Airtop is a conversational web agent builder.
It lets you create AI agents that can log in, browse any website, extract data, and complete tasks - just by describing what you want in plain English. No code, no workflows, no APIs. Just words.
This is the 4th launch from Airtop. View more
Mark by Airtop
Launched this week
Give Mark your website and it researches your business, creates a personalized GTM plan, and builds web agents that automate lead gen, enrichment, outbound, SEO, and Google Ads campaigns. Using Mark is like vibe coding, but for sales and marketing campaigns. Built on Airtop's Agent Builder platform, Mark compiles every automation into deterministic code, so its agents run reliably and 10-100X cheaper than LLM-per-step agents. Real marketing automations, built just by typing.
Free Options
Launch Team / Built With
Airtop
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!
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.
Airtop
@dipankar_sarkar Great point, building the agents is exactly where Mark shines, it uses the Airtop platform so agents are reliable and cheap to run. Our secret sauce: we compile intent into code that runs like software, using LLMs only when needed. 1% of the cost, 10x faster, and the same result every time.
Airtop
@dipankar_sarkar Great question! You can ask Mark to check the results of the agents at regular intervals, and then modify the agents if needed. You can also chat with Mark at any time to review results and optimize.
"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.
Airtop
@shubham4real Exactly right, that long tail of internal tools and dashboards with no API is the whole reason browser automation exists. It's where the real repetitive work lives and where most integrations were never going to happen.
On bot detection: you're pointing at the hardest part, and it's most of the engineering. A big chunk of Airtop is the execution layer built for exactly this: a managed cloud browser fleet with real browser fingerprints, residential IPs, and handling for auth, 2FA, and CAPTCHA. The goal isn't to "look human" as a trick, it's to actually run in a real browser environment so the traffic is genuine rather than a headless signature.
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?
Airtop
@dipankar_sarkar Great question. The agent builder decides 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?
Airtop
Hello @dipankar_sarkar , on the initial prompt of the agent or during agent building time, you can provide the agent builder with information on how to steer the agent code. If you provide enough information for certain steps to be validated via an LLM, the agent can do so.
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?
Airtop
@dipankar_sarkar Awesome question. You don't need to enumerate anything in advance, just describe the expected behavior. The idea is not to eliminate all usage of the LLM but to code the parts that are consistent and definitely do not require an LLM. Ping with your use case and I will help you to get the agent running.
The compile-agents-into-executable-code approach instead of keeping an LLM in the loop is what makes the reliability and cost claim believable. My setup question is about the accounts Mark logs into: when he posts to social or pulls LinkedIn data, where do those sessions/credentials actually live, on Airtop's hosted browsers server-side, or something scoped per user? And when a site changes its flow and a compiled agent breaks, does Mark detect the failure and auto-recompile, or do I have to trigger a rebuild?
Airtop
@noctis06 We do provide a Vault system as part of Airtop, where you can securely store your credentials to be used on the sites.
Each agent also stores the browser profiles, so that subsequent runs do not require to login each time.
In particular, the Vault and browser profiles are encrypted for your or your team's use only.
If the agent breaks, for any reason, it will notify you, and you will be able to review the failure and adapt the agent accordingly.
Now, depending on the failure, we do provide a self-healing mechanism for some of the actions the agent does, but websites vary in quality and potential changes, so there will be scenarios where you will need to adapt the agent (trigger a rebuild) to correct the failure.