Mark by Airtop - Vibe automation for solo marketers

We built Mark because the existing marketing options were bad.

When we launched our own product and tried to go to market, everything about it was complicated. Marketing is fractured across SEO, content, outbound, social, and paid ads, and the tooling is so fragmented you practically need a go-to-market engineer just to wire a dozen subscriptions together. The alternative was an agency: $10K+ a month, and a lot of weekly meetings that eat up time.

So we built the marketing employee we wished we could hire. We’ve used Mark to automate large parts of Airtop's own marketing, including our Google Ads. Now we want to give him to everybody.

Most marketing tools in this space fail in one of three ways:

❌ Chatbots that ideate, then leave you to do all the actual work.

❌ Automation platforms that hand you building blocks, a dozen integrations to wire up, and zero marketing expertise.

❌ LLM-based agents that are unreliable and cost a fortune to run at scale.

Meet Mark 🚀

🔷 He researches your company deeply, crafts a personalized go-to-market plan, then builds web agents to execute it: SEO, lead generation, social, even paid ads.

🔷 Batteries included. Waterfall contact databases, email verification, enrichment, LinkedIn intelligence, are built in, so you can cancel your other subscriptions. Mark also has access to his own web browser: he can log into websites, fill out forms, and post on social media.

🔷 Built with training from real experts. Talking with Mark is like talking with a highly knowledgeable marketing consultant.

🔷 10x faster and 10-100x cheaper to run than LLM-based solutions. Mark uses intelligence to build reliable agents, then compiles them into executable code, like traditional software.

Who is this for?

Founders, lean teams, and solo marketers who don’t have the time, budget, or patience for a traditional marketing agency or complicated marketing automation tools.

🔗 Get started today

Try Mark free at airtop.ai/mark. Use the code MARKPH26 for one free month of Airtop’s Starter plan, which includes a 14 day trial of Mark.

Have a chat with Mark about your business, and see how he can take the pain out of going to market.

We are here all day. Ask us anything!

This looks super useful for automation workflows. How reliable is it when you scale multiple browser tasks at the same time?

The GTM plan is the part that demos well, but generating a plan is the easy 20%. The thing I'd want to know building this kind of agent: does Mark close the loop on what actually converted, weeks later and through noisy attribution, and revise? Or is it generate-once? A plan a solo marketer can't measure against gets abandoned by week two.

"no APIs, just words" is a strong claim for browser agents specifically because most sites don't have an API to begin with. that's the actual gap browser automation fills, the long tail of internal tools and dashboards that were never built with integration in mind. how does it handle sites with heavy bot detection though? logging in and browsing like a human is exactly the pattern most anti-bot systems are tuned to catch.

Automating outbound and Google Ads straight from a website scrape is the part I'd watch closely - if Mark's GTM read of the business is slightly off, you're not just wasting time, you're burning ad spend and sending outbound copy that misrepresents what the company does. Is there a review step before Mark actually starts spending or emailing on your behalf, or does it go straight from plan to live campaign?

Compiling intent into code is the right instinct, we landed in the same place: re-deriving the same plan with an LLM on every run is what kills reliability. Where it got hard for us was the genuinely non-deterministic steps, like 'is this reply a real lead or a bounce', you can't compile the judgment out. So the interesting line is compiled-vs-LLM: does Mark let the author pin certain steps as always-LLM, or does the compiler decide that itself?

Fair, as long as the builder calls that right. The step I'd want to override is the judgment one, like is-this-reply-a-real-lead. Your monitoring layer confirms a step completed, but a classifier can complete perfectly cleanly and still be wrong, and compiling it hides that failure mode. Can the author pin a specific step to stay LLM-evaluated on every run instead of letting it get compiled down to fixed code?

Got it, so it's steer-at-build-time. The thing I keep bumping into: declaring upfront which steps stay LLM-validated assumes I already know where the judgment gets hard, but the is-this-a-real-lead call goes wrong on the ambiguous 5% I can't enumerate in advance. What I'd really want is a step that re-checks itself at runtime when its own confidence drops, not a static flag set at build. Is that on the roadmap, or is build-time steering the model you're committed to?

I used the early version of Mark to do some seriously cool stuff. My personal use case is building AI ops to make a small team of marketers' lives easier, and we're big fans.

I may be biased because I've been burned by agencies before, having to wait 6-12 months for results, not doing a good job with product stuff, and having little impact on priority SEO—focusing instead on improving 50-100-rank keywords.

Not going to reveal too many secrets but we were able to turn that around with Mark. Most of our automatable processes are assisted by Mark now, and we've brought in-house what would traditionally be considered agency work, seeing a significant increase in our tracked metrics while spending much less.

Tried it for scraping some old vendor portals and it actually handled the login flow without me babysitting it, which honestly caught me off guard. Curious to see how it holds up on trickier captchas.