Salesforce steps into RPA buying Servicetrace and teaming it with Mulesoft

Over the last couple of years, Robotic Process Automation or RPA has been red hot with tons of investor activity and M&A from companies like SAP, IBM and ServiceNow. UIPath had a major IPO in April and has a market cap over $30 billion. I wondered when Salesforce would get involved and today the company dipped its toe into the RPA pool, announcing its intent to buy German RPA company Servicetrace.

Salesforce intends to make Servicetrace part of Mulesoft, the company it bought in 2018 for $6.5 billion. The companies aren’t divulging the purchase price, suggesting it’s a much smaller deal. When Servicetrace is in the fold, it should fit in well with Mulesoft’s API integration, helping to add an automation layer to Mulesoft’s tool kit.

“With the addition of Servicetrace, MuleSoft will be able to deliver a leading unified integration, API management, and RPA platform, which will further enrich the Salesforce Customer 360 — empowering organizations to deliver connected experiences from anywhere. The new RPA capabilities will enhance Salesforce’s Einstein Automate solution, enabling end-to-end workflow automation across any system for Service, Sales, Industries, and more,” Mulesoft CEO Brent Hayward wrote in a blog post announcing the deal.

While Einstein, Salesforce’s artificial intelligence layer, gives companies with more modern tooling the ability to automate certain tasks, RPA is suited to more legacy operations, and this acquisition could be another step in helping Salesforce bridge the gap between older on-prem tools and more modern cloud software.

Brent Leary, founder and principal analyst at CRM Essentials says that it brings another dimension to Salesforce’s digital transformation tools. “It didn’t take Salesforce long to move to the next acquisition after closing their biggest purchase with Slack. But automation of processes and workflows fueled by realtime data coming from a growing variety sources is becoming a key to finding success with digital transformation. And this adds a critical piece to that puzzle for Salesforce/MulseSoft,” he said.

While it feels like Salesforce is joining the market late, in an investor survey we published in May Laela Sturdy, general partner at CapitalG told us that we are just skimming the surface so far when it comes to RPA’s potential.

“We’re a long way from needing to think about the space maturing. In fact, RPA adoption is still in its early infancy when you consider its immense potential. Most companies are only now just beginning to explore the numerous use cases that exist across industries. The more enterprises dip their toes into RPA, the more use cases they envision,” Sturdy responded in the survey.

Servicetrace was founded in 2004, long before the notion of RPA even existed. Neither Crunchbase nor Pitchbook shows any money raised, but the website suggests a mature company with a rich product set. Customers include Fujitsu, Siemens, Merck and Deutsche Telekom.


By Ron Miller

Cognigy raises $44M to scale its enterprise-focused conversational AI platform

Artificial intelligence is becoming an increasingly common part of how customer service works — a trend that was accelerated in this past year as so many other services went virtual and digital — and today a startup that has built a set of low-code tools to help enterprises integrate more AI into their customer service processes is announcing some funding to fuel its growth.

Cognigy, which provides a low-code conversational AI platform that notably can be used flexibly across a range of applications and geographies — it supports 120 languages; it can be used in external or internal service applications; it can support voice services but also chatbots; it provides real-time assistance for human agents and usage analytics or fully-automated responses; it can integrate with standard call center software, and also with RPA packages; and it can be run in the cloud or on-premise — has closed a round of $44 million, funding that it will be using to continue scaling its business internationally.

Insight Partners is leading the Series B investment, with previous backers DN Capital, Global Brain, Nordic Makers, Inventures and Digital Innovation and Growth also participating. The Dusseldorf-based company had previously only raised $11 million and spent the first several years of business bootstrapped.

Cognigy is not disclosing its valuation but it has up to now built up a concentration of customers in areas like transportation, e-commerce and insurance and counts a number of big multinational companies among its customer list, including Lufthansa, Mobily, BioNTech, Vueling Airlines, Bosch, and Daimler, with “thousands” of virtual assistants now powered by Cognigy live in the market.

With 25% of Cognigy’s business already coming from the U.S., the plan now is to use some funding to invest in building out its service deeper into the U.S., Asia and across more of Europe, CEO and founder Philipp Heltewig said in an interview.

“Conversational AI” these days appears in many guises: it can be a chatbot you come across on a website when you’re searching for something, or it can be prompts provided to agents or salespeople, information and real-time feedback to help them do their jobs better. Conversational AI can also be a personal assistant on your company’s HR application to help you book time off or deal with any number of other administrative jobs, or a personal assistant that helps you use your phone or set your house alarm.

There are a number of companies in the tech world that have built tools to address these various use cases. Specifically in the area of services aimed at enterprises, some of them, like Gong, are raising huge money right now. What is notable about Cognigy is that it has built a platform that is attempting to address a wide swathe of applications: one platform, many uses, in other words.

Cognigy’s other selling point is that it is playing into the new interest in low- and no-code tools, which in Cognigy’s case makes the integration of AI into a customer assistance process a relatively easy task, something that can be built not just by developers, but data scientists, those working directly on conversation design, and non-technical business users using the tools themselves.

“The low-code platform helps enterprises adopt what is otherwise complex technology in an easy and flexible way, whether it is customer or employee contact center,” said Heltewig. As you might expect, there are some direct competitors in the low- and no-code conversational AI space, too, including Ada, Talkie, Snaps and more.

Flexibility seems to be the order of the day for enterprises, and also the companies building tools for them: it means that a company can grow into a larger customer, and that in theory Cognigy will also evolve the platform based on what its customers need. As one example, Heltewig pointed out that a number of its customers are — contrary to the beating drum and march you see every day towards cloud services — running a fair number of applications on-premises, since this appears to be a key way to ensure the security of the customer data that they handle.

“Lufthansa could never run its customer services in the cloud because they handle a lot of sensitive data and they want full ownership of it,” he noted. “We can run cloud services and have a full offering for those who want it, but many large enterprises prefer to run their services on premises.”

Teddie Wardi, an MD at Insight, is joining the board with this round. “We are thrilled to be leading Cognigy’s Series B as the company continues on their ScaleUp journey,” he said in a statement. “Evident by their strong customer retention, Cognigy has created an essential product for global businesses to improve their customer experience in an efficient and effortless manner. With the new funding, Cognigy will be able to expand their leadership position to reach new markets and acquire more customers.”


By Ingrid Lunden

5 investors discuss the future of RPA after UIPath’s IPO

Robotic process automation (RPA) has certainly been getting a lot of attention in the last year, with startups, acquisitions and IPOs all coming together in a flurry of market activity. It all seemed to culminate with UiPath’s IPO last month. The company that appeared to come out of nowhere in 2017 eventually had a final private valuation of $35 billion. It then had the audacity to match that at its IPO. A few weeks later, it still has a market cap of over $38 billion in spite of the stock price fluctuating at points.

Was this some kind of peak for the technology or a flash in the pan? Probably not. While it all seemed to come together in the last year with a big increase in attention to automation in general during the pandemic, it’s a market category that has been around for some time.

RPA allows companies to automate a group of highly mundane tasks and have a machine do the work instead of a human. Think of finding an invoice amount in an email, placing the figure in a spreadsheet and sending a Slack message to Accounts Payable. You could have humans do that, or you could do it more quickly and efficiently with a machine. We’re talking mind-numbing work that is well suited to automation.

In 2019, Gartner found RPA was the fastest-growing category in enterprise software. In spite of that, the market is still surprisingly small, with IDC estimates finding it will reach just $2 billion in 2021. That’s pretty tiny for the enterprise, but it shows that there’s plenty of room for this space to grow.

We spoke to five investors to find out more about RPA, and the general consensus was that we are just getting started. While we will continue to see the players at the top of the market — like UiPath, Automation Anywhere and Blue Prism — jockeying for position with the big enterprise vendors and startups, the size and scope of the market has a lot of potential and is likely to keep growing for some time to come.

To learn about all of this, we queried the following investors:

  • Mallun Yen, founder and partner, Operator Collective
  • Jai Das, partner and president, Sapphire Ventures
  • Soma Somasegar, managing director, Madrona Venture Group
  • Laela Sturdy, general partner, CapitalG
  • Ed Sim, founder and managing partner, Boldstart Ventures

We have seen a range of RPA startups emerge in recent years, with companies like UiPath, Blue Prism and Automation Anywhere leading the way. As the space matures, where do the biggest opportunities remain?

Mallun Yen: One of the fastest-growing categories of software, RPA has been growing at over 60% in recent years, versus 13% for enterprise software generally. But we’ve barely scratched the surface. The COVID-19 pandemic forced companies to shift how they run their business, how they hire and allocate staff.

Given that the workforce will remain at least partially permanently remote, companies recognize that this shift is also permanent, and so they need to make fundamental changes to how they run their businesses. It’s simply suboptimal to hire, train and deploy remote employees to run routine processes, which are prone to, among other things, human error and boredom.

Jai Das: All the companies that you have listed are focused on automating simple repetitive tasks that are performed by humans. These are mostly data entry and data validation jobs. Most of these tasks will be automated in the next couple of years. The new opportunity lies in automating business processes that involve multiple humans and machines within complicated workflow using AI/ML.

Sometimes this is also called process mining. There have been BPM companies in the past that have tried to automate these business processes, but they required a lot of services to implement and maintain these automated processes. AI/ML is providing a way for software to replace all these services.

Soma Somasegar: For all the progress that we have seen in RPA, I think it is still early days. The global demand for RPA market size in terms of revenue was more than $2 billion this past year and is expected to cross $20 billion in the coming decade, growing at a CAGR of more than 30% over the next seven to eight years, according to analysts such as Gartner.

That’s an astounding growth rate in the coming years and is a reflection of how early we are in the RPA journey and how much more is ahead of us. A recent study by Deloitte indicates that up to 50% of the tasks in businesses performed by employees are considered mundane, administrative and labor-intensive. That is just a recipe for a ton of process automation.

There are a lot of opportunities that I see here, including process discovery and mining; process analytics; application of AI to drive effective, more complex workflow automation; and using low code/no code as a way to enable a broader set of people to be able to automate tasks, processes and workflows, to name a few.

Laela Sturdy: We’re a long way from needing to think about the space maturing. In fact, RPA adoption is still in its early infancy when you consider its immense potential. Most companies are only now just beginning to explore the numerous use cases that exist across industries. The more enterprises dip their toes into RPA, the more use cases they envision.

I expect to see market leaders like UiPath continue to innovate rapidly while expanding the breadth and depth of their end-to-end automation platforms. As the technology continues to evolve, we should expect RPA to penetrate even more deeply into the enterprise and to automate increasingly more — and more critical — business processes.

Ed Sim: Most large-scale automation projects require a significant amount of professional services to deliver on the promises, and two areas where I still see opportunity include startups that can bring more intelligence and faster time to value. Examples include process discovery, which can help companies quickly and accurately understand how their business processes work and prioritize what to automate versus just rearchitecting an existing workflow.


By Ron Miller

Laiye, China’s answer to UiPath, closes $50 million Series C+

Robotic process automation has become buzzy in the last few months. New York-based UiPath is on course to launch an initial public offering after gaining an astounding valuation of $35 billion in February. Over in China, homegrown RPA startup Laiye is making waves as well.

Laiye, which develops software to mimic mundane workplace tasks like keyboard strokes and mouse clicks, announced it has raised $50 million in a Series C+ round. The proceeds came about a year after the Beijing-based company pulled in the first tranche of its Series C round.

Laiye, six years old and led by Baidu veterans, has raised over $130 million to date according to public information.

Leading investors in the Series C+ round were Ping An Global Voyager Fund, an early-stage strategic investment vehicle of Chinese financial conglomerate Ping An, and Shanghai Artificial Intelligence Industry Equity Investment Fund, a government-backed fund. Other participants included Lightspeed China Partners, Lightspeed Venture Partners, Sequoia China and Wu Capital.

RPA tools are attracting companies looking for ways to automate workflows during COVID-19, which has disrupted office collaboration. But the enterprise tech was already gaining traction prior to the pandemic. As my colleague, Ron Miller wrote this month on the heels of UiPath’s S1 filing:

“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.”

In one case, Laiye’s RPA software helped the social security workers in the city of Lanzhou speed up their account reconciliation process by 75%; in the past, they would have to type in pensioners’ information and check manually whether the details were correct.

In another instance, Laiye’s chatbot helped automate the national population census in several southern Chinese cities, freeing census takers from visiting households door-to-door.

Laiye said its RPA enterprise business achieved positive cash flow and its chatbot business turned profitability in the fourth quarter of 2020. Its free-to-use edition has amassed over 400,000 developers, and the company also runs a bot marketplace connecting freelance developers to small-time businesses with automation needs.

Laiye is expanding its services globally and boasts that its footprint now spams Asia, the United States and Europe.

“Laiye aims to foster the world’s largest developer community for software robots and built the world’s largest bot marketplace in the next three years, and we plan to certify at least one million software robot developers by 2025,” said Wang Guanchun, chair and CEO of Laiye.

“We believe that digital workforce and intelligent automation will reach all walks of life as long as more human workers can be up-skilled with knowledge in RPA and AI”.


By Rita Liao

IBM acquires Italy’s MyInvenio to integrate process mining directly into its suite of automation tools

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.”


By Ingrid Lunden

Docugami’s new model for understanding documents cuts its teeth on NASA archives

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.

What’s up, .docx?

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.

Illustration showing a document corresponding to pieces of another document.

Image Credits: Docugami

“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.”

Docugami in action

Illustration showing a person interacting with a digital document.

Image Credits: Docugami

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.”

Illustration showing a document being turned into a report and a spreadsheet.

Image Credits: Docugami

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.

NASA’s treasure trove

Image Credits: NASA

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.”

“Maybe it’s because I’m French”

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.”


By Devin Coldewey

No code, workflow, and RPA line up for their automation moment

We’ve seen a lot of trend lines moving throughout 2020 and into 2021 around automation, workflow, robotic process automation (RPA) and the movement to low-code and no-code application building. While all of these technologies can work on their own, they are deeply connected and we are starting to see some movement towards bringing them together.

While the definition of process automation is open to interpretation, and could include things like industrial automation, Statista estimates that the process automation market could be worth $74 billion in 2021. Those are numbers that are going to get the attention of both investors and enterprise software executives.

Just this week, Berlin-based Camunda announced a $98 million Series B to help act as a layer to orchestrate the flow of data between RPA bots, microservices and human employees. Meanwhile UIPath, the pure-play RPA startup that’s going to IPO any minute now, acquired Cloud Elements, giving it a way to move beyond RPA into API automation.

Not enough proof for you? How about ServiceNow announcing this week that it is buying Indian startup Intellibot to give it — you guessed it — RPA capabilities. That acquisition is part of a broader strategy by the company to move into full-scale workflow and automation, which it discussed just a couple of weeks ago.

Meanwhile at the end of last year, SAP bought a different Berlin process automation startup, Signavio, for $1.2 billion after announcing new automated workflow tools and an RPA tool at the beginning of December. Microsoft is in on it too, having acquired process automation startup Softmotive last May, which it then combined with its own automation tool PowerAutomate.

What we have here is a frothy mix of startups and large companies racing to provide a comprehensive spectrum of workflow automation tools to empower companies to spin up workflows quickly and move work involving both human and machine labor through an organization.

The result is hot startups getting prodigious funding, while other startups are exiting via acquisition to these larger companies looking to buy instead of build to gain a quick foothold in this market.

Cathy Tornbohm, Distinguished Research Vice President at Gartner, says part of the reason for the rapidly growing interest is that these companies have stayed on the sidelines up until now, but they see an opportunity and are using their checkbooks to play catch up.

“IBM, SAP, Pega, Appian, Microsoft, ServiceNow all bought into the RPA market because for years they didn’t focus on how data got into their systems when operating between organizations or without a human. [Instead] they focused more on what happens inside the client’s organization. The drive to be digitally more efficient necessitates optimizing data ingestion and data flows,” Tornbohm told me.

For all the bluster from the big vendors, they do not control the pure-play RPA market. In fact, Gartner found that the top three players in this space are UIPath, Automation Anywhere and Blue Prism.

But Tornbohm says that, even as the traditional enterprise vendors try to push their way into the space, these pure-play companies are not sitting still. They are expanding beyond their RPA roots into the broader automation space, which could explain why UIPath came up from its pre-IPO quiet period to make the Cloud Elements announcement this week.

Dharmesh Thakker, managing partner at Battery Ventures, agrees with Tornbohm, saying that the shift to the cloud, accelerated by COVID-19, has led to an expansion of what RPA vendors are doing.

“RPA has traditionally focused on automation-UI flow and user steps, but we believe a full automation suite requires that ability to automate processes across the stack. For larger companies, we see their interest in the category as a way to take action on data within their systems. And for standalone RPA vendors, we see this as validation of the category and an invitation to expand their offerings to other pillars of automation,” Thakker said.

The activity we have seen across the automation and workflow space over the last year could be just the beginning of what Thakker and Tornbohm are describing, as companies of all sizes fight to become the automation stack of choice in the coming years.


By Ron Miller

ServiceNow takes RPA plunge by acquiring India-based startup Intellibot

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.

 


By Ron Miller

How artificial intelligence will be used in 2021

Scale AI CEO Alexandr Wang doesn’t need a crystal ball to see where artificial intelligence will be used in the future. He just looks at his customer list.

The four-year-old startup, which recently hit a valuation of more than $3.5 billion, got its start supplying autonomous vehicle companies with the labeled data needed to train machine learning models to develop and eventually commercialize robotaxis, self-driving trucks and automated bots used in warehouses and on-demand delivery.

The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses.

In 2020, that changed as e-commerce, enterprise automation, government, insurance, real estate and robotics companies turned to Scale’s visual data labeling platform to develop and apply artificial intelligence to their respective businesses. Now, the company is preparing for the customer list to grow and become more varied.

How 2020 shaped up for AI

Scale AI’s customer list has included an array of autonomous vehicle companies including Alphabet, Voyage, nuTonomy, Embark, Nuro and Zoox. While it began to diversify with additions like Airbnb, DoorDash and Pinterest, there were still sectors that had yet to jump on board. That changed in 2020, Wang said.

Scale began to see incredible use cases of AI within the government as well as enterprise automation, according to Wang. Scale AI began working more closely with government agencies this year and added enterprise automation customers like States Title, a residential real estate company.

Wang also saw an increase in uses around conversational AI, in both consumer and enterprise applications as well as growth in e-commerce as companies sought out ways to use AI to provide personalized recommendations for its customers that were on par with Amazon.

Robotics continued to expand as well in 2020, although it spread to use cases beyond robotaxis, autonomous delivery and self-driving trucks, Wang said.

“A lot of the innovations that have happened within the self-driving industry, we’re starting to see trickle out throughout a lot of other robotics problems,” Wang said. “And so it’s been super exciting to see the breadth of AI continue to broaden and serve our ability to support all these use cases.”

The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses, Wang said, adding that advancements in natural language processing of text, improved offerings from cloud companies like AWS, Azure and Google Cloud and greater access to datasets helped sustain this trend.

“We’re finally getting to the point where we can help with computational AI, which has been this thing that’s been pitched for forever,” he said.

That slow burn heated up with the COVID-19 pandemic, said Wang, noting that interest has been particularly strong within government and enterprise automation as these entities looked for ways to operate more efficiently.

“There was this big reckoning,” Wang said of 2020 and the effect that COVID-19 had on traditional business enterprises.

If the future is mostly remote with consumers buying online instead of in-person, companies started to ask, “How do we start building for that?,” according to Wang.

The push for operational efficiency coupled with the capabilities of the technology is only going to accelerate the use of AI for automating processes like mortgage applications or customer loans at banks, Wang said, who noted that outside of the tech world there are industries that still rely on a lot of paper and manual processes.


By Kirsten Korosec

Salesforce applies AI to workflow with Einstein Automate

While Salesforce made a big splash yesterday with the announcement that it’s buying Slack for $27.7 billion, it’s not the only thing going on for the CRM giant this week. In fact Dreamforce, the company’s customer extravaganza is also on the docket. While it is virtual this year, there are still product announcements aplenty and today the company announced Einstein Automate, a new AI-fueled set of workflow solutions.

Sarah Franklin, EVP & GM of Platform, Trailhead and AppExchange at Salesforce says that she is seeing companies facing a digital imperative to automate processes as things move ever more quickly online, being driven there even faster by the pandemic. “With Einstein Automate, everyone can change the speed of work and be more productive through intelligent workflow automation,” she said in a statement.

Brent Leary, principal analyst at CRM Essentials says that combined these tools are designed to help customers get to work more quickly. “It’s not only about identifying the insight, it’s about making it easier to leverage it at the the right time. And this should make it easier for users to do it without spending more time and effort,” Leary told TechCrunch.

Einstein is the commercial name given to Salesforce’s artificial intelligence platform that touches every aspect of the company’s product line, bringing automation to many tasks and making it easier to find the most valuable information on customers, which is often buried in an avalanche of data.

Einstein Automate encompasses several products designed to improve workflows inside organizations. For starters, the company has created Flow Orchestrator, a tool that uses a low-code, drag and drop approach for building workflows, but it doesn’t stop there. It also relies on AI to provide help suggest logical next steps to speed up workflow creation.

Salesforce is also bringing Mulesoft, the integration company it bought for $6.5 billion in 2018 into the mix. Instead of processes like a mortgage approval workflow, the Mulesoft piece lets IT build complex integrations between applications across the enterprise, and the Salesforce family of products more easily.

To make it easier to build these workflows, Salesforce is announcing the Einstein Automate collection page available in AppExchange, the company’s application marketplace. The collection includes over 700 pre-built connectors so customers can grab and go as they build these workflows, and finally it’s updating the OmniStudio, their platform for generating customer experiences. As Salesforce describes it, “Included in OmniStudio is a suite of resources and no-code tools, including pre-built guided experiences, templates and more, allowing users to deploy digital-first experiences like licensing and permit applications quickly and with ease. ”

Per usual with Salesforce Dreamforce announcements, the Flow Orchestrator being announced today won’t be available in beta until next summer. The Mulesoft component will be available in early 2021, but the OmniStudio updates and the Einstein connections collection are available today.


By Ron Miller

Microsoft brings new robotic process automation features to its Power Platform

Earlier this year, Microsoft acquired Softomotive, a player in the low-code robotic process automation space with a focus on Windows. Today, at its Ignite conference, the company is launching Power Automate Desktop, a new application based on Softomotive’s technology that lets anyone automate desktop workflows without needing to program.

“The big idea of Power Platform is that we want to go make it so development is accessible to everybody,” Charles Lamanna, Microsoft’s corporate VP for its low-code platform, told me. “And development includes understanding and reporting on your data with Power BI, building web and mobile applications with Power Apps, automating your tasks — whether it’s through robotic process automation or workflow automation — with Power Automate, or building chatbots and chat-based experiences with Power Virtual Agent.”

Power Automate already allowed users to connect web-based applications, similar to Zapier and IFTTT, but the company also launched a browser extension late last year to help users connect native system components to Power Automate. Now, with the integration of the Softomotive technology and the launch of this new low-code Windows application, it’s taking this integration into the native Windows user interface one step further.

“Everything still runs in the cloud and still connects to the cloud, but you now have a rich desktop application to author and record your UI automations,” Lamanna explained. He likened it to an “ultimate connector,” noting that the “ultimate API is just the UI.”

He also stressed that the new app feels like any other modern Office app, like Outlook (which is getting a new Mac version today, by the way) or Word. And like the modern versions of those apps, Power Automate Desktop derives a lot of its power from being connected to the cloud.

It’s also worth noting that Power Automate isn’t just a platform for automating simple two or three-step processes (like sending you a text message when your boss emails you), but also for multistep, business-critical workflows. T-Mobile, for example, is using the platform to automate some of the integration processes between its systems and Sprint.

Lamanna noted that for some large enterprises, adopting these kinds of low-code services necessitates a bit of a culture shift. IT still needs to have some insights into how these tools are used, after all, to ensure that data is kept safe, for example.

Another new feature the company announced today is an integration between the Power Platform and GitHub, which is now in public preview. The idea here is to give developers the ability to create their own software lifecycle workflows. “One of the core ideas of Power Platform is that it’s low code,” Lamanna said. “So it’s built first for business users, business analysts, not the classical developers. But pro devs are welcome. The saying I have is: we’re throwing a party for business users, but pro devs are also invited to the party.” But to get them onto the platform, the team wants to meet them where they are and let them use the tools they already use — and that’s GitHub (and Visual Studio and Visual Studio Code).


By Frederic Lardinois

Chef goes 100% open source

Chef, the popular automation service, today announced that it is open sourcing all of its software under the Apache 2 license. Until now, Chef used an open core model with a number of proprietary products that complemented its open-source tools. Most of these proprietary tools focused on enterprise users and their security and deployment needs. Now, all of these tools, which represent somewhere between a third and half of Chef’s total code base, are open source, too.

“We’re moving away from our open core model,” Chef SVP of products and engineering Corey Scobie told me. “We’re now moving to exclusively open source software development.”

He added that this also includes open product development. Going forward, the company plans to share far more details about its roadmap, feature backlogs and other product development details. All of Chef’s commercial offerings will also be built from the same open source code that everybody now has access to.

Scobie noted that there are a number of reasons why the company is doing this. He believes, for example, that the best way to build software is to collaborate in public with those who are actually using it.

“With that philosophy in mind, it was really easy to justify how we’d take the remainder of the software that we product and make it open source,” Scobie said. “We believe that that’s the best way to build software that works for people — real people in the real world.”

Another reason, Scobie said, is that it was becoming increasingly difficult for Chef to explain which parts of the software were open source and which were not. “We wanted to make that conversation easier, to be perfectly honest.”

Chef’s decision comes during a bit of a tumultuous time in the open source world. A number of companies like Redis, MongoDB and Elasic have recently moved to licenses that explicitly disallow the commercial use of their open source products by large cloud vendors like AWS unless they also buy a commercial license.

But here is Chef, open sourcing everything. Chef co-founder and board member Adam Jacob doesn’t think that’s a problem. “In the open core model, you’re saying that the value is in this proprietary sliver. The part you pay me for is this sliver of its value. And I think that’s incorrect,” he said. “I think, in fact, the value was always in the totality of the product.”

Jacob also argues that those companies that are moving to these new, more restrictive licenses, are only hurting themselves. “It turns out that the product was what mattered in the first place,” he said. “They continue to produce great enterprise software for their customers and their customers continue to be happy and continue to buy it, which is what they always would’ve done.” He also noted that he doesn’t think AWS will ever be better at running Elasticsearch than Elastic or, for that matter, at running Chef better than Chef.

It’s worth noting that Chef also today announced the launch of its Enterprise Automation Stack, which brings together all of Chef’s tools (Chef Automate, Infra, InSpec, Habitat and Workstation) under a unified umbrella.

“Chef is fully committed to enabling organizations to eliminate friction across the lifecycle of all of their applications, ensuring that, whether they build their solutions from our open source code or license our commercial distribution, they can benefit from collaboration as code,” said Chef CEO Barry Crist. “Chef Enterprise Automation Stack lets teams establish and maintain a consistent path to production for any application, in order to increase velocity and improve efficiency, so deployment and updates of mission-critical software become easier, move faster and work flawlessly.”


By Frederic Lardinois

Trello acquires Butler to add power of automation

Trello, the organizational tool owned by Atlassian, announced an acquisition of its very own this morning when it bought Butler for an undisclosed amount.

What Butler brings to Trello is the power of automation, stringing together a bunch of commands to make something complex happen automatically. As Trello’s Michael Pryor pointed out in a blog post announcing the acquisition, we are used to tools like IFTTT, Zapier and Apple Shortcuts, and this will bring a similar type of functionality directly into Trello.

Screenshot: Trello

“Over the years, teams have discovered that by automating processes on Trello boards with the Butler Power-Up, they could spend more time on important tasks and be more productive. Butler helps teams codify business rules and processes, taking something that might take ten steps to accomplish and automating it into one click.” Pryor wrote.

This means that Trello can be more than a static organizational tool. Instead, it can move into the realm of light-weight business process automation. For example, this could allow you to move an item from your To Do board to your Doing board automatically based on dates, or to share tasks with appropriate teams as a project moves through its lifecycle, saving a bunch of manual steps that tend to add up.

The company indicated that it will be incorporating the Alfred’s capabilities directly into Trello in the coming months. It will make it available to all level of users including the free tier, but they promise more advanced functionality for Business and Enterprise customers when the integration is complete. Pryor also suggested that more automation could be coming to Trello. “Butler is Trello’s first step down this road, enabling every user to automate pieces of their Trello workflow to save time, stay organized and get more done.”

Atlassian bought Trello in 2017 for $425 million, but this acquisition indicates it is functioning quasi-independently as part of the Atlassian family.


By Ron Miller

HashiCorp scores $100M investment on $1.9 billion valuation

HashiCorp, the company that has made hay developing open source tools for managing cloud infrastructure, obviously has a pretty hefty commercial business going too. Today the company announced an enormous $100 million round on a Unicorn valuation of $1.9 billion.

The round was led by IVP, whose investments include AppDynamics, Slack and Snap. New comer Bessemer Venture Partners joined existing investors GGV Capital, Mayfield, Redpoint Ventures, and True Ventures in the round. Today’s investment brings the total raised to $179 million.

The company’s open source tools have been downloaded 45 million times, according to data provided by the company. It has used that open source base to fuel the business (as many have done before).

“Because practitioners choose technologies in the cloud era, we’ve taken an open source-first approach and partnered with the cloud providers to enable a common workflow for cloud adoption. Commercially, we view our responsibility as a strategic partner to the Global 2000 as they adopt hybrid and multi-cloud. This round of funding will help us accelerate our efforts,” company CEO Dave McJannet said in a statement.

To keep growing, it needs to build out its worldwide operations and that requires big bucks. In addition, as the company scales that means adding staff to beef up customer success, support and training teams. The company plans on making investments in these areas with the new funding.

HashiCorp launched in 2012. It was the brainchild of two college students, Mitchell Hashimoto and Armon Dadgar, who came up with the idea of what would become HashiCorp while they were still at the University of Washington. As I wrote in 2014 on the occasion of their $10 million Series A round:

“After graduating and getting jobs, Hashimoto and Dadgar reunited in 2012 and launched HashiCorp . They decided to break their big problem down into smaller, more manageable pieces and eventually built the five open source tools currently on offer. In fact, they found as they developed each one, the community let them know about adjacent problems and they layered on each new tool to address a different need.”

HashiCorp has continued to build on that early vision, layering on new tools over the years. It is not alone in building a business on top of open source and getting rewarded for their efforts. Just this morning, Neo4j, a company that built a business on top of its open source graph database project announced an $80 million Series E investment.


By Ron Miller

UIpath lands $225M Series C on $3 billion valuation as robotics process automation soars

UIPath is bringing automation to repetitive processes inside large organizations and it seems to have landed on a huge pain point. Today it announced a massive $225 million Series C on a $3 billion valuation.

The round was led by CapitalG and Sequoia Capital. Accel, which invested in the companies A and B rounds also participated. Today’s investment brings the total raised to $408 million, according to Crunchbase, and comes just months after a $153 million Series B we reported on last March. At that time, it had a valuation of over $1 billion, meaning the valuation has tripled in less than six months.

There’s a reason this company you might have never heard of is garnering this level of investment so quickly. For starters, it’s growing in leaps in bounds. Consider that it went from $1 million to $100 million in annual recurring revenue in under 21 months, according to the company. It currently has 1800 enterprise customers and claims to be adding 6 new ones a day, an astonishing rate of customer acquisition.

The company is part of the growing field of robotics process automation or RPA . While the robotics part of the name could be considered a bit of a misnomer, the software helps automate a series of mundane tasks that were typically handled by humans. It allows companies to bring a level of automation to legacy processes like accounts payable, employee onboarding, procurement and reconciliation without actually having to replace legacy systems.

Phil Fersht, CEO and chief analyst at HFS, a firm that watches the RPA market, says RPA isn’t actually that intelligent. “It’s about taking manual work, work-arounds and integrated processes built on legacy technology and finding way to stitch them together,” he told TechCrunch in an interview earlier this year.

It isn’t quite as simple as the old macro recorders that used to record a series of tasks and execute them with a keystroke, but it is somewhat analogous to that approach. Today, it’s more akin to a bot that may help you complete a task in Slack. RPA is a bit more sophisticated moving through a workflow in an automated fashion.

Ian Barkin from Symphony Ventures, a firm that used to do outsourcing, has embraced RPA. He says while most organizations have a hard time getting a handle on AI, RPA allows them to institute fundamental change around desktop routines without having to understand AI.

If you’re worrying about this technology replacing humans, it is somewhat valid, but Barkin says the technology is replacing jobs that most humans don’t enjoy doing. “The work people enjoy doing is exceptions and judgment based, which isn’t the sweet spot of RPA. It frees them from mundaneness of routine,” he said in an interview last year.

Whatever it is, it’s resonating inside large organizations and UIpath, is benefiting from the growing need by offering its own flavor of RPA. Today its customers include the likes of Autodesk, BMW Group and Huawei.

As it has grown over the last year, the number of employees has increased 3x  and the company expects to reach 1700 employees by the end of the year.


By Ron Miller