The Easiest AI Workflow I’ve Built, and It Saves Me Hours on LinkedIn
How I use a no-code AI tool to turn LinkedIn profiles into structured lead data in minutes
👋 Hey there, I’m Richard. Each week, I break down practical AI workflows that help marketers, founders, and operators work faster without sacrificing quality or control.
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.
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:
Takes a spreadsheet of LinkedIn profile URLs
Scrapes each profile automatically
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