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Abe Turan
Abe Turan

Posted on Originally published at deepusecase.com

Practical AI Automation for Solopreneur Marketing: A Real-World Guide

You're a Marketing Department of One. Stop Acting Like It.

As a solopreneur, you're the CEO, the head of sales, the bookkeeper, and the entire marketing department. The constant context-switching is a killer. One minute you're closing a deal, the next you're supposed to be writing a brilliant LinkedIn post. This is why most marketing advice for solo operators is useless. It assumes you have time you don't have. This is also why most AI “hacks” are a distraction. Generating 50 mediocre blog post ideas with ChatGPT isn't a system; it’s just another task on your endless list. Effective AI automation for solopreneur marketing isn't about creating more work. It’s about building systems that run without you.

Forget the toys. I'm going to show you how to build one specific, high-value system I actually use: an automated content research and first-draft engine. This machine scours the web for relevant content, uses an AI agent to analyze it, and drops a high-quality draft for a social media post into a folder for you to review. It works while you sleep. By the end of this article, you'll have the exact blueprint to build it yourself.

Building Your Research-to-Draft Pipeline: The Actual Steps

This isn't theoretical. This is a production workflow. It has three main components: an input source, a processing engine, and an output destination. We'll connect them with an automation platform. Here’s the stack:

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  • Content Sourcing: Feedly. You need a way to aggregate high-quality articles, news, and blog posts from your industry. An RSS reader is perfect for this. I use Feedly. Their Pro plan is about $8.25/month, and for curating your input, it's a non-negotiable expense.
  • Automation Glue: Make (formerly Integromat). This is what connects the services. You could use Zapier automations, but I find Make's visual interface more intuitive for multi-step workflows, and its pricing is often more favorable for complex operations. The free tier is fine for building and testing, but you'll want the Core plan ($9/month) for it to run reliably.
  • The Brain (LLM): Anthropic's API (Claude 3 Sonnet). We're not using a web interface. That's manual labor. We need API access. I'm using Claude 3 Sonnet here because its cost-to-performance ratio is excellent for this kind of analysis and writing task as of early 2026. You could swap in OpenAI's GPT-4o, but Sonnet is cheaper and just as good for this specific job.
  • Output Destination: Google Docs. Simple, effective, and free. You could also use Notion, Coda, or even just email the drafts to yourself.

Here is the step-by-step process inside Make.com:

Step 1: The TriggerCreate a new scenario in Make.com. The first module is your trigger. Search for the Feedly app and select the “Watch Articles in Category” module. Connect your Feedly account and choose the specific category you want to monitor (e.g., “AI in Marketing”). This scenario will now automatically run every time a new article appears in that Feedly category.

Step 2: Get the Full Article TextThis is where things get tricky, and it’s my biggest gripe with this kind of workflow. Many RSS feeds only provide a summary, not the full article text. Sending a summary to your AI will get you a summary of a summary—useless. You often need to fetch the full content from the source URL. You can try Make's built-in HTTP module to make a “GET request” to the article URL. Sometimes this works. Often, it doesn’t, because sites have protections. A more reliable (but more advanced) method involves a third-party scraping tool like Firecrawl, but for now, let’s assume the basic HTTP module works or that your RSS feed is full-text.

Step 3: The AI Processing CoreThis is the magic. Add an Anthropic module to your scenario and select “Create a Message.” In the prompt section, you will give the AI its instructions. Do not be lazy here. A good prompt is the difference between gold and garbage. Here’s a prompt that works:

You are an expert marketing analyst for a solo consultant who advises B2B software companies. Your tone is sharp, insightful, and slightly contrarian. You do not use jargon or fluff. Read the following article content and perform these two tasks: First, extract the single most important insight, one surprising statistic, and one non-obvious takeaway. Second, using that information, write a 250-word LinkedIn post. The post should start with a strong hook, explain the core idea, and end with a question for the audience. Format your entire output as a single, clean JSON object with the following keys: "key_insight", "surprising_stat", "contrarian_take", "linkedin_draft".

Here is the article content:

[Map the full text from Step 2 here]

Notice the specifics. It has a role, a tone, a target audience, a clear task, and a required output format (JSON). Forcing a JSON output makes the next step incredibly reliable.

Step 4: Parse the AI's ResponseThe Anthropic module will output a text string that looks like a JSON object. Add a “Parse JSON” module to your Make scenario. Feed the text from the previous step into this module. It will turn the text into structured data (key_insight, linkedin_draft, etc.) that you can easily use in the next step.

Step 5: Create the DraftAdd the final module: Google Docs, “Create a Document.” Map the data from the “Parse JSON” module into the document. For the title, you could use something like Draft: [Original Article Title]. In the content, you can structure it nicely:

Key Insight: [Map the key_insight data here]Surprising Stat: [Map the surprising_stat data here]Contrarian Take: [Map the contrarian_take data here]

LinkedIn Draft:[Map the linkedin_draft data here]

Save and activate your scenario. That’s it. Now, every time a new article hits your Feedly, this system will read it, analyze it, and write a thoughtful social media draft for you.

What Do Most People Get Wrong With This Setup?

Many people try something like this and fail. It’s usually for one of three reasons.

First, they use the wrong tool for the job. They play around in the ChatGPT web interface, manually copying and pasting articles. That is not automation. It's a glorified copy-paste macro. You are still the bottleneck. To build a real system, you must use the API. It's the only way to have services talk to each other without your involvement.

Second, their inputs are garbage. The most important part of this entire system is your Feedly curation. If you subscribe to 100 low-quality blogs, your AI agent will produce 100 low-quality drafts. You must be ruthless. Find the 10-15 sources that are truly insightful in your niche. A smaller, high-quality input stream is infinitely better than a wide, noisy one.

Finally, they expect a finished product. This is the biggest misconception. This is an AI assistant, not an AI replacement. The goal is to get you 80% of the way to a published piece in zero minutes of your active time. The drafts it creates are starting points. They require your unique perspective, your final polish. Anyone selling a ready-to-deploy AI agent that can run your marketing on full autopilot is lying. The value is in eliminating the blank page and the tedious research, not in firing yourself.

Is This System Really Worth the Cost and Effort?

Let's talk money. Feedly Pro is about $8/mo. Make.com's Core plan is $9/mo. The Claude 3 Sonnet API calls for, say, 200 articles a month might run you $5. The total monthly cost is under $25. Is it worth it?

Absolutely. It's a bargain.

Think about the time cost of the alternative. Finding and reading one good article might take 15 minutes. Distilling its points and writing a decent social post might take another 30. That’s 45 minutes per post. If this system gives you just four solid drafts a month, it has already saved you three hours of work. For $25. The ROI is obvious.

But the real benefit isn't the time saved; it's the consistency. Marketing for a solopreneur fails due to a lack of consistency. You get busy with client work and your marketing goes dark for a month. This system doesn't get busy. It works every single day. My favorite part is waking up, opening a folder, and seeing three or four new, intelligent ideas waiting for my review. It completely changes the creative process from a stressful “what should I post?” to an easy “which of these great ideas do I want to share today?”

This is a real, deployable blueprint for an AI marketing assistant. It’s not a theoretical trick. It requires a few hours to set up and, yes, some things can break (like a website changing its HTML and breaking your scraper), but the core logic is sound and incredibly powerful for a one-person operation.

You can build this entire workflow yourself following the steps above. It’s a great way to learn how these systems are put together.

Adjacent reading: deeper coverage of AI agent platforms.

If you'd rather skip the build and deploy a working version in an afternoon, we've packaged this exact workflow as a ready-to-deploy blueprint. You can find it at deepusecase.com/vault.

Originally published at deepusecase.com

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