Why We Are Building Lapis

Every marketing team runs the same way. Ahrefs or Semrush for SEO. Google Analytics or PostHog for traffic. Meta Ads Manager, LinkedIn Campaign Manager and Google Ads for paid. Klaviyo or Mailchimp for email. Canva or Figma for design. Google Docs or Notion for managing it all.

Each tool is good at one thing. But none of them talk to each other.

Your writing tool doesn't know what keywords you're going after. Your ad platform has no idea what's working on the organic side. Your analytics are completely separate from your content calendar. Your design tool doesn't know your campaign goals. You're constantly copying data between tools, switching between 10 different tabs, recreating context, and explaining the same thing to different systems.

When it's time to decide what to write next or where to spend ad budget, you're left guessing. The data that would help make the answer obvious is sitting in another tool you'd have to log into, export from, and manually piece together before going back to the original platform and performing the action.

This isn't new. It's been the story of marketing tech for decades.

There's been very few venture scale marketing software companies. Think about that for a second. Marketing is a trillion dollar industry and every company does marketing and yet the software that enables this has never produced outcomes anywhere close to what we've seen in Sales (Salesforce), HR (Workday), Finance (Stripe), or Dev tools (GitHub, Datadog).

Why? Because the companies that build marketing tools made more money by staying separate.

One company built an email tool. Another built an SEO tool. Another built a social media scheduler. Another built an ads manager and analytics. And the list goes on. Each raised venture capital and fought for their slice of marketing budget but they had no reason to share data with each other (sometimes even with you). In fact, they had every reason not to. If your data is trapped in their tool, you're less likely to leave. If switching is painful, you'll keep paying. So data was kept locked up on purpose.

The result is what you see today with a dozen different marketing tools, each with its own login, data model, and way of thinking about the customer. The whole stack is held together by CSV exports, Zapier automations, and someone on the team who remembers how all the pieces connect.

Marketing tech has been plagued by point solutions that serve immediate needs and after a couple of years, demand would dries up and marketers were left with a solution that refused to innovate (Outreach and Salesloft) or get acquired by Adobe or Twilio.

Until now. At Lapis, we are building the first vertically integrated marketing software company.

AI agents change the way software is built.

For the first time in the history of software, we can build systems that actually do the work. Previous generations of marketing tools are essentially fancy dashboards with some basic automations. They could show you data and they could send an email on a schedule but they couldn't think, create, or make decisions. AI agents can.

As an example, think about design. Traditionally, creating ad creative required a human designer who understood brand guidelines, campaign objectives and requirements, and visual best practices. They'd spend hours in Figma or Photoshop iterating on a concept, resizing for different placements, and making sure everything was on-brand. It was a huge bottleneck and most startups either hired agencies (expensive) or had founders fumbling around in Canva (painful).

An AI agent can do this in seconds. Not by generating generic AI slop, but by actually understanding your brand and your goals. An Agent can create dozens of variations, test them faster than a human can, learn what works, and iterate endlessly. And it gets better with every campaign.

Another example is paid media marketing. Today, running ads is a full time job and you need someone who understands audience targeting, creative testing, budget allocation, and platform-specific quirks across the different ad platforms. That person spends most of their time on operational work like uploading creatives, adjusting bids, pulling reports, and copying data between systems.

An AI agent can handle all of this busywork on its own. You give an agent your brand assets and it will take care of the rest. It creates different versions of your ads, figures out who should see them based on its research of your ideal customer, and launches campaigns across all ad platforms. It can also monitor campaigns and optimize them in real time, fully autonomously.

This is why building everything in one place finally becomes possible. The reason no one could build a tool that was great across multiple areas was because each area required deep human expertise. You needed SEO experts to build SEO tools. Designers to build design tools. Media buyers to build ads tools. Now, AI agents can have that expertise and a single platform can have agents that do all the busywork. And the agents can learn and execute at a level that previously required human specialists, and increasingly are starting to outperform them.

Context powers the system.

Today, your ad platform knows your ad performance. Your analytics tool knows your website traffic. Your CRM knows your customers. Your content tool knows what you've written. But none of them know what the others know. They're blind to each other.

When you connect all these systems to one platform, something interesting happens. You start to see patterns that were invisible before. Blog posts about a certain topic drive more signups than others. Customers from LinkedIn ads have higher lifetime value than those from Google. An AI agent with full context sees the whole picture and can tell you which audiences to target, what messaging to use, and when to run campaigns.

This context is very powerful. Every integration and every campaign teaches the system something new. A successful ad reveals what messaging works, and a high-converting landing page shows what your customers care about. These insights feed back into the system and make every future decision smarter. The longer you use the system, the more the sytem understands you. Over time, it understands your business better than any combination of disconnected tools ever could.

But for a platform to learn like this, everything needs to live in one place.

Marketing needs a system of record.

A system of record is a single source of truth for an entire function. Sales has Salesforce. Every deal, every contact, every conversation lives there. HR has Workday. Finance has NetSuite. Engineering has GitHub. These tools own their function and everything else plugs into them. A sales tool that doesn't integrate with Salesforce just wouldn't be a successful tool.

Marketing has never had this.

This is why marketing feels chaotic. Every report requires pulling data from multiple tools, hoping the numbers match, manually piecing together a picture that should be obvious.

Lapis is built to be that system of record for marketing. The one place where everything lives - your brand, your campaigns, your analytics, your content, your competitive intelligence all exist in one connected system. Not as a dashboard that displays data from other tools. As the actual source of truth that other tools plug into.

That's the first problem we're solving. The second is one that nobody understands just yet.

Search is changing. Nobody is really building for it.

Search is changing. Not a little bit. Completely. People are asking ChatGPT instead of Googling. They use Perplexity for research. Claude for thinking through problems. Gemini for recommendations. This isn't going away. It's as big as when everything moved to mobile.

AI SEO or GEO companies aren't solving this. They're using AI to pump out hundreds and thousands of blog posts and calling it a day. That's not AI SEO, it's just a CMS powered by an LLM.

The real opportunity isn't content, its ads. The way people discover products and information will fundamentally change in the next 2-3 years. This is the next frontier of paid acquisition, and it's arriving faster than most marketers realize.

But AI search ads won't exist in a vacuum. For the foreseeable future, most traffic will still come from Google. Running ads on ChatGPT without understanding what's working on Google is like running Meta ads without knowing your website conversion stats. You need context from everywhere to make smart decisions.

This is where the system of record matters. When Lapis knows your Google Ads performance, your Meta performance, your website analytics, and your customer data, it can help you run AI search ads from day 1.

Companies also have no idea what AI says about them. When someone asks ChatGPT for a product recommendation in their category, they don't know if they show up, if their competitor shows up, or if neither does. This didn't exist two years ago, and it's growing fast.

The old SEO world was built on keywords. But people don't type keywords into ChatGPT, they describe their problems and have natural language conversations. These conversations are messy, varied, and hard to model. Lapis models prompts through extensive crawling and a reinforcement learning environment that learns in real time. We are building the tools to track how AI models perceive your brand across the kinds of prompts real people actually use, and to help you shape what they say.

We're building Lapis for the growth team of the future.

Most of a growth marketer's time is spent on busywork. Pulling numbers from one tool, copying them to a spreadsheet, exporting reports, uploading creatives to another platform. The actual strategic work gets squeezed into whatever time is left.

What if all of that just happened automatically? Reports build themselves. Campaigns optimize themselves. Marketers can spend their time on strategy, positioning, and creative direction, the work that actually requires human judgement.

That's the bet we're making.

One platform where AI analytics, ad creation, content, design, and competitive intel all work together. Where AI agents handle the execution. Where what you learn in one place makes everything else better.

Marketing has been fragmented for decades. Better point solutions won't fix this, they're what caused this problem in the first place. We will fix this.

We're not building another tool to add to your stack. We're building the thing that replaces the stack.

Growing shouldn't mean more complexity.