Jun
17
2021
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How to launch a successful RPA initiative

Robotic process automation (RPA) is rapidly moving beyond the early adoption phase across verticals. Automating just basic workflow processes has resulted in such tremendous efficiency improvements and cost savings that businesses are adapting automation at scale and across the enterprise.

While there is a technical component to robotic automation, RPA is not a traditional IT-driven solution. It is, however, still important to align the business and IT processes around RPA. Adapting business automation for the enterprise should be approached as a business solution that happens to require some technical support.

A strong working relationship between the CFO and CIO will go a long way in getting IT behind, and in support of, the initiative rather than in front of it.

A strong working relationship between the CFO and CIO will go a long way in getting IT behind, and in support of, the initiative rather than in front of it.

More important to the success of a large-scale RPA initiative is support from senior business executives across all lines of business and at every step of the project, with clear communications and an advocacy plan all the way down to LOB managers and employees.

As we’ve seen in real-world examples, successful campaigns for deploying automation at scale require a systematic approach to developing a vision, gathering stakeholder and employee buy-in, identifying use cases, building a center of excellence (CoE) and establishing a governance model.

Create an overarching vision

Your strategy should include defining measurable, strategic objectives. Identify strategic areas that benefit most from automation, such as the supply chain, call centers, AP or revenue cycle, and start with obvious areas where business sees delays due to manual workflow processes. Remember, the goal is not to replace employees; you’re aiming to speed up processes, reduce errors, increase efficiencies and let your employees focus on higher value tasks.

Jun
01
2021
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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 nontechnical 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 a 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 toward 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.”

May
18
2021
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Artificial raises $21M led by Microsoft’s M12 for a lab automation platform aimed at life sciences R&D

Automation is extending into every aspect of how organizations get work done, and today comes news of a startup that is building tools for one industry in particular: life sciences. Artificial, which has built a software platform for laboratories to assist with, or in some cases fully automate, research and development work, has raised $21.5 million.

It plans to use the funding to continue building out its software and its capabilities, to hire more people, and for business development, according to Artificial’s CEO and co-founder David Fuller. The company already has a number of customers including Thermo Fisher and Beam Therapeutics using its software directly and in partnership for their own customers. Sold as aLab Suite, Artificial’s technology can both orchestrate and manage robotic machines that labs might be using to handle some work; and help assist scientists when they are carrying out the work themselves.

“The basic premise of what we’re trying to do is accelerate the rate of discovery in labs,” Fuller said in an interview. He believes the process of bringing in more AI into labs to improve how they work is long overdue. “We need to have a digital revolution to change the way that labs have been operating for the last 20 years.”

The Series A is being led by Microsoft’s venture fund M12 — a financial and strategic investor — with Playground Global and AME Cloud Ventures also participating. Playground Global, the VC firm co-founded by ex-Google exec and Android co-creator Andy Rubin (who is no longer with the firm), has been focusing on robotics and life sciences and it led Artificial’s first and only other round. Artificial is not disclosing its valuation with this round.

Fuller hails from a background in robotics, specifically industrial robots and automation. Before founding Artificial in 2019, he was at Kuka, the German robotics maker, for a number of years, culminating in the role of CTO; prior to that, Fuller spent 20 years at National Instruments, the instrumentation, test equipment and industrial software giant. Meanwhile, Artificial’s co-founder, Nikhita Singh, has insight into how to bring the advances of robotics into environments that are quite analogue in culture. She previously worked on human-robot interaction research at the MIT Media Lab, and before that spent years at Palantir and working on robotics at Berkeley.

As Fuller describes it, he saw an interesting gap (and opportunity) in the market to apply automation, which he had seen help advance work in industrial settings, to the world of life sciences, both to help scientists track what they are doing better, and help them carry out some of the more repetitive work that they have to do day in, day out.

This gap is perhaps more in the spotlight today than ever before, given the fact that we are in the middle of a global health pandemic. This has hindered a lot of labs from being able to operate full in-person teams, and increased the reliance on systems that can crunch numbers and carry out work without as many people present. And, of course, the need for that work (whether it’s related directly to Covid-19 or not) has perhaps never appeared as urgent as it does right now.

There have been a lot of advances in robotics — specifically around hardware like robotic arms — to manage some of the precision needed to carry out some work, but up to now no real efforts made at building platforms to bring all of the work done by that hardware together (or in the words of automation specialists, “orchestrate” that work and data); nor link up the data from those robot-led efforts, with the work that human scientists still carry out. Artificial estimates that some $10 billion is spent annually on lab informatics and automation software, yet data models to unify that work, and platforms to reach across it all, remain absent. That has, in effect, served as a barrier to labs modernising as much as they could.

A lab, as he describes it, is essentially composed of high-end instrumentation for analytics, alongside then robotic systems for liquid handling. “You can really think of a lab, frankly, as a kitchen,” he said, “and the primary operation in that lab is mixing liquids.”

But it is also not unlike a factory, too. As those liquids are mixed, a robotic system typically moves around pipettes, liquids, in and out of plates and mixes. “There’s a key aspect of material flow through the lab, and the material flow part of it is much more like classic robotics,” he said. In other words, there is, as he says, “a combination of bespoke scientific equipment that includes automation, and then classic material flow, which is much more standard robotics,” and is what makes the lab ripe as an applied environment for automation software.

To note: the idea is not to remove humans altogether, but to provide assistance so that they can do their jobs better. He points out that even the automotive industry, which has been automated for 50 years, still has about 6% of all work done by humans. If that is a watermark, it sounds like there is a lot of movement left in labs: Fuller estimates that some 60% of all work in the lab is done by humans. And part of the reason for that is simply because it’s just too complex to replace scientists — who he described as “artists” — altogether (for now at least).

“Our solution augments the human activity and automates the standard activity,” he said. “We view that as a central thesis that differentiates us from classic automation.”

There have been a number of other startups emerging that are applying some of the learnings of artificial intelligence and big data analytics for enterprises to the world of science. They include the likes of Turing, which is applying this to helping automate lab work for CPG companies; and Paige, which is focusing on AI to help better understand cancer and other pathology.

The Microsoft connection is one that could well play out in how Artificial’s platform develops going forward, not just in how data is perhaps handled in the cloud, but also on the ground, specifically with augmented reality.

“We see massive technical synergy,” Fuller said. “When you are in a lab you already have to wear glasses… and we think this has the earmarks of a long-term use case.”

Fuller mentioned that one area it’s looking at would involve equipping scientists and other technicians with Microsoft’s HoloLens to help direct them around the labs, and to make sure people are carrying out work consistently by comparing what is happening in the physical world to a “digital twin” of a lab containing data about supplies, where they are located, and what needs to happen next.

It’s this and all of the other areas that have yet to be brought into our very AI-led enterprise future that interested Microsoft.

“Biology labs today are light- to semi-automated—the same state they were in when I started my academic research and biopharmaceutical career over 20 years ago. Most labs operate more like test kitchens rather than factories,” said Dr. Kouki Harasaki, an investor at M12, in a statement. “Artificial’s aLab Suite is especially exciting to us because it is uniquely positioned to automate the masses: it’s accessible, low code, easy to use, highly configurable, and interoperable with common lab hardware and software. Most importantly, it enables Biopharma and SynBio labs to achieve the crowning glory of workflow automation: flexibility at scale.”

Harasaki is joining Peter Barratt, a founder and general partner at Playground Global, on Artificial’s board with this round.

“It’s become even more clear as we continue to battle the pandemic that we need to take a scalable, reproducible approach to running our labs, rather than the artisanal, error-prone methods we employ today,” Barrett said in a statement. “The aLab Suite that Artificial has pioneered will allow us to accelerate the breakthrough treatments of tomorrow and ensure our best and brightest scientists are working on challenging problems, not manual labor.”

May
07
2021
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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.

Apr
20
2021
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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 spans 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”.

Apr
15
2021
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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 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 with its automation products customers across a range of verticals, including financial, insurance, healthcare and manufacturing.

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 price tag.

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.”

Apr
12
2021
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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.

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 hyperadvanced 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.

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 multidisciplinary 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% 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 five 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 “five 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 transcend 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 $10 million 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.”

Mar
26
2021
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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 toward 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.


Early Stage is the premier “how-to” event for startup entrepreneurs and investors. You’ll hear firsthand how some of the most successful founders and VCs build their businesses, raise money and manage their portfolios. We’ll cover every aspect of company building: Fundraising, recruiting, sales, product-market fit, PR, marketing and brand building. Each session also has audience participation built-in — there’s ample time included for audience questions and discussion. Use code “TCARTICLE” at checkout to get 20% off tickets right here.

Mar
23
2021
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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,” Kahn 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,” Kahn 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. Kahn 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,” Kahn 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.


Early Stage is the premier ‘how-to’ event for startup entrepreneurs and investors. You’ll hear first-hand how some of the most successful founders and VCs build their businesses, raise money and manage their portfolios. We’ll cover every aspect of company-building: Fundraising, recruiting, sales, product market fit, PR, marketing and brand building. Each session also has audience participation built-in – there’s ample time included for audience questions and discussion. Use code “TCARTICLE” at checkout to get 20 percent off tickets right here.

Mar
15
2021
--

DeepSee.ai raises $22.6M Series A for its AI-centric process automation platform

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 helps them leverage AI to turn into actionable insights the plethora of data that businesses now collect.

Image Credits: DeepSee.ai

“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.”

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