Last year, autonomous driving startup Zoox was acquired by Amazon in a deal worth $1.3 billion. Since then, Zoox has continued to pursue its existing strategy of developing and deploying autonomous passenger vehicles, revealing the design of its long-anticipated robotaxi late in December. From concept to reveal, Zoox spent six years developing its built-for-purpose passenger AV, and the plan is to launch them initially with commercial deployments in Las Vegas and San Francisco following testing. At TC Sessions: Mobility this year on June 9, we’ll have the chance to speak to Zoox co-founder and CTO Jesse Levinson about the company’s progress toward those goals, and what it’s like for Zoox nearly a year on as an Amazon company.
In an interview with TechCrunch from last year, Levinson told us that life under Amazon at the AV company has been essentially business as usual since the acquisition — with greatly expanded access to resources, of course, and potentially with even more autonomy than before, he said, since they’re not beholden to a host of outside investors as they pursue their goals.
Of course, the natural assumption when considering Amazon and its interest in autonomous vehicles is package delivery — which is why it’s so interesting that Zoox is, and has always, prioritized movement of people, not parcels, in its AV development roadmap. Zoox’s debut vehicle has been designed entirely with passenger transportation in mind, though the company’s CEO Aicha Evans has acknowledged in the past that it could definitely work on package delivery in partnership with its new corporate owner in the future.
We’ll hear from Levinson if there are any updates to Zoox’s plan or focus, and what Amazon’s ambitions are for autonomous vehicles in the long term. We’ll also talk about the AV industry overall, and the major shifts its undergone in the years that Zoox has been operating, and what that means for growing and attracting talent. Levinson knows the industry and the state of the art in AV technology better than most, so be sure to grab tickets to TC Sessions: Mobility 2021 ASAP and check out our chat on June 9.
Book your early-bird pass today and save $100 before prices increase next week and join today’s leading mobility-startup event.
After an upward revision, UiPath priced its IPO last night at $56 per share, a few dollars above its raised target range. The above-range price meant that the unicorn put more capital into its books through its public offering.
For a company in a market as competitive as robotic process automation (RPA), the funds are welcome. In fact, RPA has been top of mind for startups and established companies alike over the last year or so. In that time frame, enterprise stalwarts like SAP, Microsoft, IBM and ServiceNow have been buying smaller RPA startups and building their own, all in an effort to muscle into an increasingly lucrative market.
In June 2019, Gartner reported that RPA was the fastest-growing area in enterprise software, and while the growth has slowed down since, the sector is still attracting attention. UIPath, which Gartner found was the market leader, has been riding that wave, and today’s capital influx should help the company maintain its market position.
It’s worth noting that when the company had its last private funding round in February, it brought home $750 million at an impressive valuation of $35 billion. But as TechCrunch noted over the course of its pivot to the public markets, that round valued the company above its final IPO price. As a result, this week’s $56-per-share public offer wound up being something of a modest down-round IPO to UiPath’s final private valuation.
Then, a broader set of public traders got hold of its stock and bid its shares higher. The former unicorn’s shares closed their first day’s trading at precisely $69, above the per-share price at which the company closed its final private round.
So despite a somewhat circuitous route, UiPath closed its first day as a public company worth more than it was in its Series F round — when it sold 12,043,202 shares sold at $62.27576 apiece, per SEC filings. More simply, UiPath closed today worth more per-share than it was in February.
How you might value the company, whether you prefer a simple or fully-diluted share count, is somewhat immaterial at this juncture. UiPath had a good day.
While it’s hard to know what the company might do with the proceeds, chances are it will continue to try to expand its platform beyond pure RPA, which could become market-limited over time as companies look at other, more modern approaches to automation. By adding additional automation capabilities — organically or via acquisitions — the company can begin covering broader parts of its market.
TechCrunch spoke with UiPath CFO Ashim Gupta today, curious about the company’s choice of a traditional IPO, its general avoidance of adjusted metrics in its SEC filings, and the IPO market’s current temperature. The final question was on our minds, as some companies have pulled their public listings in the wake of a market described as “challenging”.
If the eight person team behind the new startup Hadrian has their way, they’ll have transformed the manufacturing industry within the next decade.
At least, that’s the goal for the new San Francisco-based startup, founded only last year, which has set its sights on building out a new model for advanced manufacturing to enable the satellite, space ship, and advanced energy technology companies to build the future they envision better and faster.
“We view our job as to provide the world’s most efficient space and defense component factory,” said Hadrian founder, Chris Power.
Initially, the company is building factories to make the parts that go on rocket ships, according to Power, but the business has implications for any company that needs bespoke components to make their equipment.
“Let me tell you how bad it is at the moment and what’s going to happen over the next 20 years. Right now everyone in space and defense, [including] SpaceX and Lockheed Martin, outsources their parts and manufacturing to small factories across the country. They’re super expensive, they’re unreliable and they’re completely invisible to the customers,” said Power. “This causes big problems with space and defense manufacturers in the design phase, because the lead time is so long and the iteration time is super long. Imagine running software and being able to iterate on your product once every 20 days? If you can imagine a Gantt chart of how to build a rocket, about 60% of that is buffer time… A lot of the delays in launches and stuff like that happen because parts got delivered three months ago. It’d be like running a McDonalds and realizing that your fries and burger providers could not tell you when the food would arrive.”
It’s hard to overstate the strategic importance of the parts suppliers to the operations of aerospace, defense, and advanced machining companies. As no less an authority on manufacturing than Elon Musk noted in a tweet, “The factory is the product.” It’s also hard to overstate the geopolitical importance of re-establishing the U.S. as a center of manufacturing excellence, according to Hadrian’s investors Lux Capital, Founders Fund, and Construct Capital. Which is one reason why they’re investing $9.5 million into the very early stage business.
“America made massive strategic mistakes in the early 90s which have left our national manufacturing ecosystem completely dilapidated,” said Founders Fund principal Delian Asparouhov. “The only way to get out of this disaster is to re-invent the most basic input into our aerospace and defense supply chains, machining metal parts quickly and with high tolerance. Right now, America’s most innovative company, SpaceX, relies on a network of near-retired machinists to produce space-worthy metal parts, and no one in technology is. focused on solving this.”
The factory is the product
— Elon Musk (@elonmusk) January 11, 2021
Power got to understand the problem at his previous company, Ento, which sold workforce management software to blue collar customers. It was there he realized the issue of. the aging workforce and the need for manufacturers to upgrade almost every aspect of their own technology stack. “I realized that the right way to bring technology to the industrial space is not to sell software to these companies, it’s to build an industrial business from scratch with software.”
Initially, Hadrian is focusing all of its efforts on the space industry, where the component manufacturing problem is especially acute, but the manufacturing capabilities the company is building out have broad relevance across any industry that requires highly engineered components.
“The demand for manufacturing from both the large SpaceX and Blue Origin all the way to this growing long tail of companies from Anduril to Relativity to Varda,” said Lux Capital co-founder Josh Wolfe. “Most of these guys are using mom and pop machine shops… [and] those shops are horribly inefficient. They’re not consistent, and they’re not reliable. Between the software automation, the hardware, you can cut down on inefficiency every step of the process… I like to think of value creation as waste reduction… so mundane things like quoting, scheduling, bidding, and planning all the way to the programming of the manufacturing… every one of those things takes hours to tens of hours to days and weeks, so if you can do that in minutes, it’s just a no-brainer. [Hadrian] will be the cutting edge choice for all of the new and explicitly dedicated and focused aerospace and defense companies.”
Power envisions a network of manufacturing facilities that can initially cover roughly 65% of all space and defense components, and will eventually take that number up to 95% of components. Already several of the biggest launch vehicle and satellite manufacturers are in talks with the company to produce hundreds of units for them, Power said. Some of those companies just happen to be in the Construct, Lux, and Founders Fund portfolio.
And the company’s founder sees this as a new way to revitalize American manufacturing jobs as well. “Manufacturing jobs in space and defense can easily be as high paying as a software engineering job at Google,” he said. In an ideal world, Hadrian would like to offer an onramp to high paying manufacturing careers in the 21st century in the same way that automakers provided good union jobs in the twentieth.
“We haven’t built any of this. If you look at the sheer number of people that we need to train and hire on our new technology and new systems, that people problem and that training problem is part of growing our business.”
A render of Axiom’s future commercial space station design.
Automation has become a big theme in enterprise IT, with organizations using RPA, no-code and low-code tools, and other technology to speed up work and bring more insights and analytics into how they do things every day, and today IBM is announcing an acquisition as it hopes to take on a bigger role in providing those automation services. The IT giant has acquired MyInvenio, an Italian startup that builds and operates process mining software.
Process mining is the part of the automation stack that tracks data produced by a company’s software, as well as how the software works, in order to provide guidance on what a company could and should do to improve it. In the case of myInvenio, the company’s approach involves making a “digital twin” of an organization to help track and optimize processes. IBM is interested in how myInvenio’s tools are able to monitor data in areas like sales, procurement, production and accounting to help organizations identify what might be better served with more automation, which it can in turn run using RPA or other tools as needed.
Terms of the deal are not being disclosed. It is not clear if myInvenio had any outside investors (we’ve asked and are awaiting a response). This is the second acquisition IBM has made out of Italy. (The first was in 2014, a company called CrossIdeas that now forms part of the company’s security business.)
IBM and myInvenio are not exactly strangers: the two inked a deal as recently as November 2020 to integrate the Italian startup’s technology into IBM’s bigger automation services business globally.
Dinesh Nirmal, GM of IBM Automation, said in an interview that the reason IBM acquired the company was two-fold. First, it lets IBM integrate the technology more closely into the company’s Cloud Pak for Business Automation, which sits on and is powered by Red Hat OpenShift and has other automation capabilities already embedded within it, specifically robotic process automation (RPA), document processing, workflows and decisions.
Second and perhaps more importantly, it will mean that IBM will not have to tussle for priority for its customers in competition with other solution partners that myInvenio already had. IBM will be the sole provider.
“Partnerships are great but in a partnership you also have the option to partner with others, and when it comes to priority who decides?” he said. “From the customer perspective, will they will work just on our deal, or others first? Now, our customers will get the end result of this… We can bring a single solution to an end user or an enterprise, saying, ‘look you have document processing, RPA, workflow, mining. That is the beauty of this and what customers will see.”
He said that IBM currently serves customers across a range of verticals including financial, insurance, healthcare and manufacturing with its automation products.
Notably, this is not the first acquisition that IBM has made to build out this stack. Last year, it acquired WDG to expand into robotic process automation.
And interestingly, it’s not even the only partnership that IBM has had in process mining. Just earlier this month, it announced a deal with one of the bigger names in the field, Celonis, a German startup valued at $2.5 billion in 2019.
Ironically, at the time, my colleague Ron wondered aloud why IBM wasn’t just buying Celonis outright in that deal. It’s hard to speculate if price was one reason. Remember: we don’t know the terms of this acquisition, but given myInvenio was off the fundraising radar, chances are it’s possibly a little less than Celonis’s pricetag.
We’ve asked and IBM has confirmed that it will continue to work with Celonis alongside now offering its own native process mining tools.
“In keeping with IBM’s open approach and $1 billion investment in ecosystem, [Global Business Services, IBM’s enterprise services division] works with a broad range of technologies based on client and market demand, including IBM AI and Automation software,” a spokesperson said in a statement. “Celonis focuses on execution management which supports GBS’ transformation of clients’ business processes through intelligent workflows across industries and domains. Specifically, Celonis has deep connectivity into enterprise systems such as Salesforce, SAP, Workday or ServiceNow, so the Celonis EMS platform helps GBS accelerate clients’ transformations and BPO engagements with these ERP platforms.”
Indeed, at the end of the day, companies that offer services, especially suites of services, are working in environments where they have to be open to customers using their own technology, or bringing in something else.
There may have been another force pushing IBM to bring more of this technology in-house, and that’s wider competitive climate. Earlier this year, SAP acquired another European startup in the process mining space, Signavio, in a deal reportedly worth about $1.2 billion. As more of these companies get snapped up by would-be IBM rivals, and those left standing are working with a plethora of other parties, maybe it was high time for IBM to make sure it had its own horse in the race.
“Through IBM’s planned acquisition of myInvenio, we are revolutionizing the way companies manage their process operations,” said Massimiliano Delsante, CEO, myInvenio, who will be staying on with the deal. “myInvenio’s unique capability to automatically analyze processes and create simulations — what we call a ‘Digital Twin of an Organization’ — is joining with IBM’s AI-powered automation capabilities to better manage process execution. Together we will offer a comprehensive solution for digital process transformation and automation to help enterprises continuously transform insights into action.”
A few years ago, founder Sean Lane thought he’d achieved product-market fit.
Speaking to attendees at TechCrunch’s Early Stage virtual event, Lane said Queue, a secure digital check-in tablet for hospital waiting rooms that reduced wait times by uniting and correcting electronic medical records, was “selling like hotcakes.” But once Lane realized it would only ever address one piece of a much bigger market opportunity, he sold off the product, laid off two-thirds of the people affiliated with it and redirected the employees who were left.
Lane explained that what he really wanted to build is what his company — since renamed Olive — has now become, a robotic process automation (RPA) company that takes on hospital workers’ most tedious tasks so nurses and physicians can spend more time with patients.
Customers seem to like it. According to Lane, more than 600 hospitals use the service to assist employees with tasks like prior authorizations and patient verifications.
Investors clearly approve of what Olive is selling, too: Last year, the company raised three rounds of funding totaling roughly $380 million and valuing the company at $1.5 billion. According to Crunchbase, it’s raised a total of $456 million altogether.
In fact, VCs think so much of Lane that in February, they invested $50 million in another company that Lane runs simultaneously called Circulo, a startup that describes itself as building the “Medicaid insurance company of the future.”
Still, the path from point A to B was painful, and it might not have happened if Lane didn’t have a few things going for him, including a deeply personal reason to build something that could have greater impact on the U.S. healthcare system.
Elon Musk famously said any company relying on lidar is “doomed.” Tesla instead believes automated driving functions are built on visual recognition and is even working to remove the radar. China’s Xpeng begs to differ.
Founded in 2014, Xpeng is one of China’s most celebrated electric vehicle startups and went public when it was just six years old. Like Tesla, Xpeng sees automation as an integral part of its strategy; unlike the American giant, Xpeng uses a combination of radar, cameras, high-precision maps powered by Alibaba, localization systems developed in-house, and most recently, lidar to detect and predict road conditions.
“Lidar will provide the 3D drivable space and precise depth estimation to small moving obstacles even like kids and pets, and obviously, other pedestrians and the motorbikes which are a nightmare for anybody who’s working on driving,” Xinzhou Wu, who oversees Xpeng’s autonomous driving R&D center, said in an interview with TechCrunch.
“On top of that, we have the usual radar which gives you location and speed. Then you have the camera which has very rich, basic semantic information.”
Xpeng is adding lidar to its mass-produced EV model P5, which will begin delivering in the second half of this year. The car, a family sedan, will later be able to drive from point A to B based on a navigation route set by the driver on highways and certain urban roads in China that are covered by Alibaba’s maps. An older model without lidar already enables assisted driving on highways.
The system, called Navigation Guided Pilot, is benchmarked against Tesla’s Navigate On Autopilot, said Wu. It can, for example, automatically change lanes, enter or exit ramps, overtake other vehicles, and maneuver another car’s sudden cut-in, a common sight in China’s complex road conditions.
“The city is super hard compared to the highway but with lidar and precise perception capability, we will have essentially three layers of redundancy for sensing,” said Wu.
By definition, NGP is an advanced driver-assistance system (ADAS) as drivers still need to keep their hands on the wheel and take control at any time (Chinese laws don’t allow drivers to be hands-off on the road). The carmaker’s ambition is to remove the driver, that is, reach Level 4 autonomy two to four years from now, but real-life implementation will hinge on regulations, said Wu.
“But I’m not worried about that too much. I understand the Chinese government is actually the most flexible in terms of technology regulation.”
Musk’s disdain for lidar stems from the high costs of the remote sensing method that uses lasers. In the early days, a lidar unit spinning on top of a robotaxi could cost as much as $100,000, said Wu.
“Right now, [the cost] is at least two orders low,” said Wu. After 13 years with Qualcomm in the U.S., Wu joined Xpeng in late 2018 to work on automating the company’s electric cars. He currently leads a core autonomous driving R&D team of 500 staff and said the force will double in headcount by the end of this year.
“Our next vehicle is targeting the economy class. I would say it’s mid-range in terms of price,” he said, referring to the firm’s new lidar-powered sedan.
The lidar sensors powering Xpeng come from Livox, a firm touting more affordable lidar and an affiliate of DJI, the Shenzhen-based drone giant. Xpeng’s headquarters is in the adjacent city of Guangzhou about 1.5 hours’ drive away.
Xpeng isn’t the only one embracing lidar. Nio, a Chinese rival to Xpeng targeting a more premium market, unveiled a lidar-powered car in January but the model won’t start production until 2022. Arcfox, a new EV brand of Chinese state-owned carmaker BAIC, recently said it would be launching an electric car equipped with Huawei’s lidar.
Musk recently hinted that Tesla may remove radar from production outright as it inches closer to pure vision based on camera and machine learning. The billionaire founder isn’t particularly a fan of Xpeng, which he alleged owned a copy of Tesla’s old source code.
In 2019, Tesla filed a lawsuit against Cao Guangzhi alleging that the former Tesla engineer stole trade secrets and brought them to Xpeng. XPeng has repeatedly denied any wrongdoing. Cao no longer works at Xpeng.
While Livox claims to be an independent entity “incubated” by DJI, a source told TechCrunch previously that it is just a “team within DJI” positioned as a separate company. The intention to distance from DJI comes as no one’s surprise as the drone maker is on the U.S. government’s Entity List, which has cut key suppliers off from a multitude of Chinese tech firms including Huawei.
Other critical parts that Xpeng uses include NVIDIA’s Xavier system-on-the-chip computing platform and Bosch’s iBooster brake system. Globally, the ongoing semiconductor shortage is pushing auto executives to ponder over future scenarios where self-driving cars become even more dependent on chips.
Xpeng is well aware of supply chain risks. “Basically, safety is very important,” said Wu. “It’s more than the tension between countries around the world right now. Covid-19 is also creating a lot of issues for some of the suppliers, so having redundancy in the suppliers is some strategy we are looking very closely at.”
Xpeng could have easily tapped the flurry of autonomous driving solution providers in China, including Pony.ai and WeRide in its backyard Guangzhou. Instead, Xpeng becomes their competitor, working on automation in-house and pledges to outrival the artificial intelligence startups.
“The availability of massive computing for cars at affordable costs and the fast dropping price of lidar is making the two camps really the same,” Wu said of the dynamics between EV makers and robotaxi startups.
“[The robotaxi companies] have to work very hard to find a path to a mass-production vehicle. If they don’t do that, two years from now, they will find the technology is already available in mass production and their value become will become much less than today’s,” he added.
“We know how to mass-produce a technology up to the safety requirement and the quarantine required of the auto industry. This is a super high bar for anybody wanting to survive.”
Xpeng has no plans of going visual-only. Options of automotive technologies like lidar are becoming cheaper and more abundant, so “why do we have to bind our hands right now and say camera only?” Wu asked.
“We have a lot of respect for Elon and his company. We wish them all the best. But we will, as Xiaopeng [founder of Xpeng] said in one of his famous speeches, compete in China and hopefully in the rest of the world as well with different technologies.”
5G, coupled with cloud computing and cabin intelligence, will accelerate Xpeng’s path to achieve full automation, though Wu couldn’t share much detail on how 5G is used. When unmanned driving is viable, Xpeng will explore “a lot of exciting features” that go into a car when the driver’s hands are freed. Xpeng’s electric SUV is already available in Norway, and the company is looking to further expand globally.
Workflow automation has been one of the key trends this year so far, and Zoho, a company known for its suite of affordable business tools has joined the parade with a new low code workflow product called Qntrl (pronounced control).
Zoho’s Rodrigo Vaca, who is in charge of Qntrl’s marketing says that most of the solutions we’ve been seeing are built for larger enterprise customers. Zoho is aiming for the mid-market with a product that requires less technical expertise than traditional business process management tools.
“We enable customers to design their workflows visually without the need for any particular kind of prior knowledge of business process management notation or any kind of that esoteric modeling or discipline,” Vaca told me.
While Vaca says, Qntrl could require some technical help to connect a workflow to more complex backend systems like CRM or ERP, it allows a less technical end user to drag and drop the components and then get help to finish the rest.
“We certainly expect that when you need to connect to NetSuite or SAP you’re going to need a developer. If nothing else, the IT guys are going to ask questions, and they will need to provide access,” Vaca said.
He believes this product is putting this kind of tooling in reach of companies that may have been left out of workflow automation for the most part, or which have been using spreadsheets or other tools to create crude workflows. With Qntrl, you drag and drop components, and then select each component and configure what happens before, during and after each step.
What’s more, Qntrl provides a central place for processing and understanding what’s happening within each workflow at any given time, and who is responsible for completing it.
We’ve seen bigger companies like Microsoft, SAP, ServiceNow and others offering this type of functionality over the last year as low code workflow automation has taken center stage in business.
This has become a more pronounced need during the pandemic when so many workers could not be in the office. It made moving work in a more automated workflow more imperative, and we have seen companies moving to add more of this kind of functionality as a result.
Brent Leary, principal analyst at CRM Essentials, says that Zoho is attempting to remove some the complexity from this kind of tool.
“It handles the security pieces to make sure the right people have access to the data and processes used in the workflows in the background, so regular users can drag and drop to build their flows and processes without having to worry about that stuff,” Leary told me.
Zoho Qntrl is available starting today starting at just $7 per user month.
You hear so much about data these days that you might forget that a huge amount of the world runs on documents: a veritable menagerie of heterogeneous files and formats holding enormous value yet incompatible with the new era of clean, structured databases. Docugami plans to change that with a system that intuitively understands any set of documents and intelligently indexes their contents — and NASA is already on board.
If Docugami’s product works as planned, anyone will be able to take piles of documents accumulated over the years and near-instantly convert them to the kind of data that’s actually useful to people.
Because it turns out that running just about any business ends up producing a ton of documents. Contracts and briefs in legal work, leases and agreements in real estate, proposals and releases in marketing, medical charts, etc, etc. Not to mention the various formats: Word docs, PDFs, scans of paper printouts of PDFs exported from Word docs, and so on.
Over the last decade there’s been an effort to corral this problem, but movement has largely been on the organizational side: put all your documents in one place, share and edit them collaboratively. Understanding the document itself has pretty much been left to the people who handle them, and for good reason — understanding documents is hard!
Think of a rental contract. We humans understand when the renter is named as Jill Jackson, that later on, “the renter” also refers to that person. Furthermore, in any of a hundred other contracts, we understand that the renters in those documents are the same type of person or concept in the context of the document, but not the same actual person. These are surprisingly difficult concepts for machine learning and natural language understanding systems to grasp and apply. Yet if they could be mastered, an enormous amount of useful information could be extracted from the millions of documents squirreled away around the world.
Docugami founder Jean Paoli says they’ve cracked the problem wide open, and while it’s a major claim, he’s one of few people who could credibly make it. Paoli was a major figure at Microsoft for decades, and among other things helped create the XML format — you know all those files that end in x, like .docx and .xlsx? Paoli is at least partly to thank for them.
“Data and documents aren’t the same thing,” he told me. “There’s a thing you understand, called documents, and there’s something that computers understand, called data. Why are they not the same thing? So my first job [at Microsoft] was to create a format that can represent documents as data. I created XML with friends in the industry, and Bill accepted it.” (Yes, that Bill.)
The formats became ubiquitous, yet 20 years later the same problem persists, having grown in scale with the digitization of industry after industry. But for Paoli the solution is the same. At the core of XML was the idea that a document should be structured almost like a webpage: boxes within boxes, each clearly defined by metadata — a hierarchical model more easily understood by computers.
“A few years ago I drank the AI kool-aid, got the idea to transform documents into data. I needed an algorithm that navigates the hierarchical model, and they told me that the algorithm you want does not exist,” he explained. “The XML model, where every piece is inside another, and each has a different name to represent the data it contains — that has not been married to the AI model we have today. That’s just a fact. I hoped the AI people would go and jump on it, but it didn’t happen.” (“I was busy doing something else,” he added, to excuse himself.)
The lack of compatibility with this new model of computing shouldn’t come as a surprise — every emerging technology carries with it certain assumptions and limitations, and AI has focused on a few other, equally crucial areas like speech understanding and computer vision. The approach taken there doesn’t match the needs of systematically understanding a document.
“Many people think that documents are like cats. You train the AI to look for their eyes, for their tails… documents are not like cats,” he said.
It sounds obvious, but it’s a real limitation: advanced AI methods like segmentation, scene understanding, multimodal context, and such are all a sort of hyper-advanced cat detection that has moved beyond cats to detect dogs, car types, facial expressions, locations, etc. Documents are too different from one another, or in other ways too similar, for these approaches to do much more than roughly categorize them.
And as for language understanding, it’s good in some ways but not in the ways Paoli needed. “They’re working sort of at the English language level,” he said. “They look at the text but they disconnect it from the document where they found it. I love NLP people, half my team is NLP people — but NLP people don’t think about business processes. You need to mix them with XML people, people who understand computer vision, then you start looking at the document at a different level.”
Paoli’s goal couldn’t be reached by adapting existing tools (beyond mature primitives like optical character recognition), so he assembled his own private AI lab, where a multi-disciplinary team has been tinkering away for about two years.
“We did core science, self-funded, in stealth mode, and we sent a bunch of patents to the patent office,” he said. “Then we went to see the VCs, and Signalfire basically volunteered to lead the seed round at $10 million.”
Coverage of the round didn’t really get into the actual experience of using Docugami, but Paoli walked me through the platform with some live documents. I wasn’t given access myself and the company wouldn’t provide screenshots or video, saying it is still working on the integrations and UI, so you’ll have to use your imagination… but if you picture pretty much any enterprise SaaS service, you’re 90 percent of the way there.
As the user, you upload any number of documents to Docugami, from a couple dozen to hundreds or thousands. These enter a machine understanding workflow that parses the documents, whether they’re scanned PDFs, Word files, or something else, into an XML-esque hierarchical organization unique to the contents.
“Say you’ve got 500 documents, we try to categorize it in document sets, these 30 look the same, those 20 look the same, those 5 together. We group them with a mix of hints coming from how the document looked, what it’s talking about, what we think people are using it for, etc,” said Paoli. Other services might be able to tell the difference between a lease and an NDA, but documents are too diverse to slot into pre-trained ideas of categories and expect it to work out. Every set of documents is potentially unique, and so Docugami trains itself anew every time, even for a set of one. “Once we group them, we understand the overall structure and hierarchy of that particular set of documents, because that’s how documents become useful: together.”
That doesn’t just mean it picks up on header text and creates an index, or lets you search for words. The data that is in the document, for example who is paying whom, how much and when, and under what conditions, all that becomes structured and editable within the context of similar documents. (It asks for a little input to double check what it has deduced.)
It can be a little hard to picture, but now just imagine that you want to put together a report on your company’s active loans. All you need to do is highlight the information that’s important to you in an example document — literally, you just click “Jane Roe” and “$20,000” and “5 years” anywhere they occur — and then select the other documents you want to pull corresponding information from. A few seconds later you have an ordered spreadsheet with names, amounts, dates, anything you wanted out of that set of documents.
All this data is meant to be portable too, of course — there are integrations planned with various other common pipes and services in business, allowing for automatic reports, alerts if certain conditions are reached, automated creation of templates and standard documents (no more keeping an old one around with underscores where the principals go).
Remember, this is all half an hour after you uploaded them in the first place, no labeling or pre-processing or cleaning required. And the AI isn’t working from some preconceived notion or format of what a lease document looks like. It’s learned all it needs to know from the actual docs you uploaded — how they’re structured, where things like names and dates figure relative to one another, and so on. And it works across verticals and uses an interface anyone can figure out a few minutes. Whether you’re in healthcare data entry or construction contract management, the tool should make sense.
The web interface where you ingest and create new documents is one of the main tools, while the other lives inside Word. There Docugami acts as a sort of assistant that’s fully aware of every other document of whatever type you’re in, so you can create new ones, fill in standard information, comply with regulations, and so on.
Okay, so processing legal documents isn’t exactly the most exciting application of machine learning in the world. But I wouldn’t be writing this (at all, let alone at this length) if I didn’t think this was a big deal. This sort of deep understanding of document types can be found here and there among established industries with standard document types (such as police or medical reports), but have fun waiting until someone trains a bespoke model for your kayak rental service. But small businesses have just as much value locked up in documents as large enterprises — and they can’t afford to hire a team of data scientists. And even the big organizations can’t do it all manually.
The problem is extremely difficult, yet to humans seems almost trivial. You or I could glance through 20 similar documents and a list of names and amounts easily, perhaps even in less time than it takes for Docugami to crawl them and train itself.
But AI, after all, is meant to imitate and excel human capacity, and it’s one thing for an account manager to do monthly reports on 20 contracts — quite another to do a daily report on a thousand. Yet Docugami accomplishes the latter and former equally easily — which is where it fits into both the enterprise system, where scaling this kind of operation is crucial, and to NASA, which is buried under a backlog of documentation from which it hopes to glean clean data and insights.
If there’s one thing NASA’s got a lot of, it’s documents. Its reasonably well maintained archives go back to its founding, and many important ones are available by various means — I’ve spent many a pleasant hour perusing its cache of historical documents.
But NASA isn’t looking for new insights into Apollo 11. Through its many past and present programs, solicitations, grant programs, budgets, and of course engineering projects, it generates a huge amount of documents — being, after all, very much a part of the federal bureaucracy. And as with any large organization with its paperwork spread over decades, NASA’s document stash represents untapped potential.
Expert opinions, research precursors, engineering solutions, and a dozen more categories of important information are sitting in files searchable perhaps by basic word matching but otherwise unstructured. Wouldn’t it be nice for someone at JPL to get it in their head to look at the evolution of nozzle design, and within a few minutes have a complete and current list of documents on that topic, organized by type, date, author, and status? What about the patent advisor who needs to provide a NIAC grant recipient information on prior art — shouldn’t they be able to pull those old patents and applications up with more specificity than any with a given keyword?
The NASA SBIR grant, awarded last summer, isn’t for any specific work, like collecting all the documents of such and such a type from Johnson Space Center or something. It’s an exploratory or investigative agreement, as many of these grants are, and Docugami is working with NASA scientists on the best ways to apply the technology to their archives. (One of the best applications may be to the SBIR and other small business funding programs themselves.)
Another SBIR grant with the NSF differs in that, while at NASA the team is looking into better organizing tons of disparate types of documents with some overlapping information, at NSF they’re aiming to better identify “small data.” “We are looking at the tiny things, the tiny details,” said Paoli. “For instance, if you have a name, is it the lender or the borrower? The doctor or the patient name? When you read a patient record, penicillin is mentioned, is it prescribed or prohibited? If there’s a section called allergies and another called prescriptions, we can make that connection.”
When I pointed out the rather small budgets involved with SBIR grants and how his company couldn’t possibly survive on these, he laughed.
“Oh, we’re not running on grants! This isn’t our business. For me, this is a way to work with scientists, with the best labs in the world,” he said, while noting many more grant projects were in the offing. “Science for me is a fuel. The business model is very simple – a service that you subscribe to, like Docusign or Dropbox.”
The company is only just now beginning its real business operations, having made a few connections with integration partners and testers. But over the next year it will expand its private beta and eventually open it up — though there’s no timeline on that just yet.
“We’re very young. A year ago we were like five, six people, now we went and got this $10M seed round and boom,” said Paoli. But he’s certain that this is a business that will be not just lucrative but will represent an important change in how companies work.
“People love documents. Maybe it’s because I’m French,” he said, “but I think text and books and writing are critical — that’s just how humans work. We really think people can help machines think better, and machines can help people think better.”
Cruise has expanded its robotaxi ambitions beyond San Francisco. The autonomous vehicle subsidiary of GM that also has backing from SoftBank Vision Fund, Microsoft and Honda, has struck a deal to launch a robotaxi service in Dubai in 2023.
The robotaxi service in Dubai will use the Cruise Origin, the all-electric shuttle-like vehicle that has no steering wheel or pedals and is designed to travel at highway speeds. The Origin, which was unveiled in January 2020 will be manufactured by GM.
Cruise will establish a new local Dubai-based company which will be responsible for the deployment, operation and maintenance of the fleet.
The service will start with a limited number of vehicles with plans to scale up to 4,000 vehicles by 2030 as part of Dubai’s self-driving transport strategy, according to Mattar Mohammed Al Tayer, the director-general and chairman of the board of the RTA. The robotaxis — and eventually the service — will be introduced gradually and limited to specific areas before expanding to other parts of the city.
Dubai’s Crown Prince Sheikh Hamdan bin Mohammed said the agreement with Cruise is a “major step towards realizing Dubai’s Self-Driving Transport Strategy aimed at converting 25% of total trips in Dubai into self-driving transport trips across different modes of transport by 2030.”
Importantly, Cruise has a lock on Dubai for at least a few years. Under the agreement, Cruise is the “exclusive provider” for self-driving taxis and ride-hailing services in Dubai until 2029. Al Tayer said the selection of Cruise was not taken lightly and involved a comprehensive, multi-year process.
Tines, a no-code automation platform co-founded by two senior cybersecurity operators, today announced that it has raised a $26 million Series B funding round led by Addition. Existing investors Accel and Blossom Capital participated in this round, which also includes strategic investments from CrowdStrike and Silicon Valley CISO Investments. After this round, which brings the total funding in the company to $41.1 million, Tines is now valued at $300 million.
Given that Tines co-founders Eoin Hinchy and Thomas Kinsella were both in senior security roles at DocuSign before they left to start their own company in 2018, it’s maybe no surprise that the company’s platform launched with a strong focus on security operations. As such, it combines security orchestration and robotic process automation with a low-code/no-code user interface.
“Tines is on a mission to allow frontline employees to focus on more business-critical tasks and improve their wellbeing by reducing the burden of ‘busy work’ by helping automate any manual workflow and making existing teams more efficient, effective, and engaged,” the company notes in today’s announcement.
The idea here is to free analysts from spending time on routine repetitive tasks and allow them to focus on those areas where they can have the most impact. The tools features pre-configured integrations with a variety of business and security tools, but for more sophisticated users, it also features the ability to hook into virtually any API.
The company argues that even non-technical employees should be able to learn the ins and outs of its platform within about three hours (sidenote: it’s nice to see a no-code platform acknowledge that users will actually need to spend some time with it before they can become productive).
“If software is eating the world, automation is eating the enterprise,” Hinchy said. “Yet, the majority of progress in this space still requires non-technical teams to depend on software engineers to implement their automation. Other platforms are generally either too hard to use, not flexible enough or not sufficiently robust for mission-critical workflows like cybersecurity. Tines empowers enterprise teams to automate any of their own manual workloads independently, making their jobs more rewarding while simultaneously delivering enormous value for their organizations.”
Current Tines customers include the likes of Box, Canva, OpenTable and Sophos.
The company, which was founded in Dublin, Ireland and recently opened an office in Boston, plans to use the new funding to double its 18-person team in order to support its product growth.
“Tines has quickly established itself as a market leader in enterprise automation,” said Lee Fixel, founder of Addition. “We look forward to supporting Eoin and the Tines team as they continue to scale the business and enhance their product — which is beloved by their unmatched customer base.”
No-code startups continue to see a lot of traction among enterprises, where employees — strictly speaking, non-technical, but still using software every day — are getting hands-on and building apps to take on some of the more repetitive aspects of their jobs, the so-called “citizen coders” of the working world.
And in one of the latest developments, a Bryter — an AI-based no-code startup that has built a platforms used by some 100 global enterprises to date across some 2,000 business applications and workflows — is announcing a new round of funding to double down on that opportunity. The Berlin-based company has closed a Series B of $66 million, money that it will be investing into its platform and expanding in the U.S. out of a New York office it opened last year. The funding comes on the heels of seeing a lot of demand for its tools, CEO and co-founder Michael Grupp said in an interview.
“It was a great year for low-code and no-code platforms,” said Grupp, who co-founded the company with Micha-Manuel Bues and Michael Hübl. “What everyone has realized is that most people don’t actually care about the tech. They only care about the use cases. They want to get things done.” Customers using the service include the likes of McDonald’s, Telefónica, and PwC, KPMG and Deloitte in Europe, as well as banks, healthcare and industrial enterprises.
Tiger Global is leading this round, with previous backers Accel, Dawn Capital, Notion Capital and Cavalry Ventures all also participating, along with a number of individual backers (they include Amit Agharwal, CPO of DataDog; Lars Björk, former CEO of Qlik; Ulf Zetterberg, founder and CEO of Seal Software; and former ServiceNow global SVP James Fitzgerald). The valuation is not being disclosed; Bryter has raised around $90 million to date.
Accel and Dawn co-led Bryter’s Series A of $16 million less than a year ago, in June 2020, a rapid funding pace that underscores both interest in the no-code/low-code space — Bryter’s enterprise customer base has doubled from 50 since then — and the fact that startups in it are striking while the iron is hot.
Bryter’s not the only one: Airtable, Genesis, Rows, Creatio, and Ushur are among the many startups building ‘hands-on tech creation for non-techie people’ that have raised money in the last several months.
Automation has been the bigger trend that has propelled a lot of this activity. Knowledge workers today spend most of their time these days in apps — a state of affairs that pre-dates the Covid-19 pandemic, but has definitely been furthered throughout it. While some of that work still requires manual involvement and evaluation from those workers, software has automated large swathes of those jobs.
RPA — robotic process automation, where companies like UiPath, Automation Anywhere and Blue Prism have taken a big lead — has accounted for a significant chunk of that activity, especially when it comes to reading forms and lots of data entry. But there remains a lot of other transactions and activities within specific apps where RPA is typically not used (not yet at least!). And this is where non-tech workers are finding that no-code tools like Bryter, which use artificial intelligence to deliver more personalised, yet scalable, automation, can play a very useful role.
“We sit on top of RPA in many cases,” said Grupp.
The company says that business functions where its platform has been implemented include compliance, legal, tax, privacy and security, procurement, administration, and HR, and the kinds of features that are being built include virtual assistants, chatbots, interactive self-service tools, and more.
These don’t replace people as such but cut down the time they need to spend in specific tasks to process and handle information within them, and could in theory also be used to build tools for customers to interact with services more easily, cutting down on the amount of time that agents are getting details and handling engagements.
That scalability, and the rapid customer up-take from a pool of users that extends beyond tech early-adopters, are part of what attracted the funding.
“Bryter has all the characteristics of a top-tier software company: high quality product that solves a real customer pain point, a large market opportunity and a world-class founding team,” said John Curtius, a partner at Tiger Global, in a statement. “The feedback from Bryter’s customers was resoundingly positive in our research, and we are excited to see the company reach new heights over the coming years.”
“Bryter has seen explosive growth over the last year, signing landmark customers across a large number of sectors and use cases. This does not come as a surprise. In the pandemic-affected world, digitalisation is no longer a nice to have, it is an imperative,” added Evgenia Plotnikova, a partner at Dawn Capital.
When UIPath filed its S-1 last week, it was a watershed moment for the robotic process automation (RPA) market. The company, which first appeared on our radar for a $30 million Series A in 2017, has so far raised an astonishing $2 billion while still private. In February, it was valued at $35 billion when it raised $750 million in its latest round.
RPA and process automation came to the fore during the pandemic as companies took steps to digitally transform. When employees couldn’t be in the same office together, it became crucial to cobble together more automated workflows that required fewer people in the loop.
RPA has enabled executives to provide a level of workflow automation that essentially buys them time to update systems to more modern approaches while reducing the large number of mundane manual tasks that are part of every industry’s workflow.
When UIPath raised money in 2017, RPA was not well known in enterprise software circles even though it had already been around for several years. The category was gaining in popularity by that point because it addressed automation in a legacy context. That meant companies with deep legacy technology — practically everyone not born in the cloud — could automate across older platforms without ripping and replacing, an expensive and risky undertaking that most CEOs would rather not take.
RPA has enabled executives to provide a level of workflow automation, a taste of the modern. It essentially buys them time to update systems to more modern approaches while reducing the large number of mundane manual tasks that are part of just about every industry’s workflow.
While some people point to RPA as job-elimination software, it also provides a way to liberate people from some of the most mind-numbing and mundane chores in the organization. The argument goes that this frees up employees for higher level tasks.
As an example, RPA could take advantage of older workflow technologies like OCR (optical character recognition) to read a number from a form, enter the data in a spreadsheet, generate an invoice, send it for printing and mailing, and generate a Slack message to the accounting department that the task has been completed.
We’re going to take a deep dive into RPA and the larger process automation space — explore the market size and dynamics, look at the key players and the biggest investors, and finally, try to chart out where this market might go in the future.
UIPath is clearly an RPA star with a significant market share lead of 27.1%, according to IDC. Automation Anywhere is in second place with 19.4%, and Blue Prism is third with 10.3%, based on data from IDC’s July 2020 report, the last time the firm reported on the market.
Two other players with significant market share worth mentioning are WorkFusion with 6.8%, and NTT with 5%.
Motional will integrate its driverless technology into Hyundai’s new all-electric SUV to create the company’s first robotaxi. At the start of 2023, customers in certain markets will be able to book the fully electric, fully autonomous taxi through the Lyft app.
The Hyundai IONIQ 5, which was revealed in February with a consumer release date expected later this year, will be fully integrated with Motional’s driverless system. The vehicles will be equipped with the hardware and software needed for Level 4 autonomous driving capabilities, including LiDAR, radar and cameras to provide the vehicle’s sensing system with 360 degrees of vision, and the ability to see up to 300 meters away. This level of driverless technology means a human will not be required to take over driving.
The interior living space will be similar to the consumer model, but additionally equipped with features needed for robotaxi operation, according to a Motional spokesperson. Motional did not reveal whether or not the vehicle would still have a steering wheel, and images of the robotaxi aren’t yet available.
Motional’s IONIQ 5 robotaxis have already begun testing on public roads and closed courses, and they’ll be put through more months of testing and real-world experience before being deployed on Lyft’s platform. The company says it’ll complete testing only once it’s confident that the taxis are safer than a human driver.
Motional, the Aptiv-Hyundai $4 billion joint venture aimed at commercializing driverless cars, announced its partnership with Lyft in December, signaling the ride-hailing company’s primary involvement in Motional’s plans. The company recently announced that it began testing its driverless tech on public roads in Las Vegas. Hyundai’s IONIQ 5 is Motional’s second platform to go driverless on public roads.
UI-licious’ co-founders, chief technology officer Eugene Cheah (left) and chief executive officer Shi Ling Tai (right)
UI-licious, a Singapore-based startup that simplifies automated user interface testing for web applications, announced today it has raised $1.5 million in pre-Series A funding. The round was led by Monk’s Hill Ventures and will be used to grow UI-licious’ product development and marketing teams.
Founded in 2016 by Shi Ling Tai and Eugene Cheah, UI-licious serves companies of all sizes, and its current clients include Daimler, Jones Lang LaSalle and tech recruitment platform Glints.
Tai, UI-licious’ chief executive officer, said that about 90% of software teams around the world rely on manual testing, which is both time-consuming and expensive. UI-licious enables users to write test scripts in pseudocode, or a language that is similar to plain English and therefore accessible to people with little programming experience.
Software teams can then schedule how often the tests run. UI-licious’ proprietary smart targeting test engine supports all browsers and allows the same scripts to be run even if there are changes in a web application’s user interface or underlying code. It also produces detailed error reports to reduce the time needed to find and fix a bug.
When asked how UI-licious compares to other automated user interface testing solutions, Tai told TechCrunch, “Coded solutions require a trained engineer to inspect the website’s code to write the test scripts. The problem is that most software testers are not trained programmers, sometimes they may be the marketing or sales team that owns the project. And while there are other no-code solutions that allow non-programmers to record their actions and replay it, such tests tend to become obsolete quickly as the UI changes.”
UI-licious’ selling point is that “it is designed to make it accessible for anyone to automate UI testing and set up error alerts without needing to know how to code,” she added. “UI-licious also reduces the effort to maintain the tests as the UI code changes with its smart targeting test engine.”
In press statement, Monk’s Hill Ventures partner Justin Nguyen said, “Co-founders Shi Ling and Eugene have developed a product to address the quality assurance issues that have plagued the software automation industry for decades,” adding that “the team’s experience as software engineers has equipped them with the technical knowledge and insights to build a simple and robust tool that empowers manual testers to automate testing and detect bugs before users do.”
ServiceNow became the latest company to take the robotic process automation (RPA) plunge when it announced it was acquiring Intellibot, an RPA startup based in Hyderabad, India. The companies did not reveal the purchase price.
The purchase comes at a time where companies are looking to automate workflows across the organization. RPA provides a way to automate a set of legacy processes, which often involve humans dealing with mundane repetitive work.
The announcement comes on the heels of the company’s no-code workflow announcements earlier this month and is part of the company’s broader workflow strategy, according to Josh Kahn, SVP of Creator Workflow Products at ServiceNow.
“RPA enhances ServiceNow’s current automation capabilities including low code tools, workflow, playbooks, integrations with over 150 out of the box connectors, machine learning, process mining and predictive analytics,” Khan explained. He says that the company can now bring RPA natively to the platform with this acquisition, yet still use RPA bots from other vendors if that’s what the customer requires.
“ServiceNow customers can build workflows that incorporate bots from the pure play RPA vendors such as Automation Anywhere, UiPath and Blue Prism, and we will continue to partner with those companies. There will be many instances where customers want to use our native RPA capabilities alongside those from our partners as they build intelligent, end-to-end automation workflows on the Now Platform,” Khan explained.
The company is making this purchase as other enterprise vendors enter the RPA market. SAP announced a new RPA tool at the end of December and acquired process automation startup Signavio in January. Meanwhile Microsoft announced a free RPA tool earlier this month, as the space is clearly getting the attention of these larger vendors.
ServiceNow has been on a buying spree over the last year or so buying five companies including Element AI, Loom Systems, Passage AI and Sweagle. Khan says the acquisitions are all in the service of helping companies create automation across the organization.
“As we bring all of these technologies into the Now Platform, we will accelerate our ability to automate more and more sophisticated use cases. Things like better handling of unstructured data from documents such as written forms, emails and PDFs, and more resilient automations such as larger data sets and non-routine tasks,” Khan said.
Intellibot was founded in 2015 and will provide the added bonus of giving ServiceNow a stronger foothold in India. The companies expect to close the deal no later than June.
It’s clear that automated workflow tooling has become increasingly important for companies. Perhaps that explains why Camunda, a Berlin startup that makes open-source process automation software, announced an €82 million Series B today. That translates into approximately $98 million U.S.
Insight Partners led the round with help from A round investor Highland Europe. When combined with the $28 million A investment from December 2018, it brings the total raised to approximately $126 million.
What’s attracting this level of investment says Jakob Freund, co-founder and CEO at Camunda, is the company is solving a problem that goes beyond pure automation. “There’s a bigger thing going on which you could call end-to-end automation or end-to-end orchestration of endpoints, which can be RPA bots, for example, but also micro services and manual work [by humans],” he said.
He added, “Camunda has become this endpoint agnostic orchestration layer that sits on top of everything else.” That means that it provides the ability to orchestrate how the automation pieces work in conjunction with one another to create this full workflow across a company.
The company has 270 employees and approximately 400 customers at this point, including Goldman Sachs, Lufthansa, Universal Music Group and Orange. Matt Gatto, managing director at Insight Partners, sees a tremendous market opportunity for the company and that’s why his firm came in with such a big investment.
“Camunda’s success demonstrates how an open, standards-based, developer-friendly platform for end-to-end process automation can increase business agility and improve customer experiences, helping organizations truly transform to a digital enterprise,” Gatto said in a statement.
Camunda is not your typical startup. Its history actually dates back to 2008 as a business process management (BPM) consulting firm. It began the Camunda open-source project in 2013, and that was the start of pivoting to become an open-source software company with a commercial component built on top of that.
It took the funding at the end of 2018 because the market was beginning to catch up with the idea, and they wanted to build on that. It’s going so well that the company reports it’s cash-flow positive, and will use the additional funding to continue accelerating the business.
DeepSee.ai, a startup that helps enterprises use AI to automate line-of-business problems, today announced that it has raised a $22.6 million Series A funding round led by led by ForgePoint Capital. Previous investors AllegisCyber Capital and Signal Peak Ventures also participated in this round, which brings the Salt Lake City-based company’s total funding to date to $30.7 million.
The company argues that it offers enterprises a different take on process automation. The industry buzzword these days is ‘robotic process automation,’ but DeepSee.ai argues that what it does is different. I describe its system as ‘knowledge process automation’ (KPA). The company itself defines this as a system that “mines unstructured data, operationalizes AI-powered insights, and automates results into real-time action for the enterprise.” But the company also argues that today’s bots focus on basic task automation that doesn’t offer the kind of deeper insights that sophisticated machine learning models can bring to the table. The company also stresses that it doesn’t aim to replace knowledge workers but help them leverage AI to turn the plethora of data that businesses now collect into actionable insights.
“Executives are telling me they need business outcomes and not science projects,” writes DeepSee.ai CEO Steve Shillingford. “And today, the burgeoning frustration with most AI-centric deployments in large-scale enterprises is they look great in theory but largely fail in production. We think that’s because right now the current ‘AI approach’ lacks a holistic business context relevance. It’s unthinking, rigid, and without the contextual input of subject-matter experts on the ground. We founded DeepSee to bridge the gap between powerful technology and line-of-business, with adaptable solutions that empower our customers to operationalize AI-powered automation – delivering faster, better, and cheaper results for our users.”
To help businesses get started with the platform, DeepSee.ai offers three core tools. There’s DeepSee Assembler, which ingests unstructured data and gets it ready for labeling, model review and analysis. Then, DeepSee Atlas can use this data to train AI models that can understand a company’s business processes and help subject-matter experts define templates, rules and logic for automating a company’s internal processes. The third tool, DeepSee Advisor, meanwhile focuses on using text analysis to help companies better understand and evaluate their business processes.
Currently, the company’s focus is on providing these tools for insurance companies, the public sector and capital markets. In the insurance space, use cases include fraud detection, claims prediction and processing, and using large amounts of unstructured data to identify patterns in agent audits, for example.
That’s a relatively limited number of industries for a startup to operate in, but the company says it will use its new funding to accelerate product development and expand to new verticals.
“Using KPA, line-of-business executives can bridge data science and enterprise outcomes, operationalize AI/ML-powered automation at scale, and use predictive insights in real time to grow revenue, reduce cost, and mitigate risk,” said Sean Cunningham, Managing Director of ForgePoint Capital. “As a leading cybersecurity investor, ForgePoint sees the daily security challenges around insider threat, data visibility, and compliance. This investment in DeepSee accelerates the ability to reduce risk with business automation and delivers much-needed AI transparency required by customers for implementation.”
This morning Pipe17, a software startup focused on the e-commerce market, announced that it has closed $8 million in funding.
Pipe17’s service helps smaller e-commerce merchants connect their digital tools, without the need to code. With the startup’s service, e-commerce operations that may lack an in-house IT function can quickly connect their selling platform to shipping, or point-of-sale data to their ERP.
The venture arm of a large logistics investor GLP, GLP Capital Partners led the round.
Pipe17 co-founders Mo Afshar and Dave Shaffer told TechCrunch in an interview that the idea for their startup came from examining the e-commerce market, noting the energy to be found concerning selling platforms, and the comparative dearth of software to help get e-commerce tools to work together; Shopify and BigCommerce and Shippo are just fine, but if you can’t code you might wind up schlepping data from one platform to the next to keep your e-commerce operation humming.
So they built Pipe17 to fill in the gap.
According to Afshar, Pipe17 wants to simplify operations for e-commerce merchants through the lens of connection; the pair of co-founders believe that easy cross-compatibility is the key missing ingredient in the modern-day e-commerce software stack, likening the current e-commerce maket to the IT and datacenter worlds before the advent of Splunk and Datadog.
The prevailing view in the e-commerce industry, the co-founders explained, is that to fix a problem e-commerce players should purchase another application. Pipe17 thinks that most ecommerce companies probably have enough tooling, and that they instead need to get their existing tooling to communicate.
What’s neat about the startup is that it’s building something that we might call no-code-no-code, or no-code to a higher degree. Instead of offering a interface for non-developers to visually map out connections between different software services, it has pre-built what might need to be mapped. Just pick the two e-commerce services you want to link, and Pipe17 will connect them for you in an intelligent manner. For folks who find any sort of coding hard (which probably describes a lot of indie online store operators), the method could be an attractive pitch.
The startup’s customer target are sellers doing single-digit millions to nine-figures in year sales.
Why did Pipe17 raise capital now? The co-founders said that there are only so many chances to simplify a large market, akin to what Plaid and Twilio did for their own niches, so taking on funds now made sense. In Afshar’s view, e-commerce operations is going to be simply massive. Given the growth in digital selling that we saw last year, it’s a perspective that is hard to dispute.
The niche that Pipe17 wants to fill has more than one player. While the startups themselves might quibble about just how much competitive space they share, Y Combinator-backed Alloy recently raised $4 million to build a no-code e-commerce automation service. Which is related to what Pipe17 does. It will be interesting to see if they wind up in competition, and, if so, who comes out on top.
Efficient and cost-effective vaccine distribution remains one of the biggest challenges of 2021, so it’s no surprise that startup Notable Health wants to use their automation platform to help. Initially started to help address the nearly $250 billion annual administrative costs in healthcare, Notable Health launched in 2017 to use automation to replace time-consuming and repetitive simple tasks in health industry admin. In early January of this year, they announced plans to use that technology as a way to help manage vaccine distribution.
“As a physician, I saw firsthand that with any patient encounter, there are 90 steps or touchpoints that need to occur,” said Notable Health medical director Muthu Alagappan in an interview. “It’s our hypothesis that the vast majority of those points can be automated.”
Notable Health’s core technology is a platform that uses robotic process automation (RPA), natural language processing (NLP), and machine learning to find eligible patients for the COVID-19 vaccine. Combined with data provided by hospital systems’ electronic health records, the platform helps those qualified to receive the vaccine set up appointments and guides them to other relevant educational resources.
“By leveraging intelligent automation to identify, outreach, educate and triage patients, health systems can develop efficient and equitable vaccine distribution workflows,” said Notable Health strategic advisor and Biden Transition COVID-19 Advisory Board Member Dr. Ezekiel Emanuel, in a press release.
Making vaccine appointments has been especially difficult for older Americans, many of whom have reportedly struggled with navigating scheduling websites. Alagappan sees that as a design problem. “Technology often gets a bad reputation, because it’s hampered by the many bad technology experiences that are out there,” he said.
Instead, he thinks Notable Health has kept the user in mind through a more simplified approach, asking users only for basic and easy-to-remember information through a text message link. “It’s that emphasis on user-centric design that I think has allowed us to still have really good engagement rates even with older populations,” he said.
While the startup’s platform will likely help hospitals and health systems develop a more efficient approach to vaccinations, its use of RPA and NLP holds promise for future optimization in healthcare. Leaders of similar technology in other industries have already gone on to have multi-billion dollar valuations, and continue to attract investors’ interest.
Artificial intelligence is expected to grow in healthcare over the next several years, but Alagappan argues that combining that with other, more readily available intelligent technologies is also an important step towards improved care. “When we say intelligent automation, we’re really referring to the marriage of two concepts: artificial intelligence—which is knowing what to do—and robotic process automation—which is knowing how to do it,” he said. That dual approach is what he says allows Notable Health to bypass administrative bottlenecks in healthcare, instructing bots to carry out those tasks in an efficient and adaptable way.
So far, Notable Health has worked with several hospital systems across multiple states in using their platform for vaccine distribution and scheduling, and are now using the platform to reach out to tens of thousands of patients per day.