Quick Answer
- A strong AI content pipeline starts with a clear workflow, not a blank document.
- Jira tracks the stage, Lindy handles automation, and a story database supplies relevant examples.
- The goal is not to replace thinking; it is to remove the friction of starting and retrieving context.
A few months ago I was sitting on a call with the Lindy team, doing a live demo of my workflows. Someone asked me to show something that actually surprised me when I built it.
So I screen-shared my Jira board.
I dragged a card from Backlog to In Progress.
Within about two minutes, a podcast outline hit my Slack with real stories from my past conversations already tagged in… relevant ones, not random ones. The agent had searched my Supabase vector database, found the stories that matched the topic, and built the outline around them.
The Lindy team was quiet for a second. Then: Wait. Can you show that again?
Yeah. That is the one.
The problem content creation actually has
Most people think the bottleneck is writing. It is not. The bottleneck is starting.
Before I built this system, I would sit down to record a podcast and spend 20-30 minutes organizing my thoughts. Looking for the story I told three months ago that was perfect for this topic. Flipping through notes.
Not terrible. But I record 2-3 times a week. That friction compounds.
How the system actually works
Three pieces.
The Kanban board (Jira). Each quarter I plan my content topics and drop them into Jira as cards. When I am ready to create, I drag the card to In Progress. That drag is the trigger.
The webhook to Lindy. Jira fires a webhook the moment a card changes status. Lindy is listening. The agent receives the card data and that context becomes the input for what it builds.
The Supabase story database. Over the past couple years I have been running a transcript-first system where every meeting, coaching call, podcast recording, and workshop gets transcribed and stored. I extracted the stories from those transcripts and stored them in Supabase with vector embeddings.
So when Lindy gets the topic, it runs a semantic search against that database. Finds the 3-5 most relevant stories. Tags them into the outline.
Total time from drag to outline: a few minutes.
Why this works better than just prompting an LLM
A generic outline is fine. But generic outlines produce generic content.
When the outline already has a specific story from a real client call, the recording goes somewhere real. The stories are mine. The AI just surfaced them at the right moment.
Your past conversations are a goldmine. The problem is not that the material is not there. The problem is accessing it when you need it.
The bigger thing I noticed
Once you have a system synthesizing across your transcripts, you start seeing patterns you would never catch otherwise.
I added a companion workflow that runs every Friday. It reads the transcripts from all my meetings that week, sometimes 15 to 20 of them, and gives me a summary of recurring themes and patterns.
Reading one meeting in isolation, you cannot see it. But when an agent reads 20 meetings and tells you four different clients mentioned confusion around the same thing… that is actually useful intelligence. Not just notes. Insight.
That took maybe two hours to build on top of what was already there.
The 80-20 principle for agent building
Does this task happen daily or weekly? If yes, build it now. Is this a quarterly or one-off thing? Wait.
The math on compounding is brutal in your favor when something runs every week. A workflow I built in one weekend has now run over a hundred times. The ROI cleared in the first two weeks.
That is the first agent most people should build: whatever their version of I do this every week and the prep is annoying is.
How to build something like this
You do not need Jira or Supabase.
- A Google Sheet can replace the Kanban board. Add a status column. Use Zapier or Make to watch for status changes.
- A simple Airtable base with tagged notes can replace a vector database.
- The Lindy agent can be a Make scenario or a custom GPT with a retrieval plugin.
The core pattern: trigger on status change, look up relevant context, assemble structured output.
What changed after building this
The biggest change is not speed. It is energy.
When I sit down to record, I am not in logistics mode. I am in thinking mode from the start. The outline is there. The stories are there. I just bring the energy and the perspective.
Build the system once. Let it do the boring part. Show up for the part only you can do.
Want to build something like this? The best starting point is my Productivity Academy where we walk through agent building from scratch with real workflows.
