When it comes to AI, most of us are already using it in one way or another. While marketers may lean on it for data analysis, to clean up some copy, or automate repetitive tasks, most marketers primarily use it as a search engine.
Whether it’s ChatGPT, Claude, or Gemini, chances are you’ve already used one of these tools to research something. But what if, instead of using existing AI to gather insights on your audience, you could build your own AI research tool?
Kamil Rextin did just that, using Claude Code to build his own paid media co-pilot. Not a prompt or a ChatGPT conversation but an actual tool that converts plain-English audience descriptions into LinkedIn-ready targeting parameters.
In this article, we’ll show why this is bigger than AI paid media automation and how prompt-to-taxonomy targeting changes the GTM workflow from planning and segmentation to activation and iteration (and why it’s quickly becoming a defensible advantage).
Table of contents
Why platform UX is your hidden tax
When you build a campaign, you’re not doing strategy for 80% of the time. You’re navigating the user interface (UI), scrolling through dropdown menus that weren’t designed for speed, but comprehensive coverage.
LinkedIn wants you to see every possible targeting option because that’s how they surface features you didn’t know existed.
Great for LinkedIn’s adoption metrics. Terrible for your throughput.
Every campaign becomes a 30-minute exercise in:
- Remembering which job titles you used last time;
- Wondering if “Software Engineering” and “Software Development” target different people;
- Second-guessing whether you excluded the right seniorities;
- Copy-pasting the same targeting structure across three audience variations.
The cognitive load isn’t strategy; it’s interface navigation.
You’ve probably already felt this friction and thought, “There has to be a better way.” Now, you can build that better way yourself in less time than it takes to set up your next campaign manually.
From AI research to building paid media AI research tools
Research is basically extraction. You’re pulling insights from AI, then manually translating those insights into platform actions. The handoff between “understanding the audience” and “configuring the targeting,” however, is still manual.
We rebuilt Kamil’s approach and expanded it to remove that gap.
We wanted multi-platform coverage: LinkedIn, Meta, Google, X, TikTok, and Reddit. And, instead of just targeting automation, we wanted ad copy variations generated alongside the audience parameters.
All it took was Lovable (a no-code AI app builder) and a structured prompt to build the tool in just 5 minutes.
Here’s what it does:
- You input your website URL.
- The AI scrapes and analyzes your positioning, value proposition, and product details.
- It generates an ICP based on that analysis.
- It maps that ICP to platform-specific targeting criteria (LinkedIn job titles, Reddit subreddits, Meta interests).
- It writes ad copy variations tailored to that audience.
How we built it
The entire build came down to prompt structure. The framework breaks into three parts:
1. System role
Define who the AI is and what expertise it needs:
You are a performance marketing strategist with expertise in:
– Multi-platform paid media campaign structure
– Audience segmentation and ICP development
– Platform-specific targeting taxonomies
– Ad copy best practices by channel
Your goal is to convert business context into platform-ready campaign assets.
2. User inputs
Specify exactly what the user provides:
The user will provide:
– Website URL
– Product/service description
– Target advertising platform
These inputs drive the audience and creative analysis.
3. Analysis instructions
Tell the AI how to think, step by step:
Step 1: Scrape and analyze the website to understand value proposition, positioning, and product details.
Step 2: Derive an ICP based on who would benefit most from this product.
Step 3: Map that ICP to platform-specific targeting parameters.
Step 4: Generate ad copy variations following platform best practices.
That’s it. That structure gave Lovable enough context to build a working app in minutes.
The first version worked immediately. Even though the UI wasn’t perfect (free plan gets you five daily credits, so you’re limited on refinement iterations), the core functionality shipped instantly.
What it does, platform by platform
We tested it across LinkedIn and Reddit to see if it truly understood platform differences, and here’s what we found.
LinkedIn test
Input: Our website URL + “target B2B marketers”
Output:
- Job titles: “Marketing Director,” “Demand Generation Manager,” “Growth Marketing Lead”
- Functions: Marketing, Business Development
- Seniorities: Manager, Director, VP
- Skills: Demand generation, ABM, marketing automation
- Industries: Marketing and Advertising, Computer Software
Plus three ad copy variations emphasizing efficiency, systems thinking, and practitioner credibility.
Reddit test
Same inputs, different platform.
Output:
- Subreddits: r/marketing, r/PPC, r/digital_marketing, r/adops
- Interest targeting: Performance marketing, B2B marketing, advertising technology
- Community behavior indicators: Active in marketing career discussions, frequently asks about tools and platforms
Different taxonomy and tone in the copy, but platform-specific recommendations.
The AI research tool didn’t just regurgitate generic advice. It adapted to each platform’s targeting structure because we told it to.
Where this gets interesting: The API layer
Right now, the tool gives you insights. You still copy-paste targeting into LinkedIn or Reddit manually and transcribe the ad copy.
The next iteration connects platform APIs.
Instead of generating a targeting recommendation, it drafts the campaign directly in LinkedIn Campaign Manager, and rather than suggesting ad copy, it populates the creative fields.
And what’s great about building this paid media AI research tool is that it’s not some massive engineering project; it’s API documentation and a few integration prompts.
But here’s where it compounds:
Once you connect performance data, the tool can learn. It sees which audiences convert, which targeting combinations underperform, and which ad copy variations drive CTR.
Then it starts making recommendations based on your actual results, rather than industry best practices scraped from blog posts.
That’s the obvious next step once you’re generating campaigns programmatically.
Why marketers should build, not just prompt
This isn’t about replacing platforms. LinkedIn’s targeting is still valuable, and Meta’s audience network is still powerful.
This is about eliminating the friction between strategic thinking and platform execution.
Every hour you spend clicking through dropdowns is an hour you’re not spending on:
- Testing new audience hypotheses;
- Analyzing performance patterns;
- Iterating creative approaches;
- Actually talking to customers.
The platforms want comprehensive UIs because they serve millions of advertisers with different needs. That’s fine. But you don’t need ‘comprehensive’. You need ‘fast and repeatable’ for your specific use case.
Building your own tools gives you:
- Speed: Faster audience setup means going from idea to live campaign in minutes, instead of hours.
- Consistency: Every campaign uses the same targeting logic, no missed parameters.
- Experimentation velocity: Spin up three audience variations in the time it used to take to build one.
- Fewer targeting mistakes: Reduce missed job titles, wrong seniorities, or forgotten exclusions.
- Lower cognitive load: Focus on strategy instead of platform mechanics.
You already have the AI literacy to do this.
If you’ve written a detailed ChatGPT prompt, you can build it. The only difference is where you paste that prompt. Instead of a chat interface, you paste it into Lovable or Cursor, or any AI app builder.
Same skill. Different output.
What to do next
Start with the friction you feel most.
The dropdown you click through every single campaign. The targeting structure you copy-paste constantly. The ad copy variations you manually rewrite for each platform.
Then build your own version that removes it. Here’s the five-minute, five-step targeting automation flow:
1. Copy the prompt framework: We’ve shared the full prompt [here]. Use it as-is or adapt it to your specific platforms and workflow.
2. Choose your builder: Lovable is great for non-technical marketers, whereas Cursor or Claude Code work if you want more customization. All are AI-native, meaning you build with prompts, not code.
3. Test with one platform first: Don’t build multi-platform targeting on day one. Start with LinkedIn or Meta (whichever you use most) and prove the concept.
4. Add the API layer when you’re ready: Once the targeting generation works, connect platform APIs to draft campaigns directly. This is where the time savings become ridiculous.
5. Feed it performance data: As campaigns run, pull performance metrics back into the tool. Let it learn what actually works for your business, instead of what works in theory.
Stop renting, start building
We’ve been conditioned to think of marketing technology as something we subscribe to. Platforms we log into. Tools we evaluate and purchase.
But the new reality is that you can build your own stack, with the AI tools you’re already using for research and copywriting.
The question isn’t whether AI will change marketing workflows. It already has.
The question is whether you’ll keep using AI as a better search engine, or whether you’ll use it to eliminate the grunt work that shouldn’t exist in the first place.
Want to learn more about how to make AI work for you?
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