When ChatGPT dropped, Adithyan Ilangovan quit his job with no plan. He built a product and failed. Then, he built a tech-enabled service called AI Podcasting and hit $14.5k MRR.
He did that via a hybrid model that he says is underutilized.
Here's Adithyan with the details. 👇
I did my master's in Video Engineering. After my studies, I worked and built video engineering infrastructure at a couple of startups and then led product at Paramount Studios. So you could say I'm a research engineer with a product background.
I wasn't super happy with thecorporate grind, and I had always wanted to start a business. It was always on the back of my mind. I wanted to do something meaningful and have some more free time for myself — ha! What a trap that was!
Anyway, ChatGPT happened, and I knew immediately that something big was coming. Things were going to change.
So, like many others, I jumped on the AI bandwagon. I quit my job with no specific plans and struggled for the first six months.
I started by building a great technical product, but it was not a good business.
Eventually, I figured stuff out. I mean, it's still a struggle, but I have some revenue now.
I run AI Podcasting (AIP), a podcast post-production agency.
Podcasters record their content, and we handle everything after that: editing, audio, notes, social, publishing, distribution, paid/organic promotion, and more.
Basically, our pitch is that you can focus on telling the story you want to tell to your audience, and we will help you deal with all the stuff that comes after clicking "finish recording."
We're at about $14.5k MRR.

We figured out which market to serve, then did a lot of work manually — we didn't build a product right away. We did the same things the creator or the podcaster would have done after recording their content.
Basically, we lived through their pain. And it was brutal.
We kept doing it for two to three months until we slowly started realizing what parts could be easily automated with AI and other tools.
Eventually, we were able to automate about 80% with AI. So that is basically our margin.
Our internal tool stack is:
Python for backend
NextJS for frontend
OpenAI APIs for AI workflows.
FFmpeg for media
MongoDB
Azure Infrastructure
Modal for a lot of scale for Python containers
Lots of custom automations
We use a done-for-you subscription model. Flat monthly pricing, usually $2.5k to $3k for 4 episodes per month, with add-on hours as needed. Revenue growth comes from expanding workflows with existing customers.
I think this is an important note: Not many people are exploring the hybrid model of heavy automation plus human polish. There is definitely a market premium and demand for that.
Everything that can be 100% automated will mean competition — and soon, it'll get sucked up by the frontier model providers.
But this hybrid model of running like an agency creates a moat. Especially with the personal relationships that you will build with the clients — and trying to understand their preferences and pains. It's hard to get in. But once you are in, you can lock in and keep increasing your margins with AI.
Things are moving rapidly, so I can't say for sure — but that's my take for now.
As far as growth, we could do much more — and much better.
We've grown mostly from referrals. Strong outcomes drive word of mouth. We also grow via inbound from the creator network.
We haven't done any paid ads so far.
If I had to startover, here's what I'd do:
I wouldn't start with B2C as I did with my original project. I'd start with B2B.
I wouldn't build a thing until I had a customer, or at least clearly understood the pain point (via The Mom Test).
I would charge early and charge high.
If you are a hacker, most likely you will love building; that's the easy part. But you need to get out and talk to real customers early and often.
Do not overbuild, or I would go as far as saying it's better not to build anything at all at the start.
And try to understand the market very clearly. If you think you already have a good understanding, stress-test your assumptions. The Mom Test is a very good book in that regard.
If I had done this properly, it would have saved me maybe six months of pain.
Here's a very good test: "Is this person willing to give their credit card information for my solution to the problem they say they have?" That's it. Simple.
And after that, if people are willing to stick around, you're on the right track.
I want to scale the production engine while keeping quality high. Serve more high-value creators. Keep improving automation. And eventually, build a tooling layer for smaller creators.
Also, I am terrible at marketing and sales. I'd like to improve.
You can learn more at AIP. And you can find me on X, LinkedIn, Github, and my personal website.
Leave a Comment
Awesome Story!!!
Interesting perspective. Many founders overbuild before validating demand. I learned that the hard way.
Great example of why starting with the customer matters more than building the perfect product. The hybrid model of automation + human service is really interesting — it creates both margins and a moat. Curious how much of the workflow you think can realistically reach 90–95% automation.
Great story!
Great story: starting manually to truly understand the customer pain before automating is such an underrated strategy. Impressive how you turned that insight into a hybrid AI + human service with real traction.
Really interesting take on the hybrid automation + human polish model.
I think a lot of builders underestimate how fast fully-automated tools get commoditized, especially in AI. The moat seems to come from workflow ownership + relationships, not just automation.
Curious, at what point did you feel confident enough in the manual process to start automating parts of it?
strong example of “manual first, automate later.”
doing the work yourself forces you to see where the real friction is. that’s usually where automation actually creates margin.
a lot of founders start by building the tool. you started by living the workflow.
The "no plan" leap resonates so much, and evolving into that successful hybrid model really highlights the power of iteration. It's like watching a rough sketch of a Cartoonperson gradually become a fully-formed character with a clear purpose.
ccoool
The "agency as testing ground" model is one of those things that sounds obvious after the fact but almost no one structures intentionally. Most people treat agency work as the thing they're trying to escape, not the thing that validates what to build next. Curious how long you ran the agency side before you felt confident enough to double down on the product?
Avec cette technologie , il suffit d'un peu de savoir faire pour sortir de la galère définitivement.
Very insightful story Thanks for sharing it
The 'Hybrid Model' is definitely the gold mine for 2026. Automation gets you 80% there, but that last 20% of 'human polish' is where the moat is. I’ve been analyzing the unit economics of exactly this—how much frontier models (GPT-4o) cost vs. the 'Retry Tax' of cheaper models like DeepSeek when you're trying to hit that 80% automation mark. Adithyan, did you find that switching to cheaper models for the initial 80% workflow significantly improved your margins, or does the loss in reasoning quality end up costing more in human 'polishing' time?
Very insightful story! Thanks for sharing it honestly.
I too landed myself in the exact situation but unable to push myself out of the comfort zone. Hope i can make it like u someday
Really respect the journey here. Quitting without a clear plan and still pushing through the uncertainty takes courage most people don’t talk about. A lot of founders stop during that first phase where things don’t work yet, but that’s usually where the real learning happens.
What stood out to me most is the hybrid model you described — starting manually, understanding the real workflow pain, and then automating gradually. That approach feels much more sustainable than jumping straight into building a fully automated product without seeing how the work actually happens in real life.
I’m currently building a platform myself and one thing I’m realizing is that the market often rewards founders who stay close to the problem longer instead of rushing into building too much too early. Your story reinforces that.
Wishing you continued growth as you scale the production engine. It’s inspiring to see builders turning early struggles into a working system.
Nice project!
I'm a UI/UX and graphic designer. I help startups with landing pages, SaaS dashboards, and product UI. If you ever need design help, I'd love to collaborate.
interesting that the pivot wasn't to a different product but to a service wrapper around the same space — what did the hybrid model actually look like in the early days before it was generating real revenue?