Got to $250/mo with a transcription tool — here’s what actually worked
Spent weeks building what I thought was a “perfect” product.
Got almost no users.
What changed everything:
I stopped polishing and started talking to people actually doing transcription work.
Freelancers, editors, agencies.
One pattern kept showing up:
Getting transcripts isn’t the hard part.
Cleaning them, formatting subtitles, structuring output — that’s where hours go.
So I focused on that.
Instead of:
“AI transcription tool”
I positioned it as:
Raw video or URL → clean transcript + subtitles + Translations + summary + chapters + exports in minutes
One test:
A messy 1-hour video
→ fully structured output in ~4 minutes
That’s when the first users converted.
Now at ~$250/month — still early, but real signal.
Curious:
If you’ve used tools like Turboscribe/Otter/Descript — what’s the most annoying part of your workflow?
Link (if you want to try it):
https://videotext.io/video-to-transcript
Being fast alone is not the thing? What about accuracy and speakers? Because it is pointless if the accuracy is trash And cannot detect speakers. Tools I tried does miserable on video files that has 3-6 speakers. I am convinced that human transcription is best if accuracy matters!
That's make totally sense. VideoText addresses the exact issues, it is optimised for both speed as well as accuracy. we have benchmarked VideoText against some major tools.
it is 98.5% Accurate. and users can also edit transcripts, map speaker names and get the exports
Looks like it only works for YouTube URL and not others……looks clean tbh
Hello, Thanks for the comment. As of know we support YouTube URL, direct Video files and Voice to text transcription. we are actively testing multiple integrations.
I’ve tried a few transcription tools and always end up spending time editing the output.
If this gets closer to ready-to-use, that’s useful.
This is interesting — especially the “no cleanup” angle.
In my experience, transcripts are easy, but formatting/subtitles take the most time.
How does this handle messy audio or multiple speakers?
the positioning shift from "AI transcription tool" to "raw video to structured output in minutes" is such a good example of the difference between describing what something IS vs what it DOES for the user. i've made the same mistake - leading with the tech instead of the outcome.
$250/mo from a real workflow pain point is a solid signal. are most of your paying users freelancers or agencies? curious because agencies might pay way more for bulk processing if you haven't explored that yet
The positioning shift is the real takeaway here. I went through the same thing with my product. Started with "AI check-in analysis for fitness coaches" and got blank stares. Switched to "turns a 20 minute client review into 2 minutes" and people immediately got it.
Framing around the outcome instead of the technology made all the difference. Congrats on the 250 a month, that early traction is the hardest part.
This is a great insight — “the hard part isn’t transcription, it’s what happens after” feels very true.
I’ve run into something similar. Even once the output is clean and structured, it still doesn’t always translate into something you can actually reuse. It kind of just sits there unless you actively pull out the parts that matter.
I’ve started treating it more like a pipeline:
capture → clean → extract → reuse
Most tools seem to stop at the “clean” stage.
Curious if you’ve seen users trying to go beyond that, or if most are satisfied once the transcript is structured.
Where did you get the traffic from ? Paid ads ? How did you get to these people ?
the downstream pain is where the real product lives. 'AI transcription' is table stakes now - clean output you don't have to touch is the actual thing people pay for.
Hey, really like the direction of your app.
I think there’s a strong opportunity to increase visibility and bring more active users in.
We work on app distribution and user acquisition through trusted networks.
Would be happy to discuss if you’re open.
Nice shift — ‘transcription’ is crowded, but ‘clean, structured output fast’ actually hits the real pain. For me, the annoying part is fixing timestamps and formatting subtitles after export — that’s where most time gets wasted.
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