InKeep raises $13M seed to build no-code/code AI agent builder for technical customer support

gong's still swinging. We'd love to see it. That's fantastic. Anyway, we have our next guest coming into the TBP and Ultradome. They're in the reream waiting room. Let's bring in Nick Gomez from In Keat. Welcome to the stream, Nick. How you doing? Sorry to keep you waiting. Welcome. Appreciate it.

Uh, kick us off with an introduction on you, your company, what you're building, any news you got for us. Yeah, absolutely. So, I'm Nick, founder of Inkeep. Um, we we just raised 13 million to help. [Applause] There we go. I was hoping you'd say that. Thanks. You're not not bearing the lead on there. 13 million.

That's a good seed round. Uh appreciate everything. Uh so yeah, what what how are you going to put the money to to work? How are you going to what are you planning to build? What's the progress of the company so far? Yeah, absolutely. So happy to give you kind of the the problem statement if you will.

Um so our experience is that we were working with very technical companies. So companies like Enthropic and Midjourney, Pinecomb, Salana, so very like tech forward leading companies um to create AI assistants that they embed in like their help center or in their doc.

So kind of like customer experience agents but specifically for very developer uh technical products. So that's like kind of where we started.

Um and what we happened to notice in that experience um is that half the time we were talking to like a VP of engineering or a CTO and half the time we were talking to like a VP of customer support or of documentation.

Um, and their needs are like similar because they're both trying to like create these AI systems for their users and like automate different, you know, things internally to make their teams more efficient. Um, but obviously they approach things from a different angle.

So like an engineer or a CTO cares about like, hey, how do I control these agents, what data they have access to, how do I integrate them with my like APIs and like my software stack, etc.

and a VP of support is more like hey I just want something that kind of works out of the box and like integrates with my ticketing platform and lives on Slack etc. I don't want to be coding anything. That's not the job of my team.

So, we were getting like this, you know, kind of uh duality of like the the types of people that we were dealing with. And again, this is even within very technical companies. Um and so that caused a lot of friction, right?

Because if your engineering team is the one that creates your AI assistant, then anytime your support team needs a change, they need to go ping engineering that like, hey, can you change this thing?

Um and then vice versa if your um like VP of support or your support team uses kind of like a no code builder or you know buys a thirdparty SAS application um to help them with like the support agent then the engineering team has no hands on the process like they don't know what data it has access to they can't help with the integration etc.

Um, so it creates this like very hard fork in the road. Um, and so that that was kind of like our our big learning um, with kind of those early customers. Um, and so what we just launched as part of the fundraising announcement is a no code plus code visual builder and TypeScript SDK.

So it lets uh, no code teams like support teams, sales teams, marketing teams create AI agents that are also editable as code.

Uh so we have like a nice typescript SDK with a good developer experience that so to to simplify like an example workflow let's say a support team builds uh they they have some information on their website about how to fix a problem or they have a help center and then they would be able to instead of a user has a problem they go to the help center they they read about the issue that they're experiencing and then in theory they could hit a button that would just solve the problem like an agent that would just solve the problem for for them instead of having to contact and actually submit a ticket.

Is is that a potential workflow that that somebody would build? Yeah. So the the typical use cases for us is like you're creating an assistant that is customerf facing. So like a chat basically that you put on your website on your marketing site etc. So you can have customerf facing AI agents.

Um but teams also use agents internally to help uh you know like like look up information about a user or the the flow that you just described. um which is more like a human in the loop. Hey, my support team or my sales team is using an agent directly to help automate different tasks.

Um so we we focus primarily on like the assistants um but also the kind internal co-pilots as well for for teams. Um what's neat is like it's all powered by the same knowledge about your product, right? Uh and like access to the same backend systems, your Stripe, your CRM, your support ticketing platform.

So there's a lot of commonalities there. How much reasoning uh tokens are you guys actually using at this point versus just like optimize, you know, basically like automating steps in a process and and giving people that visual builder. Yeah, for sure.

So, one thing I think that that we do differently compared to like an NAN or a Zapier, etc. Those are um th those have very like structured sequential uh like roots. That's that's how they were built. They were like workflow automation platforms. Um our platform is fully agentic.

So it's all LLMs deciding every step of the way how to move forward. So um these things can get pretty complex but it's like the the same stuff that like powers chipd deep research where it's breaking it down into like sub agents and combining the answers at the end etc.

Um so everything is agent driven um which is great for any type of process that has a like human inputs. So like support tickets uh are a great example of um types types of workflows that are better served by like pure purely agentic workflows. That makes sense.

How should I read into the fact that you're working with anthropic? It feels like their whole thing is we're the best at AI coding. We solve software engineering. We should be able to build everything internally.

and yet you show up and you're working with them and it's like it's hard to be bullish on both companies simultane like there's some cognitive dissonance that I want you to help me work through because I know it can't actually be that crazy but do you see like kind of why I'm struggling to understand well it's very possible Nick is leveraging cla I guess but yeah yeah like like what like uh why why doesn't Anthropic build their own uh you know tool here yeah I mean honestly they could totally go do that if they dedicate their engineering resources to that.

Um, but I think it speaks to what I was talking about earlier where like, you know, the their VP of support or their VP of documentation, they don't want to go build a chat UI and like figure out how to integrate this into like Zenesk or Intercom or whatever it might be. Figure out how to make a Slack out for it, etc.

Yeah. So, you still have the need for like these like higher abstraction building blocks. Yeah. Uh, like code, you know, cloud code is very low level. It's designed for kind of software engineers, that type of stuff.

Uh, and what what we're doing is is kind of bringing that up like an abstraction layer where it's accessible to a support team, a sales team, etc. Yeah, it's just interesting.

We just talked to the founder of Open Phone now Quo and uh it was like the exact opposite starting with like the HVAC repair man needs an AI agent just to answer the phone and it's like well that person like doesn't even know what cloud code is.

Like they're never going to build their own solution so you bring them a product. But it's just very interesting to go top down. I imagine that helps with growth.

Um, do you feel like having the big AI labs has helped you kind of draft off of their massive growth rate because they're growing so fast that you just kind of naturally grow super fast as well? Or is it more that they're just a proving ground and if you can satisfy them as a customer, you can satisfy anyone?

I mean, it it's both like having them as test cases, but also kind of learning about what what they're doing is is always like great to to see and like build on our insights.

But like an example there is I I think the the frontier model providers like Enthropic and OpenAI um they were the first ones to really say like hey don't add so much scaffolding and sequential stuff and structured stuff just like let the agents cook if you will let let the LMS do the work.

Um and so that inspired a lot about uh like how how we build these workflows on the no code builder. Um it's actually pretty simple in the end. is like LMS, you give them tools and you give them data.

Uh, and then you let them agents talk to each other and then with just those kind of primitives, you can you can build very complex things. Um, so we definitely like, you know, learn from from what they're doing. That makes a ton of sense. Uh, well, thank you so much for hopping on. Yeah, thanks for breaking it down.

Congratulations. Cool white space you've carved out. Very interesting. Uh, have a great rest of your day. We'll talk to you soon. Congrats. Cheers. Let me tell you about ad out of home advertising made easy and measurable. Say goodbye to the headaches of out of home advertising. Only adqu combines