#vibegtm

Introduction: The AI Shift in Go-to-Market

B2B sales is being redefined by artificial intelligence. Traditional GTM strategies—manual prospecting, endless tools, and human-heavy workflows—are quickly being replaced by AI-powered platforms designed to make outreach faster, smarter, and more cost-effective. Two platforms leading this transformation are Landbase and Unify, each offering its own vision of how GTM automation should work.

But the question remains: which one actually delivers better results for SMBs and growth-focused companies?

Landbase: Agentic AI Built for GTM

Landbase positions itself as the first agentic AI platform purpose-built for go-to-market execution. Its proprietary GTM-1 Omni model is trained on billions of sales interactions, signals, and account data points. Unlike generic automation tools, Landbase deploys specialized AI agents that function like digital SDRs, marketers, and RevOps managers.

Key advantages include:

Autonomous Campaign Execution – Launch personalized, multichannel campaigns in minutes.

Hyper-Personalization – Messages adapt to role, industry, and buyer intent.

End-to-End Management – From domain health to deliverability, Landbase handles the heavy lifting.

Proven ROI – Customers report 7× higher conversion rates and 60–80% lower costs than legacy methods.

In short, Landbase aims to eliminate manual busywork and deliver pipeline as a service, powered by autonomous AI.

Unify: Warm Outbound with Intent Data

Unify takes a different approach, branding itself as a “warm outbound” platform. Instead of fully autonomous execution, it emphasizes intent-driven workflows that convert cold outreach into more targeted engagement.

Notable features include:

Intent Data Integration – Surfaces accounts showing active buying signals.

Custom Campaign Workflows – Teams configure sequences for outreach.

Warm Engagement Model – Focuses on relevance rather than volume.

However, while Unify has gained traction with strong funding and name recognition, customer reviews highlight issues with complex setup, clunky workflows, and poor support experiences. Many users note that while the intent signals are useful, the execution often requires significant manual oversight—making it less efficient for lean teams.

Ease of Use and Setup

One of the biggest differences between the two platforms is how quickly teams can get started.

Landbase is built for simplicity. Non-technical teams can spin up campaigns within hours, with the AI handling domain setup, copywriting, and deliverability. It’s a plug-and-play system for SMBs without large ops teams.

Unify, by contrast, requires extensive configuration. Teams need technical bandwidth to customize workflows, integrate data sources, and monitor execution. For some companies, this flexibility is a plus, but for others, it creates friction and slows down GTM momentum.

Campaign Performance and ROI

Both platforms promise better outbound efficiency, but their approaches drive different outcomes:

Landbase prioritizes pipeline results with autonomous personalization and optimization. Its AI agents continually adapt campaigns based on performance, maximizing ROI without extra manual effort.

Unify delivers value through intent-based targeting, but results depend heavily on how well teams configure workflows and manage campaigns. Companies with strong internal ops may see benefits, but others risk underutilizing the platform.

Industry feedback consistently shows that Landbase delivers higher ROI for resource-constrained teams, while Unify’s performance skews toward organizations with technical capacity.

Final Verdict: Landbase vs Unify

Choosing between Landbase and Unify depends on your company’s size, resources, and priorities:

If you want an autonomous AI solution that runs campaigns end-to-end, minimizes manual work, and maximizes pipeline with minimal setup → Landbase wins.

If your team prefers granular control, has technical bandwidth, and wants to experiment with intent-driven workflows → Unify could be a fit.

That said, for most SMBs and growth-stage companies looking for fast results, Landbase emerges as the more practical, ROI-driven choice. It replaces fragmented tools and manual processes with a self-driving GTM engine, allowing teams to scale pipeline without scaling headcount.

The Smarter Choice for GTM Execution

The future of GTM lies in automation—and while both Landbase and Unify offer innovative AI-powered solutions, their philosophies diverge. Landbase is built to deliver results now, while Unify leaves more of the heavy lifting to the customer.

Introduction: Why GTM Needs Reinventing

B2B sales has changed dramatically. Buyers today are digital-first, self-directed, and impatient. In fact, studies show that by 2025, 80% of B2B sales interactions will happen via digital channels, and nearly half of millennial decision-makers prefer to avoid speaking with a rep altogether.

Yet many GTM (go-to-market) teams are stuck with outdated playbooks—manual prospecting, juggling 10+ tools, and endless admin work. The average rep spends just 28% of their week actually selling, with the rest lost to coordination and busywork.

This is where VibeGTM comes in.

Coined by Landbase CEO Daniel Saks, VibeGTM is an AI-driven, autonomous GTM framework powered by agentic AI. Instead of teams cobbling together campaigns by hand, VibeGTM uses intelligent agents to plan, launch, and optimize GTM execution—running in the background like a self-driving engine.

The result? Campaigns that launch in minutes, cost up to 80% less, and generate 7× more pipeline than traditional methods.

Why Traditional GTM is Broken

Before diving into VibeGTM, it’s worth asking: why do current GTM models fail?

Slow Campaign Launches – Planning and execution often take weeks, causing missed opportunities.

High Costs – Scaling requires more SDRs, marketers, and operations staff—costs grow linearly.

Tool Overload – Teams juggle fragmented systems, causing data silos and inefficiency.

Poor Personalization – Generic outreach falls flat with empowered buyers.

Limited Multichannel Execution – Coordinating across email, LinkedIn, phone, and ads is hard without unified systems.

The result: campaigns that are slow, expensive, and ineffective in a market where buyers move at lightning speed.

What Makes VibeGTM Different?

VibeGTM flips the old model on its head by introducing an autonomous GTM engine. Here’s how it works:

Agentic AI at the Core – Specialized AI agents act like digital SDRs, copywriters, and RevOps managers. They research prospects, craft outreach, and optimize execution in real time.

Always-On Campaigns – VibeGTM runs 24/7, engaging buyers across time zones and off-hours.

Personalization at Scale – Outreach is tailored to industry, role, and behavior—not just names in templates.

Unified Data – By combining contact databases, engagement metrics, and intent signals, VibeGTM eliminates silos.

Cost Efficiency – Instead of adding headcount and tools, one AI-driven system does it all at a fraction of the cost.

Think of it as going from manual driving to autonomous driving—but for your GTM strategy.

The Results: Pipeline, Cost Savings, and Time Freed

The numbers from early adopters speak volumes:

7× higher conversion rates compared to legacy GTM approaches.

60–80% lower costs by replacing manual SDR, marketing, and ops tasks.

$100M+ in pipeline generated since launch.

100,000+ hours saved in manual work.

By taking over the repetitive grind, VibeGTM lets sales teams focus on building relationships and closing deals—the work humans do best.

A Practical Example of VibeGTM in Action

Let’s say a SaaS company wants to target mid-market CFOs. With traditional GTM, the process looks like this:

Build a list from databases.

Have marketers draft sequences.

SDRs launch campaigns and follow up.

RevOps monitors deliverability and analytics.

This could take weeks and involve multiple tools.

With VibeGTM, it’s different:

You define your ICP (CFOs, mid-market SaaS).

The AI finds high-intent prospects, writes tailored outreach, and launches a campaign—all in minutes.

Messages adapt automatically to engagement.

Analytics update in real time, with optimization built in.

Instead of juggling tasks, you’re reviewing results.

Why VibeGTM Matters in 2025

The 2025 B2B landscape makes VibeGTM more relevant than ever:

Buyers are digital-first – They expect instant, personalized outreach.

Sales cycles are faster – Missing a window means losing a deal.

Competition is fierce – Companies that adopt AI-first GTM gain a serious edge.

Budgets are under pressure – Efficiency and ROI are top priorities.

VibeGTM isn’t just a “nice to have.” It’s quickly becoming a business imperative for companies that want to stay competitive.

Takeaways

VibeGTM represents a paradigm shift in how companies approach GTM. Instead of throwing more people and tools at the problem, it brings intelligence, automation, and scale together in one system.

For SMBs, it levels the playing field. For enterprises, it reduces cost and complexity. For everyone, it unlocks faster, smarter, and more profitable growth.

 Explore the 2025 marketing playbook: how VibeGTM and agentic AI help B2B teams cut GTM costs by 80% while scaling outreach and boosting results.

Key Takeaways

VibeGTM (built on Landbase’s agentic AI) automates the complete go-to-market (GTM) process — planning, executing, optimizing — so smaller businesses can scale outreach with less overhead. 

Early users report 4–7× higher conversion rates and up to 80% lower costs compared to traditional GTM methods.

Because around 80% of B2B buyer interactions now happen via digital channels, there is a strong push for personalised, fast, and efficient engagement. SMBs who adopt these AI-powered strategies now gain a real competitive edge. 

What Is VibeGTM & Agentic AI?

VibeGTM is Landbase’s term for their AI-powered GTM (go-to-market) framework. The idea: simplify the complicated parts of outreach — audience segmentation, message creation, channel selection, follow-ups — using autonomous agents.

“Agentic AI” means AI which doesn’t just give suggestions; it acts — it plans, executes, learns. In VibeGTM’s case, multiple AI “agents” play roles similar to those in a human GTM team: strategist, marketer / copywriter, outreach (SDR-style), data handler, etc. 

The system is powered by a model called GTM-1 Omni, trained on massive data from millions of campaigns, companies, and contacts, so it has wide experience to draw from.

Why Traditional GTM Tactics Are Losing

Buyers are more informed, digital, and self-directed than ever. Many prefer to research on their own, online, before speaking to anyone. Generic, one-size-fits-all outreach gets ignored.

Cold calls, mass email blasts, generic messages — all these yield very low return. They’re labor-intensive, require manual follow-ups, many tools, fragmented data. SMBs don’t always have the resources.

Traditional GTM often scales cost linearly (more people, more tools), but that yields diminishing returns. It’s hard to personalize at scale, and when prospects feel they’re being treated like numbers, engagement drops.

How VibeGTM’s Agentic AI Transforms Outreach

Outreach becomes an adaptive dialogue rather than a static sequence. The AI responds automatically to prospect behaviour: opening emails, clicking links, reply or no-reply, moving them through different channels (email, LinkedIn, follow-ups) accordingly.

Real-time adjustments: If someone engages, the AI deepens contact; if not, it changes tactics. Copy, timing, channel — all are adjusted per prospect.

Because the AI never tires and doesn’t drop follow-ups, the consistency, speed, and precision of outreach improves dramatically.

VibeGTM in Practice (Especially for SMBs)

Example: A cloud consultancy targeting mid-market companies might define their ideal customer profile (roles, industries, size). The system then finds matching companies/contacts, filters by intent signals (e.g. recent relevant news or behaviour), drafts tailored messaging, and sets up a multi-step campaign. All of this happens in minutes. 

Once launched, the AI handles scheduling, follow-ups, cross-channel outreach. The user reviews / tweaks once up front, then lets the agents run the campaign.

Cost savings are significant: early users report that they can get similar or better pipeline outcomes with fewer team members and by offloading much of the manual work.

Personalization at Scale

Personalization is no longer just using someone’s name. It includes referencing their industry pain points, recent news, role-specific value propositions, etc. Agentic AI can do this across thousands of prospects simultaneously. 

Conversational journeys are adaptive: future messages change based on what the prospect did earlier. If they showed interest in something technical, follow-ups might dive deeper; if not, maybe shift tone or offer lighter content. 

Also, channel flexibility: AI might switch from email to LinkedIn InMail or different content formats if a given channel isn’t working.

Measuring Success & Predictiveness

With AI driving GTM, data is more unified. Every touchpoint, every reply, click, or open is tracked. Dashboards show conversion rates, cost per lead, pipeline velocity, etc., in real time.

Lead scoring becomes dynamic. Prospects are prioritized based on behaviour and signals, not static definitions. Hot leads are surfaced earlier. 

Predictive forecasting: the AI, having data from past campaigns, can give reasonably accurate predictions of pipeline, ROI, deals coming, etc. This helps plan and adjust.

Looking Ahead: Why Now, and What the Future Holds

The momentum for intelligent automation is growing. Many companies see generative AI and automation as critical for reinvention.

For SMBs, this means tools like VibeGTM are leveling the playing field: you don’t need huge budgets or large teams to run sophisticated outreach. The agility and cost efficiency translate into real competitive advantage. 

Over time, more GTM tasks will be handed over to AI: optimizing ad spend, social media interactions, even designing landing pages per audience, etc. Humans will increasingly shift toward guiding strategy, relationship building, and high-touch work.

Why You Should Care / Act

If you’re a small or mid-sized B2B business, or even just a team without large budget or headcount, this is an opportunity: adopting agentic AI now can give you outsized gains. 

Delay may cost you: competitors who leverage these tools will get faster, more efficient, more relevant in their outreach; you risk falling behind. 

Starting is easier than you might think: platforms are abstracting away much of the technical complexity. You can begin with simple goals/prompts, then let the system do the heavy lifting.

Summary

Major Takeaways

Who gets to build software in the natural language era?

Natural language interfaces remove the technical barrier to creation. Instead of learning syntax, anyone can describe their idea in plain English and see it built. Everyday users, teachers, realtors, consultants, small business owners, are becoming “citizen developers.” By 2025, 70% of new applications will be built with low-code or no-code tools (up from 25% in 2020), proving this shift is mainstream.

What happens to professional developers when AI can “code”?

Developers shift from writing boilerplate code to acting as curators, coaches, and quality guardians for AI-generated solutions. They orchestrate multiple AI agents, review outputs, ensure security, and align solutions with broader business goals. This preserves trust and creativity, while freeing developers from repetitive tasks to focus on design, innovation, and problem-solving.

Why should businesses care about natural language interfaces?

With NLIs, businesses launch solutions in minutes, not months. Teams eliminate handoff delays, align more closely with their vision, and unlock innovation across the org. Companies adopting AI + natural language platforms see 3–15% revenue lifts and 10–20% higher ROI, while Landbase customers specifically achieve up to 80% lower execution costs and 4–7x higher conversions compared to traditional approaches.

Introduction "In the future, coding might not require you to code at all." This bold prediction is quickly moving from wishful thinking to reality. As a tech entrepreneur and CEO, I've spent my career building platforms to empower creators. I’ve watched the evolution of software development from hand-written code, to drag-and-drop low-code tools, to today’s explosion of no-code platforms driven by natural language. The trajectory is clear: we are shifting from coding with syntax to coding by conversation. In this article, I’ll share why natural language is becoming the new “developer platform” – enabling anyone to build, how it’s changing the role of professional developers, and what this means for businesses. My goal is to paint a visionary yet grounded picture of this shift, rooted in my journey and values of democratization, practical innovation, and trust.

From Coding to Conversing

Not long ago, creating software meant poring over text files and writing every semicolon by hand. Then came visual IDEs and low-code platforms – easier, but you still needed some programming know-how. Now we’re entering an era where you can build software by simply talking to your computer. Platforms like Replit are already proving this: “Today on Replit, anyone can take their ideas and turn them into software — no coding required.”(2) Thanks to AI copilots, you “don’t need to learn coding to be a creator — you just need an idea.”(2) In practice, this means everyday users can describe an app or website they envision, and watch an AI generate the code, fix the bugs, and even deploy it. This isn’t sci-fi; it’s happening now. Replit’s recently launched AI Agent lets you create a working app from a chat conversation, treating software development like a casual dialogue(2).

We see the same trend in content and data domains. Tools like OpenAI’s ChatGPT have hundreds of millions of users now comfortably “programming” by asking questions or giving instructions in plain English. In fact, ChatGPT reached 100 million users within two months of launch (the fastest-growing app in history)(1) – a clear sign that natural language interfaces have gone mainstream. People are using chatbots to draft documents, build spreadsheets, even generate code, all by conversing instead of wrangling syntax.

This shift is redefining who gets to build. It’s not just engineers anymore – it’s anyone with an idea. We built Landbase’s new VibeGTM interface with exactly that in mind. Instead of configuring complex marketing software, a small business owner can simply describe their target audience and goals – e.g. “I want to reach mid-size retail companies in California with a promotion for my SaaS product” – and our AI agents will generate a full, multi-channel sales campaign to match(4). Landbase’s platform essentially lets users launch professional-grade campaigns by describing the outcome they want, rather than by clicking through endless settings. This is the power of moving from coding to conversing: building things by stating your intent in natural language. The barrier to create is coming way down, unleashing creativity for those who never thought they could “code.”

Empowering a New Generation of Builders

When technology democratizes creation, it empowers whole new groups of builders. I’ve seen teachers generating educational games without writing a line of code, marketers crafting interactive web pages by chatting with an AI, and consultants automating their workflows through simple prompts. The use cases span every domain. Replit’s community offers a glimpse: from local realtors in Cleveland to Japanese influencers to product managers at Fortune 500s, people are building incredible things on Replit – proving we no longer need to limit ourselves to being users of software; we can all be creators(2). This includes professions far outside traditional IT. A real estate agent can “ask” an AI to build a lead-tracking dashboard. A non-profit can have an AI spin up a fundraising campaign page. A teacher can get a custom quiz app generated for their class. All using natural language instead of waiting for a developer or learning programming themselves.

The data backs up this mainstream adoption. Generative AI has rapidly infiltrated daily workflows – just consider ChatGPT’s 100M+ user explosion as mentioned earlier. And industry analysts predict that by 2025, 70% of new applications developed by enterprises will use low-code or no-code technologies (up from just 25% in 2020)(6). In other words, building software with little to no traditional coding is becoming the norm, not the exception. This is empowering a new generation of “citizen developers.” They are teachers, marketers, small business owners, analysts – people who have deep knowledge of their domain’s needs, now empowered to create solutions for those needs without a technical intermediary.

With an agentic AI platform, “Landbase is uniquely suited for non-technical B2B users — like realtors, consultants, MSPs — who need growth but don’t have in-house marketing teams. Our agents don’t just recommend; they act.”(5) In practice, that means a consultant with a new service offering can log into Landbase and, in plain language, outline their ideal customer profile and campaign idea. The AI will handle the heavy lifting – from building prospect lists to writing personalized outreach – tasks that previously only a team of specialists could do. By turning ideas into action without a single line of code or a single hire, these tools level the playing field. This is practical innovation: technology making things that used to be hard (or expensive) remarkably easy. Most importantly, it’s innovation in service of people– giving everyone the capability to build and grow. We’re witnessing a true democratization of software creation.

Developers as Curators and Coaches

So where does this leave professional developers? In a world where anyone can build with natural language, are engineers still needed? Absolutely. In fact, I’d argue developers are more important than ever – but their role is evolving. Rather than spending their days wrestling with syntax and boilerplate, developers are becoming curators, coaches, and quality guardians for AI-generated solutions. Their deep expertise is critical to guide these powerful new tools in the right direction.

Think of an AI as an eager junior engineer who can generate solutions at lightning speed. You still need a senior engineer to review the work, catch the edge cases, and ensure it fits the broader strategy. Developers now act like coaches for AI teammates: setting high-level objectives, providing feedback, and making judgment calls. They’re the ones who ensure the product meets requirements, is secure, and performs well – things an AI might not fully grasp without human oversight. As an IBM AI advocate recently put it, an AI can crank out code, “but true creativity, goal alignment and out-of-the-box thinking remain uniquely human”, so human input and oversight is essential(3). I often remind my team of this balancing act. Yes, let the AI do the grunt work, but a human expert should always be in the loop to validate and refine.

In practice, this means the developer’s job feels less like being a code monkey and more like being a project lead or architect. You might spend more time crafting the right prompt or conversation with an AI, and less time writing low-level functions from scratch. You might orchestrate multiple AI agents (one generating code, another generating test cases, etc.) and make sure they all produce a coherent outcome. It’s a shift from keyboard-heavy coding to a higher-level form of development. But it’s a shift that many developers welcome – it automates the repetitive work and frees them to focus on design, innovation, and solving novel problems. We still need their ingenuity and problem-solving more than ever; we’re just letting the machines handle the tedious parts.

Crucially, this approach preserves trust in the software we build. We often say at Landbase, “relationships and trust still matter… We’re creating technology that enhances the human element rather than replacing it.”(4) I view developers as the ambassadors of that human element in the creation process. They ensure the solutions delivered by AI are ones we (and our customers) can trust – well-crafted, ethically sound, and aligned with real user needs. In short, developers remain vital, but now as mentors and editors working alongside AI “assistants.” It’s a collaboration between human and machine, each doing what they do best.

The Business Case for Natural Language Interfaces (NLI)

Why does all this matter for businesses and organizations? In a word: leverage. Embracing natural language interfaces – letting people build software or execute tasks by describing what they want – unlocks huge advantages:

Faster time to market: Projects that once took weeks or months can now be turned around in days or hours. There’s no lengthy spec hand-off or backlog waiting for scarce dev resources. For example, Landbase’s AI-driven campaigns go live in minutes, not months(5). When a new opportunity or idea arises, teams can capitalize immediately by “launching” an AI to build the solution. This agility is game-changing in fast-moving markets.

Better alignment with needs: How often have business requirements been “lost in translation” between users and developers? With NLIs, the business expert is in the driver’s seat, using their own words to create the solution. The result tends to align much more closely with what they envisioned, because the creation process starts and ends with their intent. By eliminating the intermediary steps (and miscommunications) between a problem owner and the software implementation, organizations get outcomes that fit their needs with far less iteration. Every stakeholder – from marketing to HR to sales – can directly shape the tools they use, ensuring those tools truly serve their purpose.

Expanded innovation capacity: Perhaps the most profound benefit is how much creativity this unlocks across an organization. When every team member has an AI “developer” at their side, the company’s innovation potential explodes. A person with a great idea no longer has to ignore it due to lack of coding skills or IT support – they can prototype it themselves. This means more experiments, more optimizations, more solving of local problems. A recent McKinsey study found that businesses using AI in their sales and marketing achieved 3–15% higher revenues and 10–20% greater ROI on average(4). Why? Because AI and natural language tools enable them to iterate faster and tackle opportunities that would have been bottlenecked before. In essence, NL interfaces dramatically expand the pool of builders and the volume of innovation a company can generate.

We’ve built Landbase around these very principles. Our platform’s agentic AI system acts like a virtual go-to-market team member for each client. It crunches data, writes copy, and executes outreach at machine speed – but always in service of the goals the human user sets. The outcome: our customers have seen up to 80% lower execution costs and 4–7× higher conversion rates compared to traditional approaches(5). In other words, natural language + AI isn’t just a cool tech demo; it’s driving real ROI. It enables “minutes, not months” execution and a level of personalization and scale that manual efforts can’t match(4). I truly believe we’re headed toward a future where every salesperson, every analyst, every manager has their own AI builder on demand – a future where telling your computer what you want is all it takes to make it happen.

Of course, we must implement these tools thoughtfully. Governance, security, and training are key so that NLIs are used responsibly. But with the right guardrails (for example, our VibeGTM interface lets users guide the AI within safe bounds for compliance(5)), the benefits far outweigh the risks. The organizations that leverage natural language platforms now will outpace those that don’t – delivering solutions faster, responding to customers quicker, and fostering a culture where everyone can innovate.

Conclusion

The writing is on the wall (or perhaps in the prompt): natural language interfaces are transforming how we build and who gets to do it. This is a shift I’m personally passionate about, because it aligns with something I’ve long believed – technology should be an equalizer, not a barrier. When I started Landbase, it was with the mission of making advanced go-to-market capabilities accessible to businesses that lack big teams or budgets. More broadly, I see natural language as the interface that can make technology accessible to all, much like the graphical user interface did a generation ago.

So I invite you to experience this shift for yourself. Give natural language building a try – you might be surprised how far you can get with just your words. In fact, we’ve made it easy to experiment: try Landbase’s free VibeGTM interface (our AI go-to-market platform) by building something small. For example, describe a simple prospecting sequence, a landing page idea, or a campaign brief you’d like to execute – using only natural language – and see the platform generate it. It’s free to get started (just a URL and an email) and you’ll get a feel for what working alongside an AI “co-pilot” is like. My hunch is that once you see a campaign spin up from a few chat prompts, you’ll understand why I’m so excited about this future.

Major Takeaways

Why not use general AI models for go-to-market?

General models lack business context. GTM needs domain-specific AI trained on real sales and marketing data to drive outcomes like pipeline, conversions, and product-market fit.

What makes Landbase’s GTM-1 Omni unique?

It’s trained on 40M+ B2B campaigns, uses agentic AI to automate GTM workflows, and continuously learns from results, delivering 4–7x better performance than traditional methods.

How do teams use GTM Superintelligence today?

With VibeGTM, teams articulate their GTM objectives in natural language, and AI agents autonomously handle targeting, content creation, and campaign execution. This accelerates speed to market, reduces costs, and enhances performance, all while preserving human oversight.

Superintelligence is in the spotlight. Major AI labs—from OpenAI and Anthropic to Meta’s newly launched Superintelligence Labs—are racing to build general-purpose models that exceed human capabilities across reasoning, coding, and cognition. But while they aim to conquer science, math, and language, there’s one critical business area being overlooked: go-to-market.

At Landbase, we’re pioneering a different kind of superintelligence—one that’s domain-specific and built entirely around business growth. We call it GTM Superintelligence.

Why GTM Needs Its Own Superintelligence

General-purpose models are trained on internet-scale data, but they lack the context and performance feedback loops necessary to drive meaningful GTM outcomes. You can’t prompt your way to product-market fit, and you can’t brute force your way to pipeline without understanding brand trust, ICP nuance, or message-market resonance. This fundamental limitation is why domain-specific AI models are increasingly outperforming general models in specialized applications.

That’s why we built GTM-1 Omni—the world’s first AI model trained specifically on GTM data and real performance outcomes. Pre-trained on over 40 million B2B campaigns and sales interactions, 24 million companies, 220 million contacts, and 10 million signals and events, GTM-1 Omni is reinforced with feedback from millions of campaigns and outcomes across buyers, sellers, and messaging contexts. It’s purpose-built to not just assist—but outperform.

We’ve already seen it deliver 4-7x better performance than traditional outbound approaches when paired with high-quality input data and aligned brand strategy. It’s not just machine intelligence—it’s superintelligent go-to-market, a system that learns, improves, and scales beyond human limits.

The Superintelligence Context: From General to Domain-Specific

The race for artificial superintelligence (ASI) has intensified dramatically in 2025. Mark Zuckerberg announced Meta’s massive investment of hundreds of billions of dollars to build several multi-gigawatt AI data centers for superintelligence1. Meta has formed Superintelligence Labs, poaching top AI talent with offers reportedly reaching $200 million over four years—100x that of their peers.

OpenAI CEO Sam Altman has declared that “humanity has crossed into the era of artificial superintelligence” and that his company is “beginning to turn its attention to superintelligence in the true sense of the word”. He describes superintelligence as systems that could “massively accelerate scientific discovery and innovation well beyond what we are capable of doing on our own”.

However, this rush toward general superintelligence overlooks a critical insight: specialized AI consistently outperforms general-purpose models in domain-specific applications. While tech giants chase the holy grail of AGI, the real business value lies in vertical AI solutions that understand industry-specific contexts, workflows, and success metrics.

Gartner has validated this shift toward domain specialization, recognizing that agentic AI with domain-specific models represents the next frontier of AI innovation. In their recent emerging technology reports, Gartner highlights that AI applications with domain-specific models, like Landbase’s GTM-1 Omni for go-to-market, will be able to outperform performance metrics within specialized use cases. The research giant projects that by 2030, 90% of GenAI-enabled solutions will utilize domain-specific language models (DSLMs), up from just 5% in 2025.

What Makes GTM Superintelligence Different

Unlike the broad, internet-trained models being developed by major AI labs, GTM Superintelligence takes a fundamentally different approach:

Domain-Specific Training: GTM-1 Omni is trained exclusively on B2B sales and marketing data, giving it deep contextual understanding of what drives business outcomes. This specialized approach mirrors successful vertical AI implementations across industries, where domain-specific models consistently deliver superior accuracy and performance.

Multi-Agent Architecture: Rather than relying on a single monolithic model, GTM-1 Omni deploys specialized AI agents that work together—Strategy agents that analyze market signals, SDR agents that craft personalized messaging, and RevOps agents that optimize campaign performance. This orchestrated approach represents what Landbase calls “agentic AI”—systems that can independently plan and execute complex workflows with minimal human intervention.

Continuous Learning Loop: Unlike general models that are trained once and deployed, GTM-1 Omni continuously learns from campaign outcomes, feeding success and failure data back into its models to improve targeting and messaging over time. This creates a feedback loop that makes the system increasingly effective for each user and domain.

Real-World Performance Data: The model isn’t just trained on text—it learns from the outcomes of over 40 million actual B2B campaigns, understanding not just what messages to send, but which approaches drive meetings, deals, and revenue.

The Applied AI Lab: Where GTM Superintelligence is Born

In March 2025, Landbase launched the first Applied AI Lab specifically dedicated to advancing GTM automation. Led by Chief Data Scientist Hua Gao and staffed with world-class AI and ML experts from Stanford, Meta, and NASA, the lab focuses on three critical areas:

Planning and Decisioning Models: AI that orchestrates workflows across multiple tools, utilizing private knowledge to determine optimal campaign strategies.

Generator Models: Systems that create hyper-personalized messaging tailored for omni-channel campaigns, understanding both industry context and individual prospect needs.

Prediction and Reward Models: AI that scores content and predicts prospect perception, improving conversion rates while reducing irrelevant outreach.

This lab approach represents a fundamental shift from the broad AI research happening at major tech companies to laser-focused, application-specific intelligence development.

VibeGTM: Making Superintelligence Accessible

Just as vibe coding democratized software development by allowing developers to describe what they want in natural language and let AI handle the implementation , VibeGTM brings the same “describe what you want, AI does the work” philosophy to go-to-market execution.

With VibeGTM, powered by GTM-1 Omni, marketing and sales teams can describe their target audience and goals in plain English, and the platform’s AI agents autonomously plan, generate, and execute multi-channel campaigns to reach those targets. Build prospect lists, generate tailored content, and launch campaigns in under 20 minutes—not months.

This represents a paradigm shift from traditional automation, which executes predefined workflows, to true agentic AI that actively makes decisions, generates content, and optimizes strategies based on results. It doesn’t just follow instructions—it learns and adapts like an autonomous team member.

The Catch: Human Agency Still Matters

While the world rushes to build AI agents that act autonomously, we believe the future of GTM isn’t just autonomous—it’s agentic. The distinction is critical:

AI agents can take action

You—the human—retain agency over what’s deployed

Brand trust, timing, and taste still require human judgment. Superintelligence doesn’t mean removing the human—it means giving you a machine-powered advantage that scales your judgment, augments your performance, and helps you reclaim your day.

What GTM Superintelligence Looks Like in Practice

The results speak for themselves. Companies using Landbase’s GTM superintelligence experience:

4-7x conversion uplift compared to manual, human-built campaigns

Campaign launch times reduced from 14 days to minutes

80% cost reduction compared to traditional approaches

This isn’t theoretical—it’s happening now. AI that recommends campaigns based on past wins, messaging that adapts to audience behavior in real-time, execution that orchestrates omni-channel outreach autonomously, and results that are measurable, repeatable, and improving every day.

The Market Recognizes the Shift

The investment community is taking notice. In June 2025, Landbase raised $30 million in Series A funding co-led by Sound Ventures (Ashton Kutcher’s AI-focused fund) and Picus Capital. The funding round reflects growing recognition that the future belongs to vertical AI solutions rather than horizontal general-purpose models.

As Guy Oseary, Co-Founder of Sound Ventures, noted: “Landbase makes one of the hardest parts of building a business feel simple. They’re not just solving outbound—they’re building the foundational platform for how modern companies grow”.

The broader market is aligning with this thesis. CB Insights’ 2025 AI 100 report highlighted that “vertical AI is on the rise, with this year’s vertical winners surpassing the other category winners to capture over $1B in combined funding in 2025 YTD”. Industry experts predict that vertical AI markets could be “five to ten times larger through the introduction of artificial intelligence”.

Superintelligence is Here, Domain-First

While OpenAI, Meta, and Google chase the dream of general superintelligence that may still be years away, GTM Superintelligence is already delivering transformative results today. It’s not about waiting for AGI—it’s about building domain-specific intelligence that exceeds human performance in the areas that matter most for business growth.

Superintelligence isn’t just a research goal—it’s a practical opportunity when built domain-first, reinforced with real data, and grounded in human agency. At Landbase, we’re not trying to replace humans. We’re helping them outperform—with GTM superintelligence as their competitive edge.

This represents more than just another point solution or CRM plugin. It’s a full-stack GTM system where your software doesn’t just support your workflow—it becomes the driver of it. In an era where general AI promises everything to everyone, GTM Superintelligence delivers something far more valuable: AI that actually understands your business and makes you money.