Personal blog of Yzmir Ramirez

UiPath came seemingly out of nowhere in the last several years, going public last year in a successful IPO during which it raised more than $527 million. It raised $2 billion in private money prior to that with its final private valuation coming in at an amazing $35 billion. UiPath CEO Daniel Dines will be joining us on a panel to discuss automation at TC Sessions: SaaS on October 27th.

The company has been able to capture all this investor attention doing something called robotic process automation (RPA), which provides a way to automate a series of highly mundane tasks. It has become quite popular, especially to help bring a level of automation to legacy systems that might not be able to handle more modern approaches to automation involving artificial intelligence and machine learning. In 2019 Gartner found that RPA was the fastest growing category in enterprise software.

In point of fact, UiPath didn’t actually come out of nowhere. It was founded in 2005 as a consulting company and transitioned to software over the years. The company took its first VC funding, a modest $1.5 million seed round, in 2015, according to Crunchbase data.

As RPA found its market, the startup began to take off, raising gobs of money, including a $568 million round in April 2019 and $750 million in its final private raise in February 2021.

Dines will be appearing on a panel discussing the role of automation in the enterprise. Certainly, the pandemic drove home the need for increased automation as masses of office workers moved to work from home, a trend that is likely to continue even after the pandemic slows.

As the RPA market leader, he is uniquely positioned to discuss how this software and other similar types will evolve in the coming years and how it could combine with related trends like no-code and process mapping. Dines will be joined on the panel by investor Laela Sturdy from CapitalG and ServiceNow’s Dave Wright, where they will discuss the state of the automation market, why it’s so hot and where the next opportunities could be.

In addition to our discussion with Dines, the conference will also include Databricks’ Ali Ghodsi, Salesforce’s Kathy Baxter and Puppet’s Abby Kearns, as well as investors Casey Aylward and Sarah Guo, among others. We hope you’ll join us. It’s going to be a stimulating day.

Buy your pass now to save up to $100. We can’t wait to see you in October!

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Reliance on a single technology as a lifeline is a futile battle now. When simple automation no longer does the trick, delivering end-to-end automation needs a combination of complementary technologies that can give a facelift to business processes: the digital operations toolbox.

According to a McKinsey survey, enterprises that have likely been successful with digital transformation efforts adopted sophisticated technologies such as artificial intelligence, Internet of Things or machine learning. Enterprises can achieve hyperautomation with the digital ops toolbox, the hub for your digital operations.

The toolbox is a synchronous medley of intelligent business process management (iBPM), robotic process automation (RPA), process mining, low code, artificial intelligence (AI), machine learning (ML) and a rules engine. The technologies can be optimally combined to achieve the organization’s key performance indicator (KPI) through hyperautomation.

The hyperautomation market is burgeoning: Analysts predict that by 2025, it will reach around $860 billion. Let’s see why.

The purpose of a digital ops toolbox

The toolbox, the treasure chest of technologies it is, helps with three crucial aspects: process automation, orchestration and intelligence.

Process automation: A hyperautomation mindset introduces the world of “automating anything that can be,” whether that’s a process or a task. If something can be handled by bots or other technologies, it should be.

Orchestration: Hyperautomation, per se, adds an orchestration layer to simple automation. Technologies like intelligent business process management orchestrate the entire process.

Intelligence: Machines can automate repetitive tasks, but they lack the decision-making capabilities of humans. And, to achieve a perfect harmony where machines are made to “think and act,” or attain cognitive skills, we need AI. Combining AI, ML and natural language processing algorithms with analytics propels simple automation to become more cognitive. Instead of just following if-then rules, the technologies help gather insights from the data. The decision-making capabilities enable bots to make decisions.

Simple automation versus hyperautomation

Here’s a story of evolving from simple automation to hyperautomation with an example: an order-to-cash process.

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.

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.

After an upward revision, UiPath priced its IPO last night at $56 per share, a few dollars above its raised target range. The above-range price meant that the unicorn put more capital into its books through its public offering.

For a company in a market as competitive as robotic process automation (RPA), the funds are welcome. In fact, RPA has been top of mind for startups and established companies alike over the last year or so. In that time frame, enterprise stalwarts like SAP, Microsoft, IBM and ServiceNow have been buying smaller RPA startups and building their own, all in an effort to muscle into an increasingly lucrative market.

In June 2019, Gartner reported that RPA was the fastest-growing area in enterprise software, and while the growth has slowed down since, the sector is still attracting attention. UIPath, which Gartner found was the market leader, has been riding that wave, and today’s capital influx should help the company maintain its market position.

It’s worth noting that when the company had its last private funding round in February, it brought home $750 million at an impressive valuation of $35 billion. But as TechCrunch noted over the course of its pivot to the public markets, that round valued the company above its final IPO price. As a result, this week’s $56-per-share public offer wound up being something of a modest down-round IPO to UiPath’s final private valuation.

Then, a broader set of public traders got hold of its stock and bid its shares higher. The former unicorn’s shares closed their first day’s trading at precisely $69, above the per-share price at which the company closed its final private round.

So despite a somewhat circuitous route, UiPath closed its first day as a public company worth more than it was in its Series F round — when it sold 12,043,202 shares at $62.27576 apiece, per SEC filings. More simply, UiPath closed today worth more per-share than it was in February.

How you might value the company, whether you prefer a simple or fully diluted share count, is somewhat immaterial at this juncture. UiPath had a good day.

While it’s hard to know what the company might do with the proceeds, chances are it will continue to try to expand its platform beyond pure RPA, which could become market-limited over time as companies look at other, more modern approaches to automation. By adding additional automation capabilities — organically or via acquisitions — the company can begin covering broader parts of its market.

TechCrunch spoke with UiPath CFO Ashim Gupta today, curious about the company’s choice of a traditional IPO, its general avoidance of adjusted metrics in its SEC filings, and the IPO market’s current temperature. The final question was on our minds, as some companies have pulled their public listings in the wake of a market described as “challenging.”

Why did UiPath not direct list after its huge February raise?

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”.

No-code startups continue to see a lot of traction among enterprises, where employees — strictly speaking, non-technical, but still using software every day — are getting hands-on and building apps to take on some of the more repetitive aspects of their jobs, the so-called “citizen coders” of the working world.

And in one of the latest developments, Bryter — an AI-based no-code startup that has built a platforms used by some 100 global enterprises to date across some 2,000 business applications and workflows — is announcing a new round of funding to double down on that opportunity. The Berlin-based company has closed a Series B of $66 million, money that it will be investing into its platform and expanding in the U.S. out of a New York office it opened last year. The funding comes on the heels of seeing a lot of demand for its tools, CEO and co-founder Michael Grupp said in an interview.

“It was a great year for low-code and no-code platforms,” said Grupp, who co-founded the company with Micha-Manuel Bues and Michael Hübl. “What everyone has realized is that most people don’t actually care about the tech. They only care about the use cases. They want to get things done.” Customers using the service include the likes of McDonald’s, Telefónica, PwC, KPMG and Deloitte in Europe, as well as banks, healthcare and industrial enterprises.

Tiger Global is leading this round, with previous backers Accel, Dawn Capital, Notion Capital and Cavalry Ventures also participating, along with a number of individual backers (they include Amit Agarwal, CPO of Datadog; Lars Björk, former CEO of Qlik; Ulf Zetterberg, founder and CEO of Seal Software; and former ServiceNow global SVP James Fitzgerald). The valuation is not being disclosed; Bryter has raised around $90 million to date.

Accel and Dawn co-led Bryter’s Series A of $16 million less than a year ago, in June 2020, a rapid funding pace that underscores both interest in the no-code/low-code space — Bryter’s enterprise customer base has doubled from 50 since then — and the fact that startups in it are striking while the iron is hot.

Bryter’s not the only one: Airtable, Genesis, Rows, Creatio and Ushur are among the many startups building “hands-on tech creation for non-techie people” that have raised money in the last several months.

Automation has been the bigger trend that has propelled a lot of this activity. Knowledge workers spend most of their time these days in apps — a state of affairs that pre-dates the COVID-19 pandemic, but has definitely been furthered throughout it. While some of that work still requires manual involvement and evaluation from those workers, software has automated large swathes of those jobs.

RPA — robotic process automation, where companies like UiPath, Automation Anywhere and Blue Prism have taken a big lead — has accounted for a significant chunk of that activity, especially when it comes to reading forms and lots of data entry. But there remains a lot of other transactions and activities within specific apps where RPA is typically not used (not yet at least!). And this is where non-tech workers are finding that no-code tools like Bryter, which use artificial intelligence to deliver more personalised, yet scalable, automation, can play a very useful role.

“We sit on top of RPA in many cases,” said Grupp.

The company says that business functions where its platform has been implemented include compliance, legal, tax, privacy and security, procurement, administration and HR, and the kinds of features that are being built include virtual assistants, chatbots, interactive self-service tools and more.

These don’t replace people as such, but cut down the time they need to spend in specific tasks to process and handle information within them, and could in theory also be used to build tools for customers to interact with services more easily, cutting down on the amount of time that agents are getting details and handling engagements.

That scalability and the rapid customer up-take from a pool of users that extends beyond tech early adopters are part of what attracted the funding.

“Bryter has all the characteristics of a top-tier software company: high quality product that solves a real customer pain point, a large market opportunity and a world-class founding team,” said John Curtius, a partner at Tiger Global, in a statement. “The feedback from Bryter’s customers was resoundingly positive in our research, and we are excited to see the company reach new heights over the coming years.”

“Bryter has seen explosive growth over the last year, signing landmark customers across a large number of sectors and use cases. This does not come as a surprise. In the pandemic-affected world, digitalisation is no longer a nice to have, it is an imperative,” added Evgenia Plotnikova, a partner at Dawn Capital.

When UIPath filed its S-1 last week, it was a watershed moment for the robotic process automation (RPA) market. The company, which first appeared on our radar for a $30 million Series A in 2017, has so far raised an astonishing $2 billion while still private. In February, it was valued at $35 billion when it raised $750 million in its latest round.

RPA and process automation came to the fore during the pandemic as companies took steps to digitally transform. When employees couldn’t be in the same office together, it became crucial to cobble together more automated workflows that required fewer people in the loop.

When UIPath raised money in 2017, RPA was not well known in enterprise software circles even though it had already been around for several years. The category was gaining in popularity by that point because it addressed automation in a legacy context. That meant companies with deep legacy technology — practically everyone not born in the cloud — could automate across older platforms without ripping and replacing, an expensive and risky undertaking that most CEOs would rather not take.

RPA has enabled executives to provide a level of workflow automation, a taste of the modern. It essentially buys them time to update systems to more modern approaches while reducing the large number of mundane manual tasks that are part of just about every industry’s workflow.

While some people point to RPA as job-elimination software, it also provides a way to liberate people from some of the most mind-numbing and mundane chores in the organization. The argument goes that this frees up employees for higher level tasks.

As an example, RPA could take advantage of older workflow technologies like OCR (optical character recognition) to read a number from a form, enter the data in a spreadsheet, generate an invoice, send it for printing and mailing, and generate a Slack message to the accounting department that the task has been completed.

We’re going to take a deep dive into RPA and the larger process automation space — explore the market size and dynamics, look at the key players and the biggest investors, and finally, try to chart out where this market might go in the future.

Meet the vendors

UIPath is clearly an RPA star with a significant market share lead of 27.1%, according to IDC. Automation Anywhere is in second place with 19.4%, and Blue Prism is third with 10.3%, based on data from IDC’s July 2020 report, the last time the firm reported on the market.

Two other players with significant market share worth mentioning are WorkFusion with 6.8%, and NTT with 5%.

When TechCrunch covered UIPath’s Series A in 2017, it was a small startup out of Romania working in a little known area of enterprise software called robotic process automation (RPA).

Then the company took off with increasingly large multibillion dollar valuations. It progressed through its investment rounds, culminating with a $750 million round on an eye-popping $35 billion valuation last month.

This morning, the company took the next step on its rapid-fire evolutionary path when it filed its S-1 to go public. To illustrate just how fast the company’s rise has been, take a look at its funding history:

Image Credits: Bryce Durbin/TechCrunch

RPA is much better understood these days with larger enterprise software companies like SAP, Microsoft, IBM and ServiceNow getting involved. With RPA, companies can automate a mundane process like processing an insurance claim, moving work automatically, while bringing in humans only when absolutely necessary. For example, instead of having a person enter a number in a spreadsheet from an email, that can happen automatically.

In June 2019, Gartner reported that RPA was the fastest-growing area in enterprise software, growing at over 60% per year, and attracting investors and larger enterprise software vendors to the space. While RPA’s growth has slowed as it matures, a September 2020 Gartner report found it expanding at a more modest 19.5% with total revenue expected to reach $2 billion in 2021. Gartner found that stand-alone RPA vendors UIPath, Blue Prism and Automation Anywhere are the market leaders.

Although the market feels rather small given the size of the company’s valuation, it’s still a nascent space. In its S-1 filing this morning, the company painted a rosy picture, projecting a $60 billion addressable market. While TAM estimates tend to trend large, UIPath points out that the number encompasses far more than pure RPA into what they call “Intelligent Process Automation.” That could include not only RPA, but also process discovery, workflow, no-code development and other forms of automation.

Indeed, as we wrote earlier today on the soaring process automation market, the company is probably going to need to expand into these other areas to really grow, especially now that it’s competing with much bigger companies for enterprise automation dollars.

While UIPath is in the midst of its quiet period, it came up for air this week to announce that it had bought Cloud Elements, a company that gives it access to API integration, an important component of automation in the enterprise. Daniel Dines, the company co-founder and CEO said the acquisition was about building a larger platform of automation tools.

“The acquisition of Cloud Elements is just one example of how we are building a flexible and scalable enterprise-ready platform that helps customers become fully automated enterprises,” he said in a statement.

While there is a lot of CEO speak in that statement, there is also an element of truth in that the company is looking at the larger automation story. It can use some of the cash from its prodigious fundraising to begin expanding on its original vision with smaller acquisitions that can fill in missing pieces in the product road map.

The company will need to do that and more to compete in a rapidly moving market, where many vendors are fighting for different parts of the business. As it continues its journey to becoming a public company, it will need to continue finding new ways to increase revenue by tapping into different parts of the wider automation stack.

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.”

Robotic process automation has taken the enterprise world by storm by providing a set of tools for those doing repetitive, volume-based tasks to use software to remove some of that labor to let those people focus on more complicated tasks. Today a startup that’s taken some of that ethos and is applying it to more individualized work — that of salespeople — is announcing some funding.

Dooly, a Vancouver, Canada-based startup that has built a set of AI-based tools that automate the busywork that goes into updating data in their sales software, and namely Salesforce, has picked up $20 million in funding to build out its business, which to date has picked up a number of customers among the sales teams of enterprise-focused software companies. They include Airtable, Asana, Intercom, Contentful, Vidyard, BigCommerce, Liftoff and CrowdRiff.

Its aim is to make sales software more useful for salespeople by eliminating the work that goes into inputting data into those systems.

“Really they’ve just created a mountain of virtual filing cabinets,” Kris Hartvigsen, Dooly’s founder and CEO, said in an emailed interview with me. “Filing cabinets just wait for drawers to be opened — or in the case of enterprise software, reports to be pulled and data to be input. We know people are capturing information across the business and our job is to make sure that the people and systems across the business have a better, faster, more far-reaching way of staying informed.”

The funding is being announced today, but it was actually raised in two tranches that had not previously been disclosed. A $3.3 million seed round was led by Boldstart Ventures and also included BoxGroup. Its $17 million Series A, meanwhile, was led by Addition, with Boldstart and BoxGroup again participating, along with Battery Ventures, Mantis (representing musicians The Chainsmokers) and SV Angel.

Alongside the VCs, there are a number of interesting strategic individual investors, too. Daniel Dines and Brandon Deer of UiPath (the RPA connection clearly is not one that I’m imagining!); Allison Pickens, the ex-COO of Gainsight; Zander Lurie of SurveyMonkey); Jay Simons, ex-CEO of Atlassian); Harry Stebbings; and other unnamed investors are all also involved. Ed Sim of Boldstart is joining Dooly’s board of directors with this announcement.

The challenge that Dooly has been built to solve is that while there are a lot of tools out there now to help salespeople source leads, manage the progress of their sales, give them advice and other helpful material to supplement their charm and the basic strength of a product, manage customers once they’ve signed on, and so on, all of them still require something important to work: a time commitment from salespeople to keep them updated with information. Ironically, the more tools to help them that are built, the more time salespeople need to spend feeding them data.

Even more ironically, one of the big daddies of the problem — the somewhat overweight Salesforce — has published figures (cited by Dooly) that say salespeople spend just 34% of their time selling. The rest (minus trips to get coffee to stay caffeinated) seems to be about data entry.

The idea with Dooly is that you turn it on, connect it to what you are using — starting with Salesforce — and Dooly lets you make notes which it then organises and puts into the right places in the rest of your apps.

“When a salesperson starts using Dooly, the ‘aha moment’ is pretty immediate,” Hartvigsen said. “Whether they want to do quick pipeline edits or push their notes to Salesforce, we don’t ask the user to learn any new patterns they aren’t familiar with, we just automate a bunch of things they hate doing, often comparing those traditional chores to clerical work.” For example, he notes, when they sync a note, Dooly automatically updates any Salesforce with any contacts found in the meeting, updates fields, adds to-dos, logs activities, and pushes messages to the appropriate internal stakeholders on Slack, all in the same motion.

The product currently also integrates with Slack, G-Cal and G-Drive, because, Hartvigsen said, “we see this as an area where there is the most immediate friction and an area that was in need of disruption.” He added that the plan is to add more integrations over time. “We see need to expand the solutions that anchor to our connected workspace, with our near-term focus being the systems that touch revenue teams,” he said.

The design of Dooly seems to be about investing a little in order to save more. On average people are using Dooly between 2.5 and 5 hours each day, but Hartvigsen claims that right now the system helps people make up for more hours each week in lost productivity. Its pricing starts at $25 per user per month, going up depending on features and use.

There are quite literally thousands of products out in the market today, and among them hundreds of strong ones, being built to help salespeople with different aspects of getting their jobs done. I’ve written about quite a few of them, and I’ve actually asked companies about whether they are tackling the very issue that Dooly has identified and is trying to fix.

They weren’t, but that doesn’t mean that they won’t. Chief among them are companies like UiPath and Salesforce, which sit on different sides of this problem and could well move into it as they keep growing. (Having UiPath as a backer by way of its founder and a senior executive points to a relationship there, which is interesting.)

In the meantime, there have been some other interesting innovations using AI to improve the sales process, with companies like Pipedrive, Clari, Seismic, Chorus.ai and Gong all using natural language, machine learning and big data analytics (itself helped by AI) to improve how sales get done.

“The first thing we noticed when we met the Dooly team was the thoughtful design-first approach to product that engendered tons of customer love. This love was inherent not only on popular ratings sites like G2 Crowd but also in the individual usage and viral adoption throughout companies with only one initial user,” said Ed Sim, founder and managing partner at Boldstart Ventures in a statement. “Dooly is revolutionizing the note-taking experience for customer facing end users from sales to customer success to product.”

“Dooly is relentlessly focused on building a user-first experience for its customers to seamlessly create workflows and unlock new revenue opportunities,” said Lee Fixel, founder of Addition, added. “We are thrilled to support Dooly as it continues to scale and enhance the sales function for more businesses.”