Personal blog of Yzmir Ramirez

Work insights platform Fin raised $20 million in Series A funding and brought in Evan Cummack, a former Twilio executive, as its new chief executive officer.

The San Francisco-based company captures employee workflow data from across applications and turns it into productivity insights to improve the way enterprise teams work and remain engaged.

Fin was founded in 2015 by Andrew Kortina, co-founder of Venmo, and Facebook’s former VP of product and Slow Ventures partner Sam Lessin. Initially, the company was doing voice assistant technology — think Alexa but powered by humans and machine learning — and then workplace analytics software in 2020. You can read more about Fin’s origins at the link below.

The new round was led by Coatue, with participation from First Round Capital, Accel and Kleiner Perkins. The original team was talented, but small, so the new funding will build out sales, marketing and engineering teams, Cummack said.

“At that point, the right thing was to raise money, so at the end of last year, the company raised a $20 million Series A, and it was also decided to find a leadership team that knows how to build an enterprise,” Cummack told TechCrunch. “The company had completely pivoted and removed ‘Analytics’ from our name because it was not encompassing what we do.”

Fin’s software measures productivity and provides insights on ways managers can optimize processes, coach their employees and see how teams are actually using technology to get their work done. At the same time, employees are able to manage their workflow and highlight areas where there may be bottlenecks. All combined, it leads to better operations and customer experiences, Cummack said.

Graphic showing how work is really done. Image Credits: Fin

Fin’s view is that as more automation occurs, the company is looking at a “renaissance of human work.” There will be more jobs and more types of jobs, but people will be able to do them more effectively and the work will be more fulfilling, he added.

Particularly with the use of technology, he notes that in the era before cloud computing, there was a small number of software vendors. Now with the average tech company using over 130 SaaS apps, it allows for a lot of entrepreneurs and adoption of best-in-breed apps so that a viable company can start with a handful of people and leverage those apps to gain big customers.

“It’s different for enterprise customers, though, to understand that investment and what they are spending their money on as they use tools to get their jobs done,” Cummack added. “There is massive pressure to improve the customer experience and move quickly. Now with many people working from home, Fin enables you to look at all 130 apps as if they are one and how they are being used.”

As a result, Fin’s customers are seeing metrics like 16% increase in team utilization and engagement, a 25% decrease in support ticket handle time and a 71% increase in policy compliance. Meanwhile, the company itself is doubling and tripling its customers and revenue each year.

Now with leadership and people in place, Cummack said the company is positioned to scale, though it already had a huge head start in terms of a meaningful business.

Arielle Zuckerberg, partner at Coatue, said via email that she was part of a previous firm that invested in Fin’s seed round to build a virtual assistant. She was also a customer of Fin Assistant until it was discontinued.

When she heard the company was pivoting to enterprise, she “was excited because I thought it was a natural outgrowth of the previous business, had a lot of potential and I was already familiar with management and thought highly of them.”

She believed the “brains” of the company always revolved around understanding and measuring what assistants were doing to complete a task as a way to create opportunities for improvement or automation. The pivot to agent-facing tools made sense to Zuckerberg, but it wasn’t until the global pandemic that it clicked.

“Service teams were forced to go remote overnight, and companies had little to no visibility into what people were doing working from home,” she added. “In this remote environment, we thought that Fin’s product was incredibly well-suited to address the challenges of managing a growing remote support team, and that over time, their unique data set of how people use various apps and tools to complete tasks can help business leaders improve the future of work for their team members. We believe that contact center agents going remote was inevitable even before COVID, but COVID was a huge accelerant and created a compelling ‘why now’ moment for Fin’s solution.”

Going forward, Coatue sees Fin as “a process mining company that is focused on service teams.” By initially focusing on customer support and contact center use case — a business large enough to support a scaled, standalone business — rather than joining competitors in going after Fortune 500 companies where implementation cycles are long and there is slow time-to-value, Zuckerberg said Fin is better able to “address the unique challenges of managing a growing remote support team with a near-immediate time-to-value.”

As artificial intelligence continues to weave its way into more enterprise applications, a startup that has built a platform to help businesses, especially non-tech organizations, build more customized AI decision-making tools for themselves has picked up some significant growth funding. Peak AI, a startup out of Manchester, England, that has built a “decision intelligence” platform, has raised $75 million, money that it will be using to continue building out its platform, expand into new markets and hire some 200 new people in the coming quarters.

The Series C is bringing a very big name investor on board. It is being led by SoftBank Vision Fund 2, with previous backers Oxx, MMC Ventures, Praetura Ventures and Arete also participating. That group participated in Peak’s Series B of $21 million, which only closed in February of this year. The company has now raised $119 million; it is not disclosing its valuation.

(This latest funding round was rumored last week, although it was not confirmed at the time and the total amount was not accurate.)

Richard Potter, Peak’s CEO, said the rapid follow-on in funding was based on inbound interest, in part because of how the company has been doing.

Peak’s so-called Decision Intelligence platform is used by retailers, brands, manufacturers and others to help monitor stock levels and build personalized customer experiences, as well as other processes that can stand to have some degree of automation to work more efficiently, but also require sophistication to be able to measure different factors against each other to provide more intelligent insights. Its current customer list includes the likes of Nike, Pepsico, KFC, Molson Coors, Marshalls, Asos and Speedy, and in the last 12 months revenues have more than doubled.

The opportunity that Peak is addressing goes a little like this: AI has become a cornerstone of many of the most advanced IT applications and business processes of our time, but if you are an organization — and specifically one not built around technology — your access to AI and how you might use it will come by way of applications built by others, not necessarily tailored to you, and the costs of building more tailored solutions can often be prohibitively high. Peak claims that those using its tools have seen revenues on average rise 5%, return on ad spend double, supply chain costs reduce by 5% and inventory holdings (a big cost for companies) reduce by 12%.

Peak’s platform, I should point out, is not exactly a “no-code” approach to solving that problem — not yet at least: It’s aimed at data scientists and engineers at those organizations so that they can easily identify different processes in their operations where they might benefit from AI tools, and to build those out with relatively little heavy lifting.

There have also been different market factors that have played a role. COVID-19, for example, and the boost that we have seen both in increasing “digital transformation” in businesses and making e-commerce processes more efficient to cater to rising consumer demand and more strained supply chains have all led to businesses being more open and keen to invest in more tools to improve their automation intelligently.

This, combined with Peak AI’s growing revenues, is part of what interested SoftBank. The investor has been long on AI for a while; but it also has been building out a section of its investment portfolio to provide strategic services to the kinds of businesses in which it invests.

Those include e-commerce and other consumer-facing businesses, which make up one of the main segments of Peak’s customer base.

Notably, one of its recent investments specifically in that space was made earlier this year, also in Manchester, when it took a $730 million stake (with potentially $1.6 billion more down the line) in The Hut Group, which builds software for and runs D2C businesses.

“In Peak we have a partner with a shared vision that the future enterprise will run on a centralized AI software platform capable of optimizing entire value chains,” Max Ohrstrand, senior investor for SoftBank Investment Advisers, said in a statement. “To realize this a new breed of platform is needed and we’re hugely impressed with what Richard and the excellent team have built at Peak. We’re delighted to be supporting them on their way to becoming the category-defining, global leader in Decision Intelligence.”

It’s not clear that SoftBank’s two Manchester interests will be working together, but it’s an interesting synergy if they do, and most of all highlights one of the firm’s areas of interest.

Longer term, it will be interesting to see how and if Peak evolves to extend its platform to a wider set of users at the organizations that are already its customers.

Potter said he believes that “those with technical predispositions” will be the most likely users of its products in the near and medium term. You might assume that would cut out, for example, marketing managers, although the general trend in a lot of software tools has precisely been to build versions of the same tools used by data scientists for these less technical people to engage in the process of building what it is that they want to use.

“I do think it’s important to democratize the ability to stream data pipelines, and to be able to optimize those to work in applications,” Potter added.

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!

Is your company interested in sponsoring or exhibiting at TC Sessions: SaaS 2021? Contact our sponsorship sales team by filling out this form.

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.

Understanding what you will change is most important to achieve a long-lasting and successful robotic process automation transformation. There are three pillars that will be most impacted by the change: people, process and digital workers (also referred to as robots). The interaction of these three pillars executes workflows and tasks, and if integrated cohesively, determines the success of an enterprisewide digital transformation.

Robots are not coming to replace us, they are coming to take over the repetitive, mundane and monotonous tasks that we’ve never been fond of. They are here to transform the work we do by allowing us to focus on innovation and impactful work. RPA ties decisions and actions together. It is the skeletal structure of a digital process that carries information from point A to point B. However, the decision-making capability to understand and decide what comes next will be fueled by RPA’s integration with AI.

We are seeing software vendors adopt vertical technology capabilities and offer a wide range of capabilities to address the three pillars mentioned above. These include powerhouses like UiPath, which recently went public, Microsoft’s Softomotive acquisition, and Celonis, which recently became a unicorn with a $1 billion Series D round. RPA firms call it “intelligent automation,” whereas Celonis targets the execution management system. Both are aiming to be a one-stop shop for all things related to process.

We have seen investments in various product categories for each stage in the intelligent automation journey. Process and task mining for process discovery, centralized business process repositories for CoEs, executives to manage the pipeline and measure cost versus benefit, and artificial intelligence solutions for intelligent document processing.

For your transformation journey to be successful, you need to develop a deep understanding of your goals, people and the process.

Define goals and measurements of success

From a strategic standpoint, success measures for automating, optimizing and redesigning work should not be solely centered around metrics like decreasing fully loaded costs or FTE reduction, but should put the people at the center. To measure improved customer and employee experiences, give special attention to metrics like decreases in throughput time or rework rate, identify vendors that deliver late, and find missed invoice payments or determine loan requests from individuals that are more likely to be paid back late. These provide more targeted success measures for specific business units.

The returns realized with an automation program are not limited to metrics like time or cost savings. The overall performance of an automation program can be more thoroughly measured with the sum of successes of the improved CX/EX metrics in different business units. For each business process you will be redesigning, optimizing or automating, set a definitive problem statement and try to find the right solution to solve it. Do not try to fit predetermined solutions into the problems. Start with the problem and goal first.

Understand the people first

To accomplish enterprise digital transformation via RPA, executives should put people at the heart of their program. Understanding the skill sets and talents of the workforce within the company can yield better knowledge of how well each employee can contribute to the automation economy within the organization. A workforce that is continuously retrained and upskilled learns how to automate and flexibly complete tasks together with robots and is better equipped to achieve transformation at scale.

Everyone from investors to casual LinkedIn observers has more reasons than ever to look at buildings and wonder what’s going on inside. The property industry is known for moving slowly when it comes to adopting new technologies, but novel concepts and products are now entering this market at a dizzying pace.

However, this ever-growing array of smart-building products has made it confusing for professionals who seek to implement digital building platform (DBP) technologies in their spaces, let alone across their entire enterprise. The waters get even murkier when it comes to cloud platforms and their impact on ROI with regard to energy usage and day-to-day operations.

Facility managers, energy professionals and building operators are increasingly hit with daily requests to review the latest platform for managing and operating their buildings. Here are a few tips to help decision-makers clear through the marketing fluff and put DBP platforms to the test.

The why, how and what

Breaking down technology decisions into bite-sized pieces, starting with fundamental functions, is the most straightforward way to cut through the promotional haze. Ask two simple questions: Who on your team will use this technology and what problem will it solve for them? Answers to these questions will help you maintain your key objectives, making it easier to narrow down the hundreds of options to a handful.

Another way to prioritize problems and solutions when sourcing smart-building technology is to identify your use cases. If you don’t know why you need a technology platform for your smart building, you’ll find it difficult to tell which option is better. Further, once you have chosen one, you’ll be hard put to determine if it has been successful. We find use cases draw the most direct line from why to how and what.

For example, let’s examine the why, how and what questions for a real estate developer planning to construct or modernize a commercial office building:

• Why will people come? — Our building will be full of amenities and technological touches that will make discerning tenants feel comfortable, safe and part of a warm community of like-minded individuals.
• How will we do it? — Implement the latest tenant-facing technology offering services and capabilities that are not readily available at home. We will create indoor and outdoor environments that make people feel comfortable and happy.
• What tools, products and technology will we use?

This last question is often the hardest to answer and is usually left until the last possible moment. For building systems integrators, this is where the real work begins.

Focus on desired outcomes

When various stakeholder groups begin their investigations of the technology, it is crucial to define the outcomes everyone hopes to achieve for each use case. When evaluating specific products, it helps to categorize them at high levels.

Several high-level outcomes, such as digital twin enablement, data normalization and data storage are expected across multiple categories of systems. However, only an enterprise building management system includes the most expected outcomes. Integration platform as a service, bespoke reports and dashboarding, analytics as a service and energy-optimization platforms have various enabled and optional outcomes.

The following table breaks down a list of high-level outcomes and aligns them to a category of smart-building platforms available in the market. Expanded definitions of each item are included at the end of this article.

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.

Vena, a Canadian company focused on the Corporate Performance Management (CPM) software space, has raised $242 million in Series C funding from Vista Equity Partners.

As part of the financing, Vista Equity is taking a minority stake in the company. The round follows $25 million in financing from CIBC Innovation Banking last September, and brings Vena’s total raised since its 2011 inception to over $363 million.

Vena declined to provide any financial metrics or the valuation at which the new capital was raised, saying only that its “consistent growth and…strong customer retention and satisfaction metrics created real demand” as it considered raising its C round.

The company was originally founded as a B2B provider of planning, budgeting and forecasting software. Over time, it’s evolved into what it describes as a “fully cloud-native, corporate performance management platform” that aims to empower finance, operations and business leaders to “Plan to Grow” their businesses. Its customers hail from a variety of industries, including banking, SaaS, manufacturing, healthcare, insurance and higher education. Among its over 900 customers are the Kansas City Chiefs, Coca-Cola Consolidated, World Vision International and ELF Cosmetics.

Vena CEO Hunter Madeley told TechCrunch the latest raise is “mostly an acceleration story for Vena, rather than charting new paths.”

The company plans to use its new funds to build out and enable its go-to-market efforts as well as invest in its product development roadmap. It’s not really looking to enter new markets, considering it’s seeing what it describes as “tremendous demand” in the markets it currently serves directly and through its partner network.

“While we support customers across the globe, we’ll stay focused on growing our North American, U.K. and European business in the near term,” Madeley said.

Vena says it leverages the “flexibility and familiarity” of an Excel interface within its “secure” Complete Planning platform. That platform, it adds, brings people, processes and systems into a single source solution to help organizations automate and streamline finance-led processes, accelerate complex business processes and “connect the dots between departments and plan with the power of unified data.”            

Early backers JMI Equity and Centana Growth Partners will remain active, partnering with Vista “to help support Vena’s continued momentum,” the company said. As part of the raise, Vista Equity Managing Director Kim Eaton and Marc Teillon, senior managing director and co-head of Vista’s Foundation Fund, will join the company’s board.

“The pandemic has emphasized the need for agile financial planning processes as companies respond to quickly-changing market conditions, and Vena is uniquely positioned to help businesses address the challenges required to scale their processes through this pandemic and beyond,” said Eaton in a written statement. 

Vena currently has more than 450 employees across the U.S., Canada and the U.K., up from 393 last year at this time.

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.”

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).