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Building AI Agents for Everyone

Gumloop (YC W24) is an AI agent builder used by companies like Shopify, Gusto, and Instacart. It lets employees build and share agents that automate work across their existing tools, while IT controls security, data access, and costs. In this Fireside, co-founders Max Brodeur-Urbas and Rahul Behal talk to Y Combinator General Partner Gustaf Alströmer share how better models transformed their visual workflow builder into an agent platform, what they learned from 1,100 customer calls, and how one power user helped them land Instacart. They also explain why bringing agents into tools like Slack drives adoption and why the people who understand a task should be the ones automating it—to help teams do more, not simply replace them. https://www.gumloop.com Chapters: 00:00 — The AI Agent Builder for Every Team 03:38 — From Workflow Automation to Enterprise AI 07:20 — What It Takes to Win Enterprise Customers 11:02 — How the Founders Met and Got Into YC 15:00 — How Gumloop Uses Its Own Agents 18:02 — Landing Shopify and Rethinking Pricing 21:23 — Fundraising and the Limits of Staying Lean 24:35 — Letting Employees Automate Their Own Work 27:43 — Building Agents Companies Can Control 31:23 — Getting IT to Say Yes Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs

Gustaf AlströmerhostMax Brodeur-UrbasguestRahul Behalguest
Oct 2, 202634mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 3:38

    The AI Agent Builder for Every Team

    1. GA

      [upbeat music] Today I'm here with Max and Rahul from Gumloop. Um, thanks for coming back to YC.

    2. MB

      Thank you.

    3. RB

      Sure.

    4. GA

      I'm really excited for this conversation. You guys are one of the most successful, yet kind of unknown companies, uh, from Winter '24. Tell us what Gum- Gumloop is.

    5. MB

      Gumloop's the agent builder that a lot of companies like Shopify, and Gusto, and Instacart, uh, use so that everyone can build agents and share them across the business while IT can manage and control that data.

    6. GA

      So kinda like the holy grail of agents. Like, this is what everyone is talking about, what they wanna act- actually build. Like, this is probably one of the more common application ideas we get from YC today.

    7. MB

      Yeah.

    8. GA

      But you actually built it, and you're deployed in, like, many of the biggest companies.

    9. MB

      Yeah.

    10. RB

      Yeah.

    11. MB

      It's definitely a, a crowded space, 'cause it, it feels, like, obvious that someone has to solve this problem.

    12. GA

      Yeah.

    13. MB

      There's definitely a, a bunch of players that are doing a good job, but we're just trying to be one of the leaders.

    14. GA

      Yeah.

    15. MB

      Be the audit. Yeah.

    16. GA

      And you applied to YC for, uh, Winter '24, uh, and you called Agent Hub at the time.

    17. MB

      Mm-hmm.

    18. GA

      And then, uh, switched to Gumloop.

    19. MB

      Yeah.

    20. GA

      Um, what was this product when you applied?

    21. MB

      Yeah, it is ironic that we called ourselves Agent Hub back then, 'cause now we are an agent hub. Uh-

    22. RB

      [chuckles]

    23. MB

      ... but there was a bunch of... Everyone thought we were a real estate agent company, uh-

    24. GA

      Oh

    25. MB

      ... 'cause agents weren't common-

    26. RB

      Yeah.

    27. GA

      Right. Right

    28. MB

      ... uh, back in the, like, back then. But we were a workflow automation platform, so same mission of, like, automating tasks.

    29. GA

      Mm-hmm.

    30. MB

      But the models weren't good enough back then, so we just made a visual builder, and people could define their, their workflow step by step.

  2. 3:38 – 7:20

    From Workflow Automation to Enterprise AI

    1. GA

      And it seemed like you were one of those companies where basically you had this idea before models were good, and once models got good, this just, like... This product got, like, infinitely better.

    2. RB

      Mm-hmm.

    3. GA

      Is that what happened?

    4. RB

      Yeah. I think that was definitely one of the strategies at first, which was, like, the models were not good enough to make agents work, and so we kind of met the models where they were.

    5. GA

      Mm.

    6. RB

      And that's why we started with this workflow builder and kinda, like, minimized the use of AI and just had it in the key area that it needed to be used for, like, a giant workflow.

    7. GA

      Mm.

    8. RB

      Everything else was standard infrastructure. That made things actually reliable and economical at a point where the models weren't good enough. But now models are good enough to automate tasks completely end to end, so you don't really need this workflow manually built out. The agent can just do it by itself.

    9. GA

      Who were the first users? What were they building?

    10. MB

      There was, like, an initial wave of f- like, PLG growth, so, like, individuals, indie hackers, people, like, just having really tedious tasks before AI could really automate anything.

    11. GA

      So, so w- like, so w-

    12. MB

      Yeah, exactly. Like, uh, I think during our YC interview, the people... The use case we quoted was someone transcribing their Dungeons & Dragons session.

    13. RB

      Mm-hmm.

    14. MB

      Uh, that was, like, innovative at the time.

    15. GA

      Yeah. Yeah.

    16. MB

      The fact that they could talk for two hours and then have a transcript and talk, like, send it around to their, the attendees. They were using a Gumloop workflow for that.

    17. GA

      Mm-hmm.

    18. MB

      But then little use cases like that, and then all of a sudden we had business users. Like, I remember this Australian health tech company was using us to automate a lot of their research just by, like, pulling from the web and, like, regulating that data and putting it in certain places.

    19. GA

      Mm-hmm.

    20. MB

      And then we realized, like, the enterprise customers are willing to pay a lot more. Their problems are a lot more serious. Um, and, like, the value we provide them is just so much deeper.

    21. GA

      Mm-hmm. How did that realization come about?

    22. MB

      Slowly-

    23. RB

      Yeah

    24. MB

      ... over time. It... I think we always wanted to be the PLG tool that, like, your, your mom would use and, like-

    25. GA

      Mm

    26. MB

      ... that your coworker would be using. But we just saw, like, when you were giving the pitch to different people, like the people who have 1,000 employees and they need, like, a layer to help them all automate, and they deeply need security and observability and access control and all of, like, the boring stuff behind automation, like, they start... Their eyebrows raise higher than, like, the, uh-

    27. GA

      But I'm assuming you didn't get to that pitch right away. There was s-some step function to get there. What-

    28. MB

      It was like a-

    29. GA

      What wa- that, that... What was that like?

    30. MB

      I think, uh, I took, like, 1,100 customer calls the first year.

  3. 7:20 – 11:02

    What It Takes to Win Enterprise Customers

    1. GA

      Were you the best product? Were you, like, the just first product? Like, how, how did you grab, like, have a foot- foot- foothold in, in big tech companies that other companies don't have? How did that happen?

    2. RB

      I think for the workflow automation builder, we genuinely were the best product. We were by far the easiest product to use, and we, our product allowed you to do things that you couldn't do on other platforms.

    3. GA

      Mm-hmm.

    4. RB

      And a lo- um, so some of the people, Aaron, who's our head of community and education, uh, he's taught a bunch of these no-code tools, and the one thing that he always said to us was, um, "The reason that, uh, Gumloop is so capable is 'cause you guys did not know what any other product in your space did or looked like." [laughs] And we kind of built things from, like, first principles.

    5. GA

      Mm-hmm.

    6. RB

      And so the way our thing worked was just a little different than anything else, and it made it so much more powerful. And we realized quickly why other tools don't work that way, 'cause it's actually really hard. [laughs] The problems that we were solving were really hard.

    7. GA

      Mm-hmm. Yo, tell us about these things. So, so, so when you sell to a big enterprise, there are things that they require that small companies don't.

    8. RB

      Mm-hmm.

    9. GA

      Like, for someone who's, like, watching this and wants to sell to enterprise, what are those things specifically?

    10. RB

      Yeah. I think stuff like role-based access control, SCIM, SAML, um, audit logging, all, all that type of stuff is, like, um, you know-

    11. GA

      Why, why do big companies ask for that?

    12. MB

      Uh, there's just so much at stake-

    13. GA

      Mm-hmm

    14. MB

      ... when you're a large company. Like, you're a publicly traded establishment that has, like, sensitive data and all these requirements and, like, restrictions with what you can do.

    15. GA

      Mm-hmm.

    16. MB

      Uh, so you need to make sure that if you're gonna give 10,000 people at your company a tool, they're only able to do the things you want them to be able to do. And certain users... I- it gets infinitely detailed.

    17. GA

      Mm-hmm.

    18. MB

      Like, how, they're like, "These, these 500 users need to be able to do these 10 things. These users are actually an outsourced team that shouldn't have access to any data. We need them to have restrictions on their accounts. We need to use our own API keys. We need to host it in our own cloud. We need to have audit logging for every tool. We need to have APIs to get that into our data lake." Like, it, it never ends.

    19. GA

      Mm-hmm.

    20. MB

      Uh, but that's, like, the value we have to create before the end user can have that wonderful moment of like, "Oh, I connected data and it just worked." Like, there's so much depth before they can use it.

    21. GA

      How-

    22. RB

      Yeah, it can be frustrating sometimes 'cause they wanna disable so much functionality that we worked so hard to build.

    23. GA

      [laughs]

    24. RB

      But, uh, yeah, I guess that's what it takes. And then when they warm up to us, we just enable more and more things.

    25. GA

      How do you figure out what all these things are and how to, h- what they need? 'Cause, like, you can't show up and be like, "Oh, we don't have these things."

    26. MB

      Mm-hmm.

    27. RB

      Yeah.

    28. GA

      Let's go build them. I'm assuming, yeah, you had to figure out along the way.

    29. MB

      There's, uh... It's just conversations with customers.

    30. GA

      Mm-hmm.

  4. 11:02 – 15:00

    How the Founders Met and Got Into YC

    1. GA

      Um, let's go back to, like, earlier. Pr- prior to doing YC, how did you guys meet and decide to start coming together?

    2. RB

      There was an AI conference going on in Vancouver, and I was presenting what I was working on at the time, which was like a personalized AI tutor for students, and Max was presenting an early version of Gumloop. And, uh, what he was building sounded really cool. It sounded really promising. I thought it had real potential to work, and so I basically, like, asked him, like, if he needs a co-founder. Well, actually, he said he needed a co-founder.

    3. MB

      Yeah. There's a couple caveats to the story.

    4. RB

      Yeah.

    5. MB

      There's one... The conference is a very generous term. It was, like, 30 people in a room, uh, just, like, talking about AI before, like, right when the GPT API came out, basically.

    6. RB

      Yeah.

    7. MB

      My co-founder had quit that morning.

    8. RB

      Mm-hmm.

    9. MB

      The guy I was working with, who I met on YC Co-Founder Matching, and he was like, "This idea won't work. I need someone more senior," like this whole laundry list of reasons. I believe the no, but not the why.

    10. RB

      Yeah.

    11. MB

      I learned that after working with YC. But I went to this meetup to take my mind off things because I was so, like, distraught and I'm like, "I'm screwed," basically. There's a bunch of other things that happened in the background that made me more screwed at the time. Rahul was demoing with his co-founder, uh, and that person ended up joining as our founding engineer, like, a year later. But, um, the whole video of us interacting for the first time is captured on my Twitter. I was like, "My co-founder quit this morning. I'm looking for another one," and then the next person to speak is Rahul asking about GPT functions-

    12. RB

      Yeah

    13. MB

      ... uh, which had just come out that day.

    14. RB

      Yeah.

    15. MB

      And he's like, "Have you considered those?" And I'm like, "No, I don't even know what that is."

    16. RB

      [laughs]

    17. MB

      But it was very serendipitous. And then we got drinks the next day, and he came with, like, his laptop that was duct taped together-

    18. RB

      [laughs]

    19. MB

      ... uh, w- with, like, a Google Doc of, like, 50 questions, 'cause the product was open source at the time, so he read all the code before AI could just answer questions about, like, a code base.

    20. RB

      Yeah.

    21. MB

      And he had, like, very detailed questions about the framework that I had built, so I was like, "This guy's pretty serious." And then we worked in a co-working space together for, like, a month and-

    22. GA

      And you were both in Vancouver?

    23. MB

      Yeah.

    24. RB

      Yeah, in Canada.

    25. GA

      And then w- how-- From there to applying to YC?

    26. RB

      Yeah, I think we always wanted to apply to YC. Max knew way more about it. He had applied to YC three times in the past. I had never applied to YC.

    27. GA

      Mm-hmm.

    28. RB

      But we knew what YC was, and so-

    29. MB

      Yeah. I'd applied with very different ideas previously.

    30. GA

      Mm-hmm.

  5. 15:00 – 18:02

    How Gumloop Uses Its Own Agents

    1. GA

      Got it. Can you talk about how you use Gumloop internally at Gumloop?

    2. MB

      Yeah. I think everyone on every team uses it for everything.

    3. RB

      Mm-hmm.

    4. MB

      The first, like, breakthrough agent that changed the way we work was this one we call Heimdall, which is connected to every single data source at the company.

    5. GA

      Mm-hmm.

    6. MB

      Um, and it has a Slack channel that was just Ask Heimdall originally, but now it's, like, in every single channel across the entire company. And you can ask it anything. Like, "Get me all the users that signed up yesterday morning that are in this time zone that have this role, and, uh, aren't in this certain cam- email campaign." It'll, like, work for 20 minutes and get you that answer, so we don't need, like, a data analytics, uh, uh, analystist or analyst.

    7. RB

      Analyst.

    8. MB

      Um, we just have, like, g- Heimdall answering every customer-related question, and then that spawned a bunch of ideas. Like, the sales team went off in the- their direction, and they have Sales Loop, which automates everything in the sales process, so they can just focus on talking to customers. Our customer success team has a Success Loop, which, like, the health score of every single customer is, like, diligently tracked. Ev- they get notifications if anything's ever going wrong, if any usage is skyrocketing or dipping.

    9. GA

      Mm-hmm.

    10. MB

      Like, everything that they were spending their time monitoring is handled for them.

    11. GA

      Yeah.

    12. MB

      Our education lead, we have cohorts every two weeks where, like, he teaches hundreds of people how to build agents.

    13. GA

      Mm-hmm.

    14. MB

      The entire process is automated from, like, people signing up, the follow-ups, the, uh... Even the editing of the videos when they demo on Fridays and show their agents, fully automated so that the clips get sent to those customers, and they can share it around their companies.

    15. GA

      Throughout this process, w- was there a moment when you felt, "We have PMF. This is work- working"? Like, like, was there something, like, very vi- visceral that you remember, or was this more gradual?

    16. MB

      I think there's been a bunch of moments like that.

    17. GA

      Yeah.

    18. MB

      But the... I remember the first one, uh, felt like our first conscious thought about whether this is something that people actually want was when they were using our initial workflow builder early, early days, and it was awful, but they were using it anyways.

    19. GA

      [laughs] Right.

    20. MB

      And we're like, "We're actually..." I've built a lot of bad software before that no one used, and for some reason, people were, like, spending five hours of their afternoon, like, building a workflow on our platform. And we're like, "Oh, this is actually so painful for people that they're gonna... They're willing to put up with how bad this is." And, um, I think that feeling of, like, "Wow, people are really investing their time," has hit us every couple months. But I don't know if you have anything else.

    21. RB

      Yeah. I think, like, when we realized that certain users were spending, like, eight hours a day, 10 hours a day using Gumloop, building stuff, like automating their entire jobs or, like, the job of their entire team, that was, I think, big. It, it was when it... When we realized, like, yeah, our users are spending way, way more hours than we are in Gumloop, you know, building stuff. And, like, some of them know how to use the platform better than, you know, we do in some ways. I think that for me, one of the big moments was, like, the first day we launched at Shopify because it was kind of like, um, we basically just, like, went out to, like, 4,000 people [chuckles] on the same day.

    22. MB

      I think it was 8,000 people.

    23. RB

      8,000 people on the same day.

    24. MB

      Yeah. They just got access and like-

    25. RB

      And just seeing people kind of like pick up and, like, use the tool and, like, play around, it was, like... It was a super cool moment, and people were just, like, figuring things out and, like, we didn't have to... I think before then it was a lot of, like, sitting down with customers and, like, building stuff o- off, out, out for them, and, like, a lot of hand-holding. But then, yeah, I don't know. People just started building stuff on their own, which was

  6. 18:02 – 21:23

    Landing Shopify and Rethinking Pricing

    1. RB

      cool.

    2. MB

      Yeah.

    3. GA

      How did you get Shopify? I, I, I remember Toby writing the AI native sort of like-

    4. MB

      Mm-hmm

    5. GA

      ... sort of like his manifesto almost.

    6. MB

      Mm-hmm.

    7. GA

      Uh, it sounds like could not be a better customer than them. How, how did you get them as-

    8. MB

      It was actually... That was one of the word of mouth customers, where, like, someone from Instacart had influence at Shopify and just put it on their radar-

    9. GA

      Yeah

    10. MB

      ... and then they evaluated the tool.

    11. GA

      Yeah.

    12. MB

      I think they, um, like, rolled out a bunch of tools the same week.

    13. GA

      Mm-hmm.

    14. MB

      Like, different automation tools.

    15. GA

      Yeah.

    16. MB

      And they just saw what stuck.

    17. GA

      Mm-hmm.

    18. MB

      Um, and then they ended up investing in Gumloop after 'cause they, they really liked-

    19. GA

      Wow

    20. MB

      ... the rollout. Um, and they've been a great partner since.

    21. GA

      Got it. What percent of employees of these companies are using Gumloop on a, like, a daily, weekly basis?

    22. MB

      It varies a ton by company. Sometimes it's, like, extremely concentrated usage with one team, like everyone using agents to automate this one function. Sometimes it's, like, wall-to-wall deployments with, like, light usage on some teams and deep usage-

    23. GA

      Yeah

    24. MB

      ... on others.

    25. GA

      Mm-hmm.

    26. MB

      Sometimes people are using Gumloop, but they don't even realize it. Like, they're l- getting all these reports and stuff so that they, they can do their job better or, like, the prep for a customer call or something, but it's, it's all Gumloop in the background. So there's, like... It's, uh, completely different patterns at every company. But for the most part, it's, uh, everyone has access. So, like, we never charge per seat. I never want someone to, like, second-guess whether they should be using this tool or not.

    27. GA

      Mm-hmm.

    28. MB

      It's just usage-based. Um, so whoever wants to start automating can just turn to Gumloop and figure it out.

    29. GA

      That's not how you get started, though. W- when did you switch to usage-based pricing compared to... Initially it was seat, seat based, right?

    30. RB

      Yeah. We had a limit on the team plan-

  7. 21:23 – 24:35

    Fundraising and the Limits of Staying Lean

    1. GA

      You've raised three rounds of funding. Uh, well, after YC. First round, and then, uh, Nexus, and then Benchmark.

    2. MB

      Mm-hmm.

    3. GA

      Can you just talk a little bit about h- how they all came, came, came around, and sort of like what you've learned about fundraising?

    4. MB

      Mm-hmm. The first round, um-

    5. GA

      Mm-hmm

    6. MB

      ... with first round [chuckles] was, uh, the most... Like, that's the only time we actually had fundraised.

    7. GA

      Yeah.

    8. MB

      Um, we did the whole process, like what YC helps you get set up to do, like a week of meetings and talking to every VC.

    9. GA

      Mm-hmm.

    10. MB

      Uh, extremely tiring, uh, and I think they were the best possible firm we could've went with. Like, they've been amazing partners. It's very similar to, like, dating, I guess, or I don't know what analogy to use. But building the company and b- gaining traction is the most important thing you can do. It's not about, like, tricking an investor to think that your company's cool or having a better pitch deck than someone else.

    11. GA

      Yeah.

    12. MB

      Like, they will always follow your company and, and try to invest if you're showing that you're changing the way people work or, like, having some impact in the market. So our other two rounds were preempted. Um, we didn't raise, we didn't make a deck. We, we just, uh, kept building, and I think the fact that we didn't need the money and we were gonna succeed regardless is what makes someone want to invest.

    13. GA

      What's the experience of being, um, a Canadian founder moving to San Francisco? I, I remember there was, like, some tweet storm about, about this or, like, advice to Canadian founders who either want to stay in Canada or people who want to move here. Like, what's...

    14. MB

      Yeah.

    15. GA

      What comes to mind?

    16. MB

      You can absolutely stay in Canada. Like, there's no... We have an office in Canada.

    17. GA

      Mm-hmm.

    18. MB

      We employ a ton of Canadians. I, I think we're gonna be going back to Canada at some point.

    19. GA

      Okay.

    20. MB

      Um, so I don't think it's a you must move to SF like a lot of people say. I think our approach was, like, we had every odd stacked against us. We still do.

    21. GA

      Mm-hmm.

    22. MB

      And, like, we want to succeed by any means necessary.

    23. GA

      Mm-hmm.

    24. MB

      So if there was a half a percent better chance that we might meet a customer or a, a, someone we want to hire in San Francisco 'cause they happen to be down the street, like Instacart is, was, like, a block away from our initial office-

    25. GA

      Yeah

    26. MB

      ... we were gonna do that. But I, I think you can 100% stay in Canada and build, like, a generational company there. There's a lot of great Canadians who are comfortable and happy, and they don't want to leave the country.

    27. GA

      Yeah.

    28. MB

      Um, you can hire them, and you have a kind of a, an advantage in that market for hiring.

    29. GA

      What are the best hubs or strongest, like, either most ambitious or best engineering talent right now?

    30. MB

      I think it's just where more people are.

  8. 24:35 – 27:43

    Letting Employees Automate Their Own Work

    1. GA

      What constrains success for the company right now? Is it, like, models are still, like, not perfect the way they need to be? Or like, like, what, what there'll be additional unlocks in the future, do you think?

    2. MB

      People don't know about us.

    3. GA

      Mm.

    4. MB

      That's, uh, one big thing.

    5. GA

      Mm-hmm.

    6. MB

      Sometimes, like, our-- I have calls with customers almost every single day where we're-- they're like inbound, or they heard about us through a friend, and then I actually show them the tool, and they're kind of shocked at how much it can do.

    7. GA

      Mm-hmm.

    8. MB

      Um, we're trying to fix that so that more people know we exist and how much work we can automate.

    9. GA

      Mm-hmm.

    10. MB

      That's one problem. The other one is we just don't have enough people.

    11. GA

      Mm.

    12. MB

      As ironic as that sounds based on, like, the small team thing.

    13. RB

      [laughs]

    14. MB

      Uh, there's just, like, 100 things that our customers are asking us for that we could be building right now.

    15. GA

      Yeah.

    16. MB

      But we're very thoughtful, I think, about who we hire and whether they're a good fit for the company. So it just-- We've been hiring more slowly than the average company.

    17. GA

      Mm-hmm.

    18. MB

      But we-- we're hiring engineers, designers, AEs, customer success.

    19. GA

      If, if I was someone who's, like, never used anything like this, and you're like, "I'm, I'm AI curious, and I work for a big enterprise," what's the vision that you would sell me on or vision that you would describe to me of, like, this is how you could run your company differently? Like, is, is there, like, a-

    20. MB

      Yeah

    21. GA

      ... a way to summarize how, how, how a company like your customers are compared to the, the ones that are not?

    22. MB

      I think the, the main eth- like, thesis is, like, the people who understand the tasks should be the ones automating them. Um, and the anti-pattern we've seen over the last couple years is, like, you might have a, a marketing ops team that has, like, something extremely tedious and cumbersome that you're losing tons of revenue by not doing. They're gonna hire people if-- to solve it, or they're gonna, like, write a spec doc of the problem they want solved with an agent or AI, and they're gonna give that to an engineering team, or they're gonna give that to a consultant-

    23. GA

      Mm-hmm

    24. MB

      ... uh, who's gonna charge them a crazy amount of money, and they're gonna go through this loop of, like, not quite right, feedback, meetings, and you'll get something half-baked. I think, like, the people who really deeply understand a task should be the ones, like, just transferring what they understand into an agent-

    25. GA

      Mm-hmm

    26. MB

      ... to automate it. And the hard part is, like, the, the biggest blocker is the, a tool that they can understand. Now we're at the point that anyone can build an agent, so that's relatively solved. And then the meta problem behind it is, like, do they have access to the right data, the permissions? Like, has IT-

    27. GA

      Mm-hmm

    28. MB

      ... let them do this sort of thing? So we're secretly, like, catering to IT teams with all the features we build first.

    29. GA

      Yeah.

    30. MB

      'Cause if they give the green light, then anyone can automate, like, hundreds of hours of work and scale their output.

  9. 27:43 – 31:23

    Building Agents Companies Can Control

    1. MB

      Yeah.

    2. GA

      What's the hardest challenges that you guys are facing right now? So someone who's thinking about joining at Gumloop, um, what, what would they work on?

    3. RB

      Yeah, I think it's a lot of the stuff that the labs are dealing with as well and the biggest challenges the labs are, uh, facing at the application layer.

    4. GA

      Mm-hmm.

    5. RB

      Which is, like, how do you get the most out of the agents and the models in their current form, and how do you get them to do real work? How do you get them to do, like, long-running tasks that take, you know, multiple hours? Uh, how do you get them to, like, use computers and, like, browsers and efficiently and stuff like that? I think a lot of the appeal at working at a company like Gumloop is that you're really at the frontier and pushing the limits of what's possible to do with AI and the current models. Um, and I think that's really exciting for people, especially people that are at a more kind of traditional company.

    6. MB

      There's a-- Every feature that people think we should build and that we start building-

    7. GA

      Yeah

    8. MB

      ... comes with, like, 100 features underneath them that are secretly the hard part.

    9. GA

      Mm-hmm.

    10. MB

      Like, yeah, browser automation is something we're working on. Uh-

    11. GA

      Mm-hmm.

    12. MB

      But then, okay, if people can automate th- with their browsers with agents in the cloud and on their local desktop, how does IT make sure that they're not going to certain websites?

    13. GA

      Mm.

    14. MB

      Or that it's not doing anything nefarious, or that-

    15. GA

      Yeah

    16. MB

      ... uh, they're able to watch back, uh, a session recording. And then the last, like, how do I watch back 5,000 session recordings and understand what agents are doing in aggregate?

    17. GA

      Mm.

    18. MB

      How do I make sure it's efficient from a cost perspective? Like, everything that comes with those features is the hard part.

    19. GA

      Mm-hmm.

    20. MB

      Um, so we have to think of, like, the whole picture when we're deploying it in a, in a company.

    21. GA

      Mm-hmm.

    22. MB

      But there's so many little edges to every feature that we need great engineers to work on.

    23. GA

      What's the dynamic with the labs? 'Cause you, you mentioned in the past that your customers already have relationship with the labs.

    24. MB

      Mm-hmm.

    25. GA

      And, um, like, uh- Some of them have competing products. I'm assuming Cowork is in this, in this world as well.

    26. MB

      Totally.

    27. GA

      Yeah.

    28. MB

      Yeah, there's a ton of competition.

    29. RB

      Mm-hmm.

    30. MB

      I think we exist in, like, this sweet spot in between the labs where, um, we're kind of like the Switzerland between the labs, and you can use your inference from wherever.

  10. 31:23 – 34:15

    Getting IT to Say Yes

    1. RB

      efficient.

    2. GA

      Um, typically there's one buyer for, for each product category, one product.

    3. RB

      Mm-hmm.

    4. GA

      But it sounds like in this case it could be still a bit bottom-up where someone actually discovers this. Like, how does it bubble up to the decision-maker?

    5. RB

      Mm-hmm.

    6. MB

      It's like a riddle every time.

    7. RB

      Mm-hmm.

    8. MB

      Uh, it's different. We're d- definitely forming more of a pattern, but the person that it bubbles up to and the person that gets most excited is normally, like, an IT leader, leader. Like, the person responsible with the AI rollout and security of the fact that, like, everyone's using 80 different tools-

    9. GA

      Yeah

    10. MB

      ... and the data's going everywhere, and they need to kind of wrangle all of that.

    11. GA

      Yeah.

    12. MB

      Um, they tend to be the person we, we're most excited to meet 'cause their eyes kind of light up when they see our feature set.

    13. GA

      Yeah.

    14. MB

      And then they're the ones unblocking, uh, the sales team to do sales related use cases, and the customer success team to do tho- their use cases 'cause they're giving them access to all of, like, the internal data. But yeah, it tends to bubble up to them, and, and sometimes it starts with, like, an individual who found us on Twitter or saw our post on LinkedIn, and then they showed it to their manager, they showed it to their manager. We gave them a pilot. They showed they generated, like, 3X more revenue with a small sales team, for example, in a week than they did la- like, the, the three months prior.

    15. GA

      Can you give a pilot to them without getting to the decision-maker?

    16. MB

      We have to talk to IT to get the pilot working.

    17. GA

      Right. Right.

    18. MB

      Uh, that, that's, like, the hard part.

    19. RB

      Yeah, it's something we're learning and figuring out over time still. But it's not super helpful to have a pilot, uh, and then they can't connect any of their tools. [laughs]

    20. GA

      Right.

    21. RB

      So, like, we kind of-

    22. MB

      We made that mistake in the past.

    23. RB

      Yeah.

    24. GA

      The number of connections that you have access to is, is directly correlated to how, how good your experience is to use-

    25. MB

      Yeah

    26. GA

      ... the product. How important was it for you guys to do YC and move to San Francisco?

    27. RB

      I think it was pretty critical. [laughs]

    28. MB

      Yeah.

    29. GA

      Yeah. [laughs]

    30. MB

      We were, uh... It gave us, like, instant validation-

Episode duration: 34:15

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