,000+ 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.”

Mar
18
2020
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Around is the new floating head video chat multitasking app

You have to actually get work done, not just video call all day, but apps like Zoom want to take over your screen. Remote workers who need to stay in touch while staying productive are forced to juggle tabs. Meanwhile, call participants often look and sound far away, dwarfed by their background and drowned in noise.

Today, Around launches its new video chat software that crops participants down to just circles that float on your screen so you have space for other apps. Designed for laptops, Around uses auto-zoom and noise cancelling to keep your face and voice in focus. Instead of crowding around one computer or piling into a big-screen conference room, up to 15 people can call from their own laptop without echo — even from right next to each other.

“Traditional videoconferencing tries to maximize visual presence. But too much presence gets in the way of your work,” says Around CEO Dominik Zane. “People want to make eye contact. They want to connect. But they also want to get stuff done. Around treats video as the means to an end, not the end in itself.”

Around becomes available today by request in invite-only beta for Mac, windows, Linux, and web. It’s been in private beta since last summer, but now users can sign up here for early access to Around. The freemium model means anyone can slide the app into their stack without paying at first.

After two years in stealth, Around’s 12-person distributed team reveals that it’s raised $5.2 million in seed funding over multiple rounds from Floodgate, Initialized Capital, Credo Ventures, AngelList’s Naval Ravikant, Product Hunt’s Ryan Hoover, Crashlytics’ Jeff Seibert, and angel Tommy Leep. The plan is to invest in talent and infrastructure to keep video calls snappy.

Not Just A Picturephone

Around CEO Dominik Zane

Around was born out of frustration with remote work collaboration. Zane and fellow Around co-founder Pavel Serbajlo had built mobile marketing company M.dot that was acquired by GoDaddy by using a fully distributed team. But they discovered that Zoom was “built around decades-old assumptions of what a video call should be” says Zane. “A Zoom video call is basically a telephone connected to a video camera. In terms of design, it’s not much different from the original Picturephone demoed at the 1964 World’s Fair.”

So together, they started Around as a video chat app that slips into the background rather than dominating the foreground. “We stripped out every unnecessary pixel by building a real-time panning and zooming technology that automatically keeps callers’ faces–and only their faces–in view at all times” Zane explains. It’s basically Facebook Messenger’s old Chat Heads design, but for the desktop enterprise.

Calls start with a shared link or /Around Slack command. You’re never unexpectedly dumped into a call, so you can stay on task. Since participants are closely cropped to their faces and not blown up full screen, they don’t have to worry about cleaning their workspace or exactly how their hair looks. That reduces the divide between work-from-homers and those in the office.

As for technology, Around’s “EchoTerminator” uses ultrasonic audio to detect nearby laptops and synchronization to eliminate those strange feedback sounds. Around also employs artificial intelligence and the fast CPUs of modern laptops to suppress noise like sirens, dog barks, washing machines, or screaming children. A browser version means you don’t have to wait for people to download anything, and visual emotes like “Cool idea” pop up below people’s faces so they don’t have to interrupt the speaker.

Traditional video chat vs Around

“Around is what you get when you rethink video chat for a 21st-century audience, with 21st-century technology,” says Initialized co-founder and general partner Garry Tan. “Around has cracked an incredibly difficult problem, integrating video into the way people actually work today. It makes other video-call products feel clumsy by comparison.”

There’s one big thing missing from Around: mobile. Since it’s meant for multitasking, it’s desktop/laptop only. But that orthodoxy ignores the fact that a team member on the go might still want to chime in on chats, even with just audio. Mobile apps are on the roadmap, though, with plans to allow direct dial-in and live transitioning from laptop to mobile. The 15-participant limit also prevents Around from working for all-hands meetings.

Competing with video calling giant Zoom will be a serious challenge. Nearly a decade of perfecting its technology gives Zoom super low latency so people don’t talk over each other. Around will have to hope that its smaller windows let it keep delays down. There’s also other multitask video apps like Loom’s asynchronously-recorded video clips that prevent distraction.

With coronavirus putting a new emphasis on video technology for tons of companies, finding great engineers could be difficult. “Talent is scarce, and good video is hard tech. Video products are on the rise. Google and large companies snag all the talent, plus they have the ability and scale to train audio-video professionals at universities in northern Europe” Zane tells me. “Talent wars are the biggest risk and obstacle for all real-time video companies.”

But that rise also means there are tons of people fed up with having to stop work to video chat, kids and pets wandering into their calls, and constantly yelling at co-workers to “mute your damn mic!” If ever there was a perfect time to launch Around, it’s now.

“Eight years ago we were a team of locals and immigrants, traveling frequently, moving between locations and offices” Zane recalls. “We realized that this was the future of work and it’s going to be one of the most significant transformations of modern society over the next 30 years . . . We’re building the product we’ve wanted for ourselves.”

One of the best things about working remotely is you don’t have colleagues randomly bugging you about superfluous nonsense. But the heaviness of traditional video chat swings things too far in the other direction. You’re isolated unless you want to make a big deal out of scheduling a call. We need presence and connection, but also the space to remain in flow. We don’t want to be away or on top of each other. We want to be around.

Nov
12
2019
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Lawyers hate timekeeping — Ping raises $13M to fix it with AI

Counting billable time in six-minute increments is the most annoying part of being a lawyer. It’s a distracting waste. It leads law firms to conservatively under-bill. And it leaves lawyers stuck manually filling out timesheets after a long day when they want to go home to their families.

Life is already short, as Ping CEO and co-founder Ryan Alshak knows too well. The former lawyer spent years caring for his mother as she battled a brain tumor before her passing. “One minute laughing with her was worth a million doing anything else,” he tells me. “I became obsessed with the idea that we spend too much of our lives on things we have no need to do — especially at work.”

That’s motivated him as he’s built his startup Ping, which uses artificial intelligence to automatically track lawyers’ work and fill out timesheets for them. There’s a massive opportunity to eliminate a core cause of burnout, lift law firm revenue by around 10% and give them fresh insights into labor allocation.

Ping co-founder and CEO Ryan Alshak (Image Credit: Margot Duane)

That’s why today Ping is announcing a $13.2 million Series A led by Upfront Ventures, along with BoxGroup, First Round, Initialized and Ulu Ventures. Adding to Ping’s quiet $3.7 million seed led by First Round last year, the startup will spend the cash to scale up enterprise distribution and become the new timekeeping standard.

I was a corporate litigator at Manatt Phelps down in LA and joke that I was voted the world’s worst timekeeper,” Alshak tells me. “I could either get better at doing something I dreaded or I could try and build technology that did it for me.”

The promise of eliminating the hassle could make any lawyer who hears about Ping an advocate for the firm buying the startup’s software, like how Dropbox grew as workers demanded easier file sharing. “I’ve experienced first-hand the grind of filling out timesheets,” writes Initialized partner and former attorney Alda Leu Dennis. “Ping takes away the drudgery of manual timekeeping and gives lawyers back all those precious hours.”

Traditionally, lawyers have to keep track of their time by themselves down to the tenth of an hour — reviewing documents for the Johnson case, preparing a motion to dismiss for the Lee case, a client phone call for the Sriram case. There are timesheets built into legal software suites like MyCase, legal billing software like TimeSolv and one-off tools like Time Miner and iTimeKeep. They typically offer timers that lawyers can manually start and stop on different devices, with some providing tracking of scheduled appointments, call and text logging, and integration with billing systems.

Ping goes a big step further. It uses AI and machine learning to figure out whether an activity is billable, for which client, a description of the activity and its codification beyond just how long it lasted. Instead of merely filling in the minutes, it completes all the logs automatically, with entries like “Writing up a deposition – Jenkins Case – 18 minutes.” Then it presents the timesheet to the user for review before they send it to billing.

The big challenge now for Alshak and the team he’s assembled is to grow up. They need to go from cat-in-sunglasses logo Ping to mature wordmark Ping.  “We have to graduate from being a startup to being an enterprise software company,” the CEO tells meThat means learning to sell to C-suites and IT teams, rather than just build a solid product. In the relationship-driven world of law, that’s a very different skill set. Ping will have to convince clients it’s worth switching to not just for the time savings and revenue boost, but for deep data on how they could run a more efficient firm.

Along the way, Ping has to avoid any embarrassing data breaches or concerns about how its scanning technology could violate attorney-client privilege. If it can win this lucrative first business in legal, it could barge into the consulting and accounting verticals next to grow truly huge.

With eager customers, a massive market, a weak status quo and a driven founder, Ping just needs to avoid getting in over its heads with all its new cash. Spent well, the startup could leap ahead of the less tech-savvy competition.

Alshak seems determined to get it right. “We have an opportunity to build a company that gives people back their most valuable resource — time — to spend more time with their loved ones because they spent less time working,” he tells me. “My mom will live forever because she taught me the value of time. I am deeply motivated to build something that lasts . . . and do so in her name.”

Mar
20
2017
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PlateIQ cooks up $4 million for restaurant management platform

 As any fan of Kitchen Nightmares may know, the restaurant business is not for the faint of heart, nor is it likely to generate fat margins. Now, a startup called PlateIQ has raised $4 million in new funding to help restaurants automate accounts payable, and nail all their other accounting work so they can focus, finally, on the food and front-of-house. Eileses Capital led the round… Read More

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