Landbase Raises $30M to Scale GTM-1 Omni – Reinforcement Learning Meets Go‑to‑Market

We built Landbase to automate B2B go-to-market with AI. Our Series A ($30M co-led by Sound Ventures and Picus Capital) gives us the resources to expand our platform. In this post, we share how GTM-1 Omni, our proprietary agentic AI model, works – including its multi-agent architecture and reinforcement learning approach – and the results it's driving (4–7x higher conversions, campaigns launched in minutes). We'll also discuss what this funding means for the platform’s future.

From Stealth to Series A: An AI Approach to GTM

Go-to-market for B2B companies is ripe for disruption. Today, sales and marketing teams juggle a dozen tools for prospecting, outreach, and CRM, often spending as much time wrangling software as they do engaging leads. As founders experienced in the GTM space, we saw an opportunity to unify and automate these workflows with an AI-driven “system of action.”

Landbase emerged from stealth in late 2024 with this mission: build the first agentic AI platform specifically designed for go-to-market execution. Instead of just another analytics dashboard, we created an AI that can act autonomously to find prospects and initiate outreach. Our team, which includes machine learning PhDs (ex-Everstring, NASA/Stanford) and GTM veterans, spent years iterating on this concept. We secured a $12.5M seed round in 2024 to develop the core technology, and as we gained traction (150 customers, 825% revenue growth in a few quarters), investors took notice again.

This June, we raised a $30M Series A co-led by Ashton Kutcher’s Sound Ventures and Picus Capital. For us, this funding is fuel to accelerate R&D and scale our platform to meet growing demand. But what exactly is under the hood of Landbase’s AI platform? Let’s dive into GTM-1 Omni.

Inside GTM-1 Omni: Multi-Agent AI with Reinforcement Learning

GTM-1 Omni is our proprietary AI engine that powers Landbase’s platform. At its core, GTM-1 Omni is a domain-specific large language model (LLM) specialized for sales and marketing content, augmented by a suite of action models that can perform tasks in the GTM workflow. In other words, it's not a single model, but an orchestrated system of AI agents working together to plan and execute campaigns. One agent might analyze ideal customer profiles and compile a prospect list; another generates personalized email content; another handles scheduling and sending messages, and so on. This multi-agent architecture allows Landbase to automate the end-to-end process of outbound campaigning in a way traditional tools can’t.

We trained GTM-1 Omni on a massive dataset of B2B go-to-market interactions – over 40 million historical campaigns and 175 million sales conversations. This gave our models a rich understanding of how different messaging, targeting, and sequences perform in real-world scenarios. But we didn’t stop at pre-training. A key innovation has been applying reinforcement learning to continually improve results. Specifically, we employ a method we call reinforcement learning with human and performance feedback, combining expert feedback with live campaign outcomes as learning signals. For example, the system might A/B test two email approaches; if one yields higher reply rates, the model weights that strategy higher in the future. Over time, GTM-1 Omni becomes smarter with each campaign, optimizing for the metrics that matter (opens, conversions, pipeline generated).

This approach has led to outsized gains:

• 4–7x higher conversion rates compared to manual outbound campaigns (even versus baseline GPT-powered scripts, we’ve observed ~7x improvement).
• 70% reduction in time spent per lead by sales teams, thanks to automating prospect research and outreach tasks.
• Campaign launch times cut from weeks to minutes. (Prior to Landbase, orchestrating a multi-touch campaign could take 14–30 days of prep; with our platform, a rep can launch a tailored campaign in the same day.)

To put it simply, GTM-1 Omni acts like a high-output AI sales development team that works 24/7. And it keeps getting better. In April 2025, we rolled out a Campaign Feed interface that lets users watch the AI agents work in real-time and provide feedback, further closing the loop between human intuition and machine efficiency.

Next Steps: Scaling Up and Opening Up

With the new funding, our focus is on scaling and refining this technology. On the ML side, we're investing in larger and more specialized models for GTM-1 Omni, as well as advancing our reinforcement learning algorithms with more granular performance data. We’re also building out more autonomous agent capabilities – imagine future GTM-1 Omni agents that can adjust your campaign strategy on the fly or handle inbound prospect responses automatically.

Another priority is making Landbase accessible to a wider audience of businesses and developers. We recently launched a free preview program, allowing anyone to try Landbase’s AI for themselves. If you’re curious to see how an AI-driven campaign works, you can sign up on our website, describe your target customer in a prompt, and let GTM-1 Omni generate a campaign plan for you. It’s a hands-on way to experience the power of an agentic GTM platform.

In the long run, we envision Landbase as a fundamental tool for go-to-market teams – akin to what CI/CD pipelines are for software teams. By offloading the repetitive grunt work of prospecting and outreach to intelligent agents, human teams can focus on strategy, creativity, and building relationships. As one of our investors put it, we want to shift GTM from a world where humans serve software to one where software works for humans.

Thanks for reading! We’ll continue to share technical insights as we grow. If you have questions about how GTM-1 Omni works or ideas for what you'd like to see it do next, let us know in the comments. And if this space excites you, we’re hiring AI engineers and full-stack developers to help build the future of go-to-market – come join us!