How I built an AI-assisted content system for Stavia Models

Once the content strategy is set, the next article starts from one topic in the Stavia content plan.

I ask AI to turn that topic into a working brief: what founder decision the article should explain, which part of Stavia product logic should be used, what screenshots or examples may help, and what the final article should become after publication.

For the benchmark article, the topic was SaaS pricing before launch. The brief had to connect pricing inputs with billing mix, churn, cash timing, recognized revenue and unit economics.

I review the first AI answer and correct the direction if needed. This is where I add the expert layer: which examples are more realistic, which angle is stronger, which parts should be removed, and where the article needs a clearer connection to the product logic.

After the article brief is clear, I do not ask AI to write from general knowledge. I ask Cursor to inspect the exact Stavia Models product area behind the topic.

For the benchmark SaaS pricing article, the source was the Pricing input tab. Cursor analyzed the pricing inputs and the dependent calculations across the forecast, P&L, cash flow and unit economics.

This gives me a writer-friendly but technically accurate source summary: what users enter, what each input means, how it affects the model, and which outputs change.

That summary becomes the bridge between the product and the article. It gives ChatGPT real logic to work with, and it gives me something concrete to review before drafting.

Once I have the article brief and the Cursor extraction, I can move to the first full draft.

The brief gives the article direction and structure. The extraction gives the product logic: how the relevant part of Stavia Models works, what inputs matter, how they affect the forecast, and what the reader should understand.

At this stage, I ask ChatGPT to turn those materials into a readable article draft. I also add my own comments: examples I want to include, places where the explanation should be clearer, benchmarks that may help, and phrases that do not sound like my style.

The goal of this stage is not a final article. It is a strong working draft that already follows the agreed angle, stays grounded in product logic, and shows where screenshots, visual explainers or interactive blocks should appear.

After that, I review the draft for logic, tone, examples and missing context before sending it to Cursor for page implementation.

After the draft is ready, I do not send a raw text document to Cursor. I first ask ChatGPT to turn the approved draft into a clear implementation task for my coding assistant.

That task explains what page to create, which article structure to follow, where screenshots should be placed, what visual blocks may be needed, how the CTA should work, and what needs to be checked after implementation.

Then I move to Cursor. In my workflow, Cursor creates the code-first article page inside the Stavia Models website. It can create the page structure, connect metadata, add layout blocks, prepare visual sections and place screenshots or placeholders.

At this stage I still give very specific comments. I correct the text, adjust the order of blocks, add notes about the visual rhythm and usually provide real screenshots manually. Cursor can help create visual components, but screenshots from the product are often faster and cleaner to capture by hand and then add as assets.

The result of this stage is a live article page draft that can be reviewed in the browser before publication.

For Stavia Models articles, I use interactive blocks when a financial idea is easier to understand by changing inputs than by reading another paragraph.

Financial modeling has a lot of formulas, links and assumptions. My goal is to explain that logic simply, so founders can see how one decision affects the model.

That is why some articles include small interactive modules: pricing assumptions, acquisition channels, AI/API usage, cost structure, hiring timing or runway scenarios.

These blocks are worth the extra effort because they make the article more intuitive. The reader can change a few assumptions and immediately see what moves: revenue, cash timing, CAC, margin, burn or runway.

AI coding assistance makes this practical. I usually know the simplified logic I want to show because it comes from the product. AI can suggest interface ideas, but the important part is giving it a clear model: which inputs the reader should change, which output should react, and what the block should help them understand.

After the article is published, I use it as a source asset for social media.

For Stavia Models, the main platform is LinkedIn because the product, topics and audience fit the context there: founders, SaaS builders, consultants, investors and people interested in startup finance.

But the same article can be adapted for other channels too. Sometimes I prepare Telegram posts, and the same logic could work for other platforms if the angle, tone and format are adjusted to the audience.

The important part is that I do not ask AI to simply summarize the article. I ask it to find a social media angle: one specific idea, founder problem, modeling mistake, practical insight or example that can stand on its own and then point back to the full article.

This makes each article reusable without making every post feel like a smaller copy of the same text.

After the article is published, I sometimes turn it into a LinkedIn carousel. Instead of using a separate slide tool, I built a reusable slide-generation system inside the project itself.

The idea was simple: if Stavia Models already has its own product style, website components and visual language, I can reuse that same logic to generate slides in code.

Inside Cursor, I created a small system that works with reusable slide templates, a working deck and benchmark examples. It lets me keep the same colors, typography, spacing and overall product feel while generating a carousel preview and a final PDF.

The process starts with the article, but the carousel is not a direct copy of it. First I define a separate angle for the slides: one practical idea, framework or founder question that works well in a short visual sequence. Then ChatGPT helps prepare the slide logic, and Cursor turns it into slides inside the existing generator.

This approach is useful beyond LinkedIn. The same code-first system can be used to generate other presentations, one-pagers or schematic visual materials based on the product and its content.