In this article, I want to show you one of the simplest but highest-leverage AI use cases I’ve built recently: scraping and structuring LinkedIn profile data in seconds using an AI-powered workflow.

This isn’t shady black hat tactics or browser bot software - it’s using a modern, no-code AI platform to turn a list of LinkedIn profile URLs into clean, structured, usable data you can actually work with.

If you do outbound, research, recruiting, partnerships, or CRM enrichment, this will save you hours.

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The Real Problem With LinkedIn Research

Most teams still do LinkedIn research the hard way.

You know the flow:

• Open a profile

• Scan job title, company, background

• Copy-paste into a spreadsheet or CRM

• Repeat… endlessly

It’s slow, error-prone, and incredibly expensive when you factor in time.

Sales and marketing teams don’t struggle because LinkedIn lacks data.
They struggle because extracting and structuring that data doesn’t scale.

This is exactly the kind of problem AI agents and tools are good at solving.

What This Workflow Actually Does

At a high level, the system does three things:

1. Takes a spreadsheet of LinkedIn profile URLs

2. Scrapes each profile automatically

3. Outputs structured, enriched data you can immediately use

Within a minute or two, you end up with a downloadable file that includes:

• A short professional background summary

• Current company

• Company website

• Company size

• Education details

• Any additional fields you choose to extract

No copy-paste. No manual research. No code.

Why I Used Relevance AI

I built this using Relevance AI, which has quickly become one of my go-to platforms for building AI-powered workflows and agents.

What makes it different is the way it separates:

• Tools — single-purpose workflows like scraping or enrichment

• Agents — multi-step systems that can reason, make decisions, and connect tools together

For this use case, I deliberately kept things simple and used a tool, not a full agent. That’s an important point.

Not everything needs an agent. Sometimes you just want a fast, reliable workflow that does one thing well.

Cost and Access (Important)

You don’t need an expensive plan to do this.

On the free plan, you get 100 credits. Scraping a single LinkedIn profile uses about 3 credits, which means you can scrape roughly 30 profiles completely free.

After that, credits are extremely cheap, making this viable even for small teams or solo operators.

This matters because many LinkedIn scraping tools lock you into high monthly fees before you’ve proven the workflow is useful.

How the Workflow Works (Conceptually)

Here’s the logic behind the setup.

First, you upload a spreadsheet containing LinkedIn profile URLs.
It doesn’t need to be fancy. A single column with profile links is enough.

Once uploaded, you run the workflow in bulk. The system processes every row automatically.

Behind the scenes, the workflow follows a simple structure:

• The LinkedIn URL is stored as an input variable

• That variable is passed into a LinkedIn scraping step

• The raw profile data is then passed into a language model

• The language model extracts and formats only the fields you care about

That final step is the key difference.

Instead of dumping messy raw data into a sheet, the AI interprets and structures the information so it’s immediately usable.

Customising What Gets Scraped

This is where the workflow becomes genuinely powerful.

If you want additional fields — for example:

• Job title

• Location

• Industry

• Seniority level

You don’t need to rebuild anything.

You simply update the prompt in the language model step and tell it what to extract. Save the change, rerun the workflow, and those new fields appear in the output.

This flexibility is what turns the tool from a basic scraper into a research and enrichment engine.

Practical Use Cases

This setup is especially useful if you’re:

• Building targeted outbound lists

• Enriching CRM records

• Researching prospects before personalised outreach

• Analysing a specific segment or role

• Preparing account-based marketing campaigns

Instead of spending hours gathering context, you start with structured data and focus on messaging, strategy, and relevance.

Where This Fits in a Bigger AI System

On its own, this is a powerful workflow.

But it becomes even more valuable when combined with agents.

For example:

• Feed the scraped data into an outbound personalisation agent

• Use it to auto-generate first-line openers for cold emails

• Enrich lead records automatically before sales outreach

• Segment profiles and route them into different campaigns

This is how AI stops being a novelty and starts becoming infrastructure.

Watch the Full Demo

If you want to see this built and run end-to-end, I’ve recorded a full walkthrough showing:

• The spreadsheet setup

• The bulk run process

• The internal workflow structure

• How to customise extracted fields

🎥 Watch the full demo below to see exactly how it works in practice.

Final Thought

AI agents and tools don’t replace thinking.
They replace manual effort.

Scraping LinkedIn profiles isn’t a strategic advantage.
What you do with that data is.

This workflow simply removes the friction between curiosity and action, so you can spend less time collecting information and more time using it.

More automations coming soon.

Until next time,
Richard

Today, I want to show you one of the smartest, most practical uses of AI in event marketing right now: a content repurposing agent system that automates blog posts, social content, and more - using your long-form videos as the engine.

I built this to save time, but it ended up saving thousands in freelancer and ops costs too. And the best part? It doesn’t just spam out low-quality AI content. This system blends automation with human oversight, making it ideal for marketers who care about quality, brand voice, and trust.

In this post, you’ll learn:

• What AI agents actually are (and how they differ from prompts)

• Why multi-agent systems matter for marketers and creators

• A breakdown of the repurposing system I built

• How to build and run your own - without writing a line of code

Let’s get into it.

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First, What Is an AI Agent?

If ChatGPT is a helpful assistant waiting for prompts, an AI agent is a proactive operator. It can:

• Trigger tasks from new data (like detecting a new YouTube upload)

• Use tools autonomously (transcription, publishing, formatting)

• Make decisions and delegate tasks

• Loop humans in for review and approvals

This means agents don’t just answer. They execute - across an entire workflow.

If you’ve ever wished for a mini marketing ops team to help you get more from your content, multi-agent systems make that possible.

Why AI Agents Matter for Marketers

Marketers today are stuck between two extremes:

1. Doing everything manually - writing, formatting, posting, promoting

2. Over-automating with low-quality AI content

Agents offer a better path.

You still create the source material - a podcast, a blog, a webinar - but instead of slicing it manually, your agents:

• Transcribe it

• Repurpose it for different platforms

• Format and post it

• Send everything back to you for final sign-off

This means you keep creative control while massively reducing the execution load. And because it’s built on your own content - not ChatGPT hallucinations - it reflects your voice, expertise, and goals.

Inside My Repurposing Agent System

I built this system using Relevance AI - a no-code platform for building AI agents - and Make.com to handle integrations and publishing.

Here’s how it works.

The Manager Agent

At the core is the YouTube Repurposing Manager Agent. It’s the orchestrator. When I upload a new video, it:

• Triggers the workflow

• Sends the link to specialist sub-agents

• Collects and routes feedback

• Publishes final versions via Webflow or LinkedIn (after I approve them)

The Sub-Agent Team

Each sub-agent handles one specific task:

• Blog Writer Agent - transcribes the video and writes a high-quality blog post

• LinkedIn Writer Agent - creates native social content

• Email Writer Agent (optional) - drafts newsletter copy

• Evaluation Agent (future) - reviews outputs for quality and consistency

Each sub-agent is set to auto-run, meaning once the manager delegates a task, they handle it end-to-end and return the result.

Human-in-the-Loop Review

This is critical. I’ve built a review loop directly into the manager agent. Once a sub-agent finishes its draft:

• It sends it back to me for review

• I can suggest edits (e.g., tone, clarity, structure)

• The manager sends that feedback back to the sub-agent

• The agent revises the content and resubmits

Only once I give final approval does the system publish anything.

What This System Actually Does

Let’s walk through the full workflow for a single YouTube video:

1. Trigger - A new YouTube video is detected via Make.com

2. Transcribe & Draft Blog - Blog writer transcribes the video and writes the post

3. Review - Manager agent sends it to me for feedback

4. Revise - Blog writer incorporates feedback

5. Format - The post is converted to HTML with proper styling

6. Publish Blog - Webflow tool posts the final article to my website

7. Draft Social - LinkedIn writer crafts content from the same video

8. Review & Approve - I make small edits, upload visuals, and hit publish

In a few hours, one video becomes a blog, a LinkedIn post, and optionally an email - with minimal human friction.

Why This Matters for You

If you’re a:

• Social manager for a event who must 10x content without 10x effort.

• A B2B event marketer sitting on hours of session footage.

• Event organiser with a library of keynotes/breakout sessions.

This kind of system gives you leverage. It turns one core piece of content into many - consistently, professionally, and with human oversight.

Even better, it saves you:

• Countless hours of admin and formatting

• Thousands of dollars in freelancer costs

• Mental overhead from switching between platforms and tools

And because you’re still involved in reviewing content before it goes live, you don’t compromise on brand, tone, or polish.

How to Build Your Own (No Code Needed)

You’ll need just three tools:

• Relevance AI (to build agents)

• Make.com (to connect systems and post content)

• Your content (YouTube, podcasts, webinars, etc.)

Here’s the high-level setup:

1. Build a Manager Agent — define its SOP (standard operating procedure), delegate tasks, and add approval steps

2. Create Specialist Sub-Agents — one for blogs, one for LinkedIn, one for email; each with a specific prompt and tool

3. Use Make.com Webhooks — to automate publishing to Webflow or LinkedIn

4. Trigger Automatically — when new content is uploaded, it kicks off the flow

No code. No API headaches. Just clear logic and modular tasks.

Watch the Demo

Want to see exactly how this system runs in real time?

🎥 Watch the full demo below where I walk through:

• The agent dashboard

• A real YouTube repurposing workflow

• How I built each agent

• The Webflow and LinkedIn publishing integrations

Final Thoughts

AI agents are the next big unlock for content teams.

They’re not here to replace your creativity — they’re here to amplify your execution. To turn a single video into a multi-channel content strategy. To remove the bottlenecks between ideation and publishing.

If you’re already creating valuable content, this system turns it into an engine.

I hope this breakdown helps you see what’s possible. Now go build your own — and spend less time formatting blog posts.

Until next time,
Richard