Caitlin Leksana, Fazeshift | AI for CFOs & AI Leaders in Silicon Valley

Summary

Highlights

• Fazeshift uses AI to automate accounts receivable, helping businesses get paid faster! 🚀
• The AI agent integrates with existing business systems, offering tailored automation solutions 🤓.
• Caitlin's journey from Harvard to founding Fazeshift highlights the entrepreneurial spirit driving Silicon Valley innovation 👩‍💼.
• Customizable AI workflows replace manual finance tasks, allowing strategic focus and efficiency 📈.
• The company's journey through Y Combinator provided a platform for growth and valuable industry connections 🌱.
• CFOs are encouraged to see AI as a tool for bringing creative and efficient change to traditional finance roles 👀.
• The challenge lies in ensuring automation is auditable and capable of scrutiny, especially in sensitive financial roles ☑️.
• Fazeshift is gearing up for significant announcements, hinting at exciting growth and expansion opportunities 🌟.

Key Takeaways

• Fazeshift is revolutionizing accounts receivable by automating processes, making businesses more efficient and freeing finance teams to focus on strategic decisions 🤖.
• AI's role in CFO technology is gaining momentum, crucial for automating manual tasks and enabling more precise financial operations 📊.
• The concept of moving from 'doer to reviewer' is central to technology's impact on business processes, allowing more strategic oversight 💼.
• Customizable AI solutions in finance enable more personalized and efficient operations, breaking down traditional data silos 🏢.
• CFOs are under pressure to integrate AI to remain competitive and efficient, highlighting the transformational role of AI in business 📈.
• Fazeshift's approach includes integrating AI with existing enterprise systems, enhancing data connectivity and financial accuracy 🔗.

Overview

Chapters

• 00:00 - 00:30: Welcome and Introduction to theCube Special Presentation The chapter titled 'Welcome and Introduction to theCube Special Presentation' features the host, John Furrier, welcoming the audience to a special presentation of theCube held in the Palo Alto studios. The event is part of the Cube and NYSE Wired community presentation. The introduction highlights the presence of theCube East at the NYSE show floor and emphasizes the rapid development of great content and community. The main focus of this week's presentation revolves around AI innovators in Silicon Valley.
• 00:30 - 01:00: Focus on AI for CFOs and Introduction of Guest Caitlin Leksana The chapter discusses the growing importance of AI in the role of CFOs, highlighting how business transformation is becoming a significant aspect of their responsibilities. It touches on various implications such as decision-making, architecture, budgeting, finance, revenue, and costs.
• 01:00 - 01:30: Caitlin Leksana and Fazeshift's Background The chapter discusses the collaboration between Caitlin Leksana and her partner, who initially worked together as HBS classmates. They have since transitioned their teamwork into a startup focused on reimagining business operations. The discourse revolves around the concept of a business operating system, emphasizing the financial and investment aspects, particularly in relation to platforms. The narrative reflects on traditional and modern approaches to handling financial linkages and dependencies in business operations.
• 01:30 - 02:00: Fazeshift's Role in Business Operations The chapter titled 'Fazeshift's Role in Business Operations' focuses on the significance of Fazeshift in the realm of business services, particularly as a managed service. It addresses key concepts such as TCO (Total Cost of Ownership) calculations and payback periods, highlighting Fazeshift's offerings and business model. The establishment of Fazeshift, including its journey through Y Combinator, is briefly touched upon. The discussion emphasizes its core service that supports business operations and draws comparisons with successful companies like Meteor, which have mastered the integration of similar business model subsystems.
• 02:00 - 02:30: Automating Accounts Receivable with AI The chapter titled 'Automating Accounts Receivable with AI' discusses how Fazeshift utilizes AI to transform accounts receivable processes. By automating manual tasks, Fazeshift enhances the efficiency of finance and accounting teams. The AI technology provides custom workflows that allow companies to focus on strategic decisions while streamlining how they get paid and manage financial operations. The overarching goal is to shift the focus from manual processes to strategic business advancement.
• 02:30 - 03:00: Resolving the Problem of Manual Tasks in ERP and CRM Systems The chapter discusses the resolution of manual tasks within ERP and CRM systems, highlighting the burden these tasks impose on organizations. The narrative references a 2014 interview with Andy Jassy, emphasizing the concept of 'undifferentiated heavy lifting' common in cloud computing. The transition from tech labor to the utilization of 10x engineers is noted, leading to the modern era where 10x business people leverage AI to enhance efficiency. The text underscores the importance of accounts receivable and cash flow in business operations, acknowledging the significant impact of resolving these manual challenges.
• 03:00 - 03:30: The Importance of Cash Flow in Business The chapter explains the significance of cash flow in business operations. It emphasizes the need for cash to flow in quickly and the supporting policies in place. Although the necessary workflows already exist, the discussion shifts to identifying specific problems businesses face, particularly in automating processes historically done manually, despite the availability of AI technologies like machine learning and natural language processing for over a decade.
• 03:30 - 04:00: How Fazeshift Solves Manual Workflow Problems The chapter discusses how Fazeshift addresses the issues of manual workflows that many organizations face, particularly those that still rely on email and Excel-based CRMs. Fazeshift introduces an AI agent that integrates with existing software systems to execute tasks currently performed by humans, enhancing efficiency and reducing manual labor. The innovative technology serves as a layer on top of existing systems, such as CRMs, and automates processes that traditionally slow down organizational operations.
• 04:00 - 04:30: Integration with Existing Systems like NetSuite and Salesforce The chapter focuses on the integration of existing systems such as NetSuite and Salesforce with human operations to streamline processes. Employees typically log into these systems to create and update records, while also managing communications through email. A significant focus is on the dichotomy between Accounts Payable (AP) and Accounts Receivable (AR). The AP department holds substantial control over financial processes, dictating the workflow, whereas AR must adapt to the payment preferences of customers. This integration aims to harmonize these workflows to achieve efficient and effective business operations.
• 04:30 - 05:00: Generative AI and Customization in Accounts Receivable The chapter focuses on the transformative impact of generative AI on customization within the accounts receivable sector. It highlights how generative AI introduces creativity and personalization, overcoming previous software limitations. Each company’s unique needs can now be addressed effectively, making AI a 'personalization dream scenario.' Although AI has been beneficial in tasks like search and retrieval, it is particularly effective for task-oriented actions, providing tailored solutions in real-time.
• 05:00 - 05:30: Personalization in Business Tasks with AI The chapter discusses the innovation in business tasks through AI, focusing on personalization and customization. It highlights how personalization, common in consumer experiences, is now being applied to daily business tasks and team operations. The conversation addresses the changes in roles such as those in finance and accounting, due to these advancements.
• 05:30 - 06:00: Transition from Doer to Reviewer in Business Roles The chapter discusses the shift in business roles from being active doers to becoming reviewers due to the increasing capabilities of AI. It highlights how AI can now automate many tasks that employees used to do manually, and thus the role of employees pivots to overseeing and reviewing AI's actions. The text emphasizes the importance of understanding AI's decision-making process, particularly for roles like CFOs. It also raises the crucial question of how businesses can facilitate the review and editing of AI actions within existing systems like NetSuite and Salesforce.
• 06:00 - 06:30: Exploring AI's Role in Automating Business Tasks The chapter titled 'Exploring AI's Role in Automating Business Tasks' delves into the critical aspects of integrating AI into business operations, particularly focusing on finance departments such as those led by CFOs. It discusses the challenges associated with ensuring data accuracy and accountability when using AI to automate tasks. The narrative highlights the necessity of providing users with an interface that allows them to understand AI decision-making processes and data usage, ensuring confidence and transparency. Additionally, the chapter touches upon the encouraging atmosphere in entrepreneurial settings, where venture capitalists (VCs) urge innovators to take bold steps in developing advanced platforms like the next Salesforce or CRM solutions, possibly through low-code development.
• 06:30 - 07:00: Review and Approval Process in AI Automation This chapter discusses the review and approval process within AI automation, particularly focusing on the critical role of data in platforms like Salesforce and NetSuite. The narrative reflects on the enthusiastic and bold approaches individuals take in these processes and emphasizes the importance of having reliable and quality data to succeed. Despite the not-so-great user interfaces of these platforms, their success is attributed to the effective handling and utilization of data.
• 07:00 - 07:30: Challenges and Approaches in AI Integration The chapter addresses the challenge of integrating AI into existing legacy systems within companies. It highlights how major systems like Salesforce and NetSuite are firmly established and are not being replaced, despite AI advancements. Innovators in AI are tasked with enhancing these systems by adding value through data surfacing and utilization, thus improving overall functionality with AI to create a more cohesive operation.
• 07:30 - 08:00: Leadership and Innovation from Salesforce and NetSuite In this chapter, the discussion revolves around the role of leadership and innovation within major companies like Salesforce and NetSuite. The focus is on how these corporations leverage technology to automate mundane tasks, thereby allowing teams to shift their focus to creating more value. The conversation also touches on the challenges of integrating AI into enterprise domains, which are often siloed and complex with varied data systems. The speakers emphasize the importance of having domain expertise to navigate these complexities, suggesting that simply applying AI is insufficient without understanding the intricate processes and the legacy systems in place.
• 08:00 - 08:30: The Role of Domain Expertise in AI Implementation The chapter discusses the complex integration of various operating systems within a company, highlighting the importance of human expertise in bridging these systems. Experienced professionals often know how to navigate different platforms like Salesforce and NetSuite, which contain vital data for business operations. By leveraging this human knowledge, companies can unite disparate data systems to enhance value beyond maintaining them in isolation. The conversation further appreciates the proactive approach of smart CFOs who are attentive to the integration of subsystems, recognizing its significance in optimizing business efficiency. The business model discussed aims to capitalize on these insights by providing solutions that connect and streamline data across multiple platforms.
• 08:30 - 09:00: Connecting Legacy Systems and Human Knowledge The chapter emphasizes the difference between core and non-core competencies in business, using cash management as an example of a core competency, essential to the lifeblood of a business. It explains that companies don't necessarily need to own the intellectual property (IP) of the underlying systems (the 'plumbing') but can rely on specialists who ensure reliability and accuracy. The discussion includes a comparison with Meteor and its expertise in usage-based pricing, highlighting the importance of specialists in maintaining accurate and reliable data handling.
• 09:00 - 09:30: CFOs' Focus on Core and Non-Core Competencies Chapter 4 of the book entitled 'CFOs' Focus on Core and Non-Core Competencies' discusses the value of leveraging best practices and expert knowledge when implementing artificial intelligence into corporate processes, such as accounts receivable. The text emphasizes the importance of seeking expert guidance to optimize resources efficiently. The dialogue further explores the democratization of learning and the applicability of these concepts in educational ventures, illustrating the diverse applications of AI.
• 09:30 - 10:00: Value Proposition of Fazeshift in Accounts Receivable The chapter discusses the potential role of human expertise in enhancing accounts receivable (AR) processes. It highlights the opportunity for individuals working within the machinery of AR to identify significant process improvements, thereby providing differentiation and value through their domain skills. Although such groundbreaking contributions have not been widespread yet, there is optimism that this level of impact will manifest as these roles evolve.
• 10:00 - 10:30: Market Demand and Customer Engagement Strategies The chapter 'Market Demand and Customer Engagement Strategies' delves into the transformative impact of AI on business processes. Teams are increasingly approaching experts with the understanding that AI can significantly streamline and optimize various operations. They believe AI has the potential to perform diverse tasks such as reading contracts, matching payments, drafting emails, and categorizing emails efficiently. This chapter highlights the strategic advantages of integrating AI into business workflows, emphasizing the benefits of proactively adapting to technological innovations to drive better outcomes and enhance customer engagement. The narrative underscores the importance of recognizing the consequential changes AI brings to market demands and the value it adds to organizational strategies.
• 10:30 - 11:00: The Importance of Data Connectivity and Technical Expertise The chapter emphasizes the democratization of AI which allows broader access and utilization of AI technologies. However, it also highlights the need for specialists who understand how to connect data and integrate AI into existing systems. This integration can free up team members to focus on process improvements instead of being bogged down by routine tasks. The discussion also touches on funding levels, suggesting it's a topic of interest in improving data connectivity and technical project implementations.
• 11:00 - 11:30: Entrepreneurial Journey and Evolution of Fazeshift The chapter begins with the founders of Fazeshift discussing their current stage in the company's journey. Having been founded a little over a year ago by two Harvard Business School alumni, the company has progressed significantly. The founders share that they participated in Y Combinator, with Garry Tan as their partner, highlighting the validation and momentum they received from the announcement made during this period. There is a sense of excitement as they talk about the proof of their concept and the influx of interest they have experienced as a result. Overall, this chapter captures the entrepreneurial spirit and progression of Fazeshift from its inception to an exciting stage of growth and customer engagement.
• 11:30 - 12:00: Challenges and Solutions in Manual Billing Processes The chapter titled 'Challenges and Solutions in Manual Billing Processes' introduces the unique approach an organization is taking with AI, which has generated considerable interest and attracted many customers. The narrator mentions a recent transition and teases an upcoming fundraising announcement expected in early January. The conversation touches on Garry, who is noted for his significant contributions in San Francisco and his involvement in promoting democratization and political engagement. The chapter appears to set the stage for discussing billing processes in the context of democratized access to technology and upcoming financial developments.
• 12:00 - 12:30: Customer Relationship Management in AR Processes The chapter focuses on the origin story of a startup related to Customer Relationship Management (CRM) within Augmented Reality (AR) processes. The conversation reveals that the idea emerged as a pivot from an initial crypto-related venture. The co-founders began discussing possibilities while at Harvard Business School and decided to shift focus based on market opportunities and personal experiences. One of the co-founders has a background in computer science from MIT and cybersecurity experience from serving in the US Navy, which contributed to the technical foundation and direction of their CRM in AR venture.
• 12:30 - 13:00: Advice for Entrepreneurs Entering the AI Space The chapter discusses the journey of entrepreneurs entering the AI space, highlighting the importance of a founder's unique understanding of a problem. It begins with a particular case of a cryptography startup, where the founders initially faced challenges related to accounts receivable as clients preferred paying in dollars rather than crypto. This realization led to a pivot, enabling them to address a much larger and more pressing market need. The narrative underscores the significance of firsthand experience with a problem that founders often bring to their ventures, guiding them in navigating new industries like AI.
• 13:00 - 13:30: Observations on Business Model Transformation In this chapter titled 'Observations on Business Model Transformation,' the discussion focuses on the strategic shifts and adjustments businesses need to make when recognizing new market opportunities. The narrative highlights the transition from traditional business models to those emphasizing service layers in the frontend combined with backend scale to achieve operational leverage. Unlike two decades ago, when the emphasis was solely on platforms and scale, contemporary strategies leverage services as a way to enhance operational efficiency, especially with the rise of AI. The chapter underscores the importance of adaptability and learning in the evolving business landscape.
• 13:30 - 14:00: CFOs' Role in Business Model and Operations Alignment The chapter focuses on the evolving roles of CFOs in aligning business models and operations with technological advancements. It highlights the necessity of understanding and implementing AI within business practices. The discussion underlines two key points: first, the growing need for expertise in AI as many are still unfamiliar with its full potential; second, the critical nature of managing data layers in this technological shift, illustrated through examples such as the Databricks layer. The conversation emphasizes that leveraging these innovations is not just advantageous but necessary for modern business operations.
• 14:00 - 14:30: Fazeshift's Vision and Company Growth Plans The chapter discusses Fazeshift's approach to data integration, comparing it to a Palantir approach. Fazeshift offers a customizable system designed to connect with existing data sources in a "plug and play" manner. The emphasis is on customization and the involvement of engineers in sales conversations due to the technical nature of discussions around data and AI. This strategy highlights the complexity and the need for technical expertise in integrating massive amounts of data, likening the process to navigating through TSA, where one desires expedited clearance.
• 14:30 - 15:00: Community and Network Benefits from Y Combinator and Harvard Business School The chapter explores the benefits and advantages of being part of networks like Y Combinator and Harvard Business School. It touches on the challenges of maneuvering through the competitive landscape, securing data, and the importance of auditing AI processes. The conversation reflects on experiences with overcoming hurdles in enterprise settings related to IT and security, emphasizing a need for thorough review and auditing of AI activities.
• 15:00 - 15:30: Conclusion and the Importance of Open-source Content The chapter discusses a shift in the company's approach towards the automation of financial and accounting tasks using AI. Initially, the company believed they could fully automate these processes, expecting full trust in AI decisions from clients. However, they realized that clients, especially CFOs, required transparency in AI operations, including decision-making processes, data usage, and recommendations, in order to audit these actions thoroughly. The focus has shifted towards providing detailed insights into AI functions to ensure responsible implementation. This pivot not only addresses client concerns but also adds value to the product’s requirements.

Caitlin Leksana, Fazeshift | AI for CFOs & AI Leaders in Silicon Valley Transcription

• 00:00 - 00:30 >> Welcome back everyone to theCube special presentation here in our Palo Alto studios. I'm John Furrier, your host of theCube. This is part of our Cube and NYSE Wired community presentation. We've got our studio here in Palo Alto. Of course, we've got theCube East on the show floor of the NYSE, and we've got great content coming together, great community development, very, very fast. And of course the focus this week is AI innovators in Silicon Valley here in this subnet of our community,
• 00:30 - 01:00 but also a focus on AI for CFOs as business transformation becomes a big part of their job. And of course it's going to impact decisions, architecture, budget, finance, revenue costs, all good stuff. So check that out. Caitlin Leksana here is the co-founder and CEO of Fazeshift, who's an innovative startup just hitting the scene. Love having founders on, Caitlin thanks for joining us. >> Thanks for having me - Joining the community. Appreciate it. >> Awesome. - So you guys just came out of YC '24, you
• 01:00 - 01:30 and your partner, HBS buddies working together on projects. So you're in the classroom working on projects. Now you've got a startup. Love what you guys do because this is a thesis we're seeing in the CFO conversation around the operating system of business. The plumbing is money and linkages between investments that have been made, sunk cost and investments as well as dependencies and all that stuff. And platforms are also a big part of it. In the old school way, buy some IT, okay, ROI,
• 01:30 - 02:00 TCO calculations, payback, serving the business, text the business now. You guys have a service that's kind of core to business as a managed service. We've seen Meteor and other companies really doing well, owning kind of a subsystem of the business model that just works and continues. Take a minute to explain Fazeshift, the origination. Obviously you got the Y Combinator, you went through that process. What are you guys offering? Talk about the business model. >> Yeah, absolutely.
• 02:00 - 02:30 Like you said, it's core of the fundamental operating system of a company. Basically we help companies get paid, we help move money. So Fazeshift is an AI agent for accounts receivable. Basically we help companies automate anything that's still manual about their accounts receivable process and help the company get paid. And then so we build customized AI workflows, we bring that into the office of the CFO, into finance teams, accounting teams, and we replace everything that's manual so those teams can focus on strategic decisions that actually move the business forward instead
• 02:30 - 03:00 of doing the very manual tasks that kind of bog the organization down. >> Yeah, I mean I remember my first time I interviewed Andy Jassy in 2014. He said undifferentiated heavy lifting that's become a mantra of, say cloud generation. Back then it was labor for tech, so they had that step function, 10x engineer. Now we're seeing the 10x business person come in with AI. So you guys are doing something that, I mean everyone knows accounts receivable, that's money. Cash flow, cash is king. >> Exactly. - I mean this is where you guys shine.
• 03:00 - 03:30 Talk about that undifferentiated heavy lifting because it's differentiated in the sense of want the cash to come in fast and all kinds of policies behind it. This workflow is pre-existing. Talk about what you guys solve. What's the use case specifically and where's that value extracted? >> Yeah, I mean the problem that we see is even though... you know, AI was around probably 10 years ago, right? There was the original machine learning, there was NLP, but the question that we ask is why is there still teams of people doing things very manually in these ERP systems
• 03:30 - 04:00 and these CRMs still via email and Excel? That's the problem that we're trying to solve is so much of this is still manual and it really bogs the organization down. So what we come in and do is we have an AI agent that plugs into their existing software system, sits as a layer on top, and we actually execute the tasks that humans are doing today. And I think that's the new technology that we haven't seen before that we're able to leverage and bring that into the company. >> What are some of the incumbent or inherent preexisting systems that you guys work with? What are you seeing most? >> Yeah, when we come into a company, it's usually a team
• 04:00 - 04:30 of somewhere between 5 and 20 people operating on top of NetSuite. So they're logging into NetSuite, they're creating records, they're then looking at their email, they're then logging into Salesforce and updating records. And so it's these existing operating systems, to your point. But then there's humans, there's knowledge internally. And what we see in when we talk about money movement, there's AP and AR. The AP side of it has a lot of control and leverage in the relationship. And so they decide how the process works. The problem is on the AR side, they have to conform to the process of how their customers want to pay them.
• 04:30 - 05:00 And that complexity, that customizability, that's never been solvable with software before until generative AI allows us to bring creativity, customizability into the process. >> Yeah, you bring up a good point. I mean if you think about every company's different and so- >> Entirely. Exactly. - ... AI is a personalization dream scenario. >> Exactly. - And we've been saying on theCUBE many times, okay, search, killer app, no problem. Find what you're looking for. Hey, where's that file? Where's that record? What's going on? Maybe a prompt, get a little answer, get a little help there. But tasks is where action is,
• 05:00 - 05:30 and this is where you guys are innovating, right? Talk about that piece because searching and making my job easier, reducing the steps it takes to do something, interface check, that's minimum table stakes. Tasks is the killer. >> Totally. And you mentioned personalization. Consumers are very comfortable with personalization. We're bringing personalization and customization into the tasks that businesses do every day and that the teams are doing every day. I kind of think about it is I think there's a lot of talk and a lot of interest around how is the role of someone in finance or accounting or the CFO changing?
• 05:30 - 06:00 The way we see it is the role of everybody in a company is moving from a doer to a reviewer. How are they reviewing that the tasks that the AI can actually do automatically for them now? So part of what we do is we can identify what's the action that needs to be taken and we actually take that action in their softwares for them, whether we're going to NetSuite, we're going into Salesforce, we're going into their existing systems and doing what the humans are doing. But I think what's really important and what if I was a CFO, I would be asking my vendors is how do you give me the ability to review that and edit it
• 06:00 - 06:30 and approve it before you actually go and do it? Because what's unique about the CFO is you are touching finance data, you cannot be inaccurate with us. And so how do you give people a place to review that? And that's really what we focus on is yes, we automate those tasks, but how do we give people an interface to understand how the AI thinks and what data it's using to make decisions. >> I was at a conference, I won't say the VC's name, and I was participating also as an attendee, and VCs are like always run into the fire, build the next Salesforce, build the next CRM, low code,
• 06:30 - 07:00 no code, "I'll do it. " A couple of people died, someone wins, they win. But it's Salesforce, NetSuite, they have the data. So everyone's like, "John, what do you think? " I'm like, well, I love the bravado. Go run into the fire, be in the arena or whatever metaphor they want to use. But at the end of the day, the role of a Salesforce or a NetSuite, it's going to be a function of their ability to have good data. And again, everyone who uses Salesforce knows that it's not the best user interface,
• 07:00 - 07:30 but it's a system of record. AI is a dream. That's why Marc Benioff's rebranding Agentforce because he now can make all those piece parts work better together with AI, but they'll still be a major system. Not the answer. >> They're not going away. Salesforce is not going away, NetSuite is not going away. But as innovators in AI coming into companies that have these legacy systems that aren't going away, the question for us is how do we still bring value to those companies? And a lot of that is how do you surface data, use the data,
• 07:30 - 08:00 give it to their teams, automate away the mundane tasks that honestly aren't very fun and allow them to actually create value for the company in other ways. >> I think the renaissance of the domain expertise in enterprise, which has been a hard nut to crack in tech, because it's very siloed, a lot of different data and systems. You can't just throw AI at that because there's a lot of knobs and buttons you got to push and people who actually have been doing it for years. >> You're absolutely right. - But they're end-to- end processes that are well-formed. >> Absolutely right. And that's what we've seen is there are all these legacy systems,
• 08:00 - 08:30 operating systems that are in place, but they don't talk to each other. And so that's where the humans come into play. Exactly what you're saying, is there are people that have been in the job for 30, 40 years that know where to look in Salesforce for this data, where to look in NetSuite, how to connect that together. They have this knowledge in their head and we come in and say, "We can connect all this data together for you. " And that makes it even more valuable than having it siloed in a single system. >> Well, I love the business model. Talk about the reaction to the comment that I was mentioning earlier about the operating system and how smart CFOs are looking at subsystems,
• 08:30 - 09:00 as you probably learned this in HBS and I did when I was in business school back in the day, core, non-core, core competency, non-core competency. Cash is a core competency, it's lifeblood of a business, but I don't necessarily have to build and own the IP of the plumbing so to speak. It doesn't sound like it's commoditizing what you do, but you're a specialist. You can say, "I'm going to be reliable with the data, accurate. " I mean Meteor has the same value proposition on usage-based pricing. >> Yeah, absolutely. And they are the experts in usage-based pricing
• 09:00 - 09:30 and that's why people bring them in. And we see that a lot is people come to us and say, "What are best practices for bringing AI into our accounts receivable process? You are the expert, you understand how to implement it. " And I think that's a lot of the value prop we bring is understanding what our best practice is because everybody's really figuring it out, and building it internally takes resources that they could be using for something else. >> We had a previous entrepreneur on who's doing out-school, it's an educational thing, it was a little bit different, but they talk about democratization of learning. I've been saying, I've been waiting to see this
• 09:30 - 10:00 in your role, you can be in the machinery doing AR and be a hero because if you identify process improvement at the point of needle moving moment, that's where the human in the loop could actually provide real differentiation because they have the domain skills. So we've yet to see that kind of explosive, wow, this person in the line, so to speak, in the workflow, actually knocked it out of the park. >> Right. - That's going to come. Have you seen any evidence of that emerging
• 10:00 - 10:30 and examples of where someone could say, "Wow, if I just do this thing, I know this one thing, if I move this, that changes the game on the other side, that creates a kind of consequence, a good consequence. " >> Teams come to us, it's really interesting. Teams come to us and they say, " I know AI should be able to do XYZ. I know AI should be able to read contracts. I know AI should be able to match my payments. I know AI should be able to draft these emails for me or categorize my emails, what have you.
• 10:30 - 11:00 I just don't quite know how to do it. " And I think that's kind of to your point about democratization, it's democratization of AI, but then you need a specialist to come in who understands how to get all of your data connected and actually bring the AI on top of all of that data and actually flow through your systems, and then that actually unlocks the people on the team to find process improvements that they've never been able to implement before because they've been busy doing, not reviewing. >> Yeah, they're so busy heads down grinding, grinding. They got to do it. Okay, so talk about the funding levels you guys are at.
• 11:00 - 11:30 Okay, where are you guys at on the company? What stage are you at? Product shipping, customers? Take us through some of the specifics. >> Yeah, absolutely. We're at exciting point in our company. So we were founded over a year ago. My co-founder and I went to Harvard Business School together. That's how we met and we started building startups together. We got into Y Combinator, went through that with Garry Tan was our partner there. He's just incredible. We did our big announcement during YC and got a ton of inbound just from that. So that was really proof that there's something here
• 11:30 - 12:00 that's been missing in the market, our unique approach to AI is really exciting to people. So got a lot of customers from that. Just got out of that this past summer and we have an exciting announcement about some fundraising coming in early January. So you'll hear about that soon. >> Okay, look, you got a big smirk there. You're hiding the ball there. >> Can't reveal all my secrets. >> So this is a funny round coming in. First of all, Garry's a great guy. I don't really know him personally, but I love the work he's done in San Francisco. He's been a big proponent of this democratization, certainly cares about what was going on in politics.
• 12:00 - 12:30 So Garry, shout out to you and the folks who made that happen. Talk about the idea. So did you guys come up with this at HBS? Was it something that you were riffing on? Was it a classic huge market let's go after we can solve this? Was it a personal thing? How did this all... What's the origination story? >> Yeah, it's kind of funny. So my co-founder and I, coming out of Harvard Business School, we were actually starting to build a crypto startup. That's where we originally started. My co-founder and CTO went to MIT for computer science, was in the US Navy, taught cybersecurity.
• 12:30 - 13:00 So he has a cryptography background, so that's really where we started. But funny enough in crypto, everyone wanted to pay us in dollars, not in crypto. And so we had our own AR problem at our own crypto startup and decided to pivot away from that. Turned out this was a much bigger market and much bigger need and something that we deeply understood as founders. And I think that's something that most founders bring to their company is their unique understanding of the problem. And we felt the problem ourselves. >> It's interesting, once you get in and you kind of navigate through
• 13:00 - 13:30 and you realize, well, sometimes you have to zig and zag and then here you guys stumbled upon a huge market opportunity. You guys thinking about the go-to-market as the service layer on the frontend with the backend scale operating leverage. Obviously there's some folks that are going to need learnings. And by the way, 20 years ago when you did a VC, all platform, all scale, no services. Services prime the pump. >> Totally. - And so with AI, you get operating leverage. So you're seeing services being cool now as a go-to-
• 13:30 - 14:00 market mechanism because you have operating leverage, you don't have to foreclose the scale. Do you guys think about that at all? >> Yeah, it's not just cool, it's a necessity, right? And there's two reasons for that. One reason is that a lot of people don't even understand the power of AI yet, and so they are looking to the experts and the innovators in this space to give them use case and examples and recommendations of what best practices are to bring AI into their workflows. So that's one. And the second is when you are touching these data layers, I mean you mentioned the Databricks layer, all
• 14:00 - 14:30 of these companies have huge amounts of data, they need it all connected together. This problem gets very technical very quickly. And so we take somewhat of a Palantir approach where it's a very customizable system, we've built our system to basically plug and play into anyone's existing data sources, a ton of customizability on top of that. But part of what that means is we bring almost this forward deploy engineer approach where there is an engineer in sales conversations because it gets very technical very quickly when you're talking data and AI. >> Yeah,. And there's also the hoops you got to jump through. It's like going to TSA, you want CLEAR and TSA Pre.
• 14:30 - 15:00 Go right through to the enterprise versus standing in line. You just want to get through the knothole. Sometimes there's some hurdles there. Have you experienced some of those and how does that go? I use that example over the top just to kind of make a point, but we've all been through that, right? It's like IT or security by the way too. Secure data is a huge thing. >> Absolutely. I think there's two hurdles that we've come in and one is it's the question that everyone should be asking is how do I review and audit what the AI is doing?
• 15:00 - 15:30 And this is actually what I'd call a mini pivot within our company. We start out being like, "We can fully automate everything. Everyone's going to trust what AI is doing." And the reality is- >> We got this. >> Yeah, we got this. Most CFOs are like, "That's not a responsible implementation of AI. I need to see everything that the AI is deciding, how it's making the decision, what's data it's using, what's it recommending, and then actually be able to audit it. " Right? At the end of the day we're talking about financial and accounting records here. So that's one thing that we've really pivoted and focused on. And then when it comes to implementation- >> Oh, it's a nice value-add too on the product requirements.
• 15:30 - 16:00 You're getting real-time working backwards- >> Entirely. Entirely. And when it comes to AI, the ability to get feedback of what's working and what's not working, a lot of unique prompt engineering going on behind the scenes with our forward deployed engineers to customize it to our clients. >> Yeah. One of the things I'd like to ask you is that let's just say that you have a spectrum of customers because I think this will work at any start-up right out of the gate. Because remember during the cloud revolution, all the customers were developers and they became huge companies, Airbnb, Dropbox.
• 16:00 - 16:30 So I'm sure startups when they hit escape velocity series B or series A, they might have Salesforce or spreadsheets. And then you have higher end like a big company that might be somewhat regulated or they have more systems, more of a data problem. Take me through the engagement. Say I'm sold on it, you had me at hello, I want in, I want my cash flow coming in fast, I want predictability, I want reliability, but I'm on spreadsheets and I'm on Salesforce. What's the use case for that one?
• 16:30 - 17:00 >> So what we initially do with every engagement is the proof is in the pudding. And so we ask people, "Give us some of your data, let's sign an NDA, give us some of your data, we'll run it through our models, we'll show it in our platform and you get to see and engage with it. " Because I think a lot of people, they get sold on the idea, they love the idea, but then it comes down to how can I understand within my workflow how this is going to help? How do I see AI working in practice? I get it in theory, but how do I see it in practice? So that's kind of to your point about engagements,
• 17:00 - 17:30 we always ask people for their data, we actually run it through our system, show them how they're going to use it, and then we need to talk with their IT departments and their security departments and actually get it implemented in there. >> So it's not so much their maturity, you have the same kind of process depending upon where they are. So you can talk mid-market all the way up to large enterprises. >> Yep, exactly. - Talk about the entrepreneurial zeal around, because I could see many things popping out where you have to say no to. I could see you guys saying, "Oh wow, I could actually get you some credit on that.
• 17:30 - 18:00 " So say, my client's paying 90 days, that's a problem which many people are having these days. It's like, okay, pay 90 days. So is there other derivative areas, adjacencies you're targeting or saying no to? What's the focus? >> Yeah. Accounts receivable is a big enough market alone. If we just win an accounts receivable, we will be a multi- billion dollar company. What you see on the accounts payable side with Coupa, Bill.com, Ariba, now Ramp, they are multi $5 billion plus companies and that doesn't exist on the AR side because what I talked about earlier
• 18:00 - 18:30 where they don't have the leverage, the customizability has never been possible. So AR alone is a big enough problem that we want to solve to be multi-billion dollar. The question is how do we get to $10 billion plus? That's where kind of the adjacencies come into play. It's always on our mind. >> Are there surprises so far in some of the early engagements where it's like you discover, "Wow, we didn't invoice this or that was gone." >> The amount of mistakes is insane, and people know it. The controllers, the CFOs, they know that this is happening. They know that there are mistakes in the data.
• 18:30 - 19:00 People are not getting billed accurately. Payments are not getting applied correctly. And at the end of the day, these are customer facing problems that affect their relationship with our customers. So the sales team usually gets involved. So we typically find- >> That's not productive. >> It's not productive. I mean sometimes it's the CRO driving the sale, because they're like, "How do I get my sales team out this Process? " So sometimes we're talking to CFOs, sometimes we're talking to CRO. >> I mean you're really cleaning house on the front end of the business, that if you do that work upfront, because it's a system now, you're plugged into a bigger
• 19:00 - 19:30 operating system, the downstream consequences of mistakes on the frontend really are amplified kind of downstream. Because people are then, "Oh, we're good here. " And then, "Wait a minute, there's big problems over here." >> Right. And I think as an entrepreneur or anyone trying to move into this space, the question that I'd urge them to ask is what is still incredibly manual? Even if it's old school, accounts receivable has been out since businesses and cash has flowed, but how do we take this old school process, bring AI into it, become a system of record and improve the processes and bring real value to the company?
• 19:30 - 20:00 >> And AR also wants to talk to AP too. I mean AP and AR, they are like together. >> Oh yeah, we're working on that too. >> Left hand, right hand. Caitlin, so we are doing the AI focus for CFOs. I'd love to get your perspective. Obviously you guys have a great view where you're at obviously and credentialed up big time. So as you look at CFOs that are watching, they're thinking about, they're under a lot of pressure. They got cybersecurity, you mentioned you guys have a background in cyber, and you probably know, it's like damn, the job's heavy on that side, insurance.
• 20:00 - 20:30 But now the business model transformations are happening. What do you see and what's your vision for CFOs out there who were really open-minded to learning and progressing towards a playbook or some guardrails or at least getting their framework around how to handle the inbound tsunami of AI? Because they're getting told down from the board level, "Infuse AI for competitive advantage and revenue growth and cost optimization. " But then you got the bottoms-up developers who are writing the apps, they're involved.
• 20:30 - 21:00 So there's a lot of a nexus of those forces happening and CFOs are now tossed in the middle of it because they're, I think soon will be driving a lot of the business transformation questions and policy. >> Yeah, this is absolutely what I've seen. I see it now as an entrepreneur. I saw it back when I was at BCG advising CFOs in Fortune 1000s. And they, I think this unknown fact that CFOs sit in the center of a lot of the decision-making. They sit in between these departments, they have a lot
• 21:00 - 21:30 of visibility, they have a lot of say. And I think kind of something you're bringing to fruition is what's a framework that they should use as they think about adopting AI? And I think that they have this purview, and most CFOs tend to be very business minded, very numbers driven. And I think at the end of the day it comes down to numbers. And that's part of what we do with getting them to try out the product before they buy or even the buy versus build decision. The CFOs are best situated to be making these decisions
• 21:30 - 22:00 as they bring AI into the company. >> Exactly. I always say revenue models is one piece of the business architecture, business model. If it's technology value, the sales teams, they're stakeholders in a system. >> Right. And I think something that we are trying to unlock for CFOs is as they are changing their understanding of their own business model, their operations need to conform to that. They can get more creative with their business model, different product lines, more creativity about who they go
• 22:00 - 22:30 after, but their operations need to support that. And that's what we see as people who are like, "We have very complex operations already, but as we scale that will only get more complex as our business models change and we try to adapt." >> And you have to make it easier. That's the minimum table stakes for you guys. >> That's our goal. - All right, what's next? You got some news coming, so probably funding, kind of tease that out a little bit. Hiring, put a plug in for Fazeshift. What's the culture like? What's the north star? What are you guys looking to do? >> Yeah, absolutely. So we just grew our team. So we've got four incredible engineers with us so far.
• 22:30 - 23:00 We're a very product engineering heavy org, very focused on building the best product out there and giving that to finance and accounting teams. So growing product, fundraising and then growing our customer base. We've gotten great feedback from our customers so far and just looking to grow to more in the future. >> How's the journey been for you and your co-founder so far? Obviously YC, awesome group, great alumni. >> Yes. - I think it's over 10 years now at least I can think of. I don't know what year they're on, but it's been quite the institution.
• 23:00 - 23:30 >> Yeah, the combination of the Y Combinator community and the Harvard Business School community. I mean it's part of the reason that my co-founder and I decided to go to both is it comes down to the people in the community and they've just been so helpful. A lot of our customers are Y Combinator alums, unicorns that have come out of Y Combinator or Harvard Business School alums as well. >> It's a great network. - It's a great network. >> Well, thanks for coming in and joining our mission here. Appreciate it. >> Thanks for having me. - Thanks for contributing. >> It was fun. - This is theCUBE plus the NYC Wired community. This is our job, opensource content, putting it out there.
• 23:30 - 24:00 As the world's changing the business model transformation is happening. Obviously we cover the tech as well as now CFOs aspect of it. And we've seen it in cybersecurity, seeing it in the data value, which is going to drive the revenue model. And Caitlin and team are targeting the money, get those AR departments all streamlined and get the cash flowing in. All right, more coverage here after this short break.