I used AI to build an entire marketing business from scratch — site, 100 pages of content, products, automation. Week 2 update.
I kept collecting AI marketing prompts. Hundreds of them. Saved in folders, bookmarked, screenshotted. And my marketing still didn't work.
The prompts weren't the problem. The problem was I had no system. A prompt for writing emails doesn't help if you don't know which emails to write, when to send them, or how they connect to your content and ads.
So I started building systems instead. Not individual prompts — complete workflows. Content system. Email system. Ad system. SEO system. Brand system. Each one covers what to build, which AI tools to use, what order to do it in, and how it connects to everything else.
I tested them on real businesses. Refined what worked. Cut what didn't. Then I built Syxo to teach other people how to run them.
Right now it's a free quiz (syxoai.com) that scores your marketing across those 5 systems, finds your weakest one, and tells you exactly which AI workflow to build first. Plus a blog with 30+ posts walking through each system step by step.
Revenue: $0 so far. Launched 2 weeks ago. The site's getting 30+ impressions/day on Google and growing. Digital products (The AI Marketing Stack — $97/$149, plus individual skill packs at $29-$39) are built and listed but no sales yet.
Building in public. Happy to share what's working and what isn't.
Good insight. Many founders collect prompts but lack a repeatable marketing system.
Since Syxo runs quizzes and evaluates marketing data, a few security and data handling points will matter early:
• If users input business details, traffic numbers, or ad data, where is that information stored and how long is it retained?
• If AI tools generate the workflow recommendations, are user inputs sent to external LLM providers, and is there zero retention?
• Are quiz submissions rate limited to prevent scraping of the scoring logic or automated abuse?
Another angle is integrity. If the quiz becomes popular, people will want to know:
• How scores are calculated
• Whether recommendations are deterministic or AI generated
• Whether any marketing data is reused for training or analytics
Transparency will help here. A short page explaining data flow, retention, and AI usage will build trust with founders entering real business information.
As a security team building Nautillo Pro, we often see early SaaS tools expose scoring APIs or form endpoints that later become attack surfaces.
Thanks - these are good points to have on the radar.
Quick context on where Syxo is right now: the quiz scoring is client-side JavaScript — runs entirely in the browser, nothing stored. If someone opts in with their email at the end, that goes to an email platform via a Cloudflare Worker proxy. That's the only data that leaves the browser.
No database, no server-side processing, no business data collected beyond name + email + quiz score. Standard email marketing setup.
Most of what you've flagged would apply at a later stage if I move to server-side processing or handle real business data. The transparency page is a solid idea.
Curious, do you see many early-stage tools run into these issues before they have traction, or does it tend to become a problem once there's actual usage volume? And what have you found the best routes to getting users is for you?
Makes sense. If scoring runs entirely client side and only name, email, and score leave the browser, the exposure surface is much smaller. That is a solid early architecture for a quiz product.
Client side scoring avoids a lot of common early issues like API scraping or score manipulation endpoints. The main things to watch later are when logic moves server side or when more business data starts flowing through the platform.
In practice we see two phases:
1. Early stage: Risk usually comes from simple form endpoints, weak rate limits, or email collection flows that become spam targets.
2. Growth stage: Problems appear once platforms introduce APIs, dashboards, integrations, or server side scoring logic.
Your current setup is actually a good pattern for early traction. Minimal data collection and simple architecture reduce risk significantly.
On the user acquisition side, founders usually gain traction fastest by publishing real examples of outcomes. Showing how the score changes for a real company, or walking through one marketing system in public, tends to convert better than describing the framework.
We often see early SaaS tools expose small endpoints that later become attack surfaces once traffic grows. If you ever want to test the platform from an external attacker perspective, Nautillo Pro has a free version available.