Why I built a voice notes app instead of just using the one on my phone
Every phone has a voice recorder. I built Submind anyway. What stock apps get wrong, the technical decisions I made, and what I learned from real users.
Published · Updated · 1620 words · 9 min read · By Prashant
Every phone ships with a voice recorder. iPhones have Voice Memos. Pixels have Recorder. Samsung has Voice Recorder. They're free, they're pre-installed, and they work well enough that most people never look for an alternative.
I knew all of this when I started building Submind. And I built it anyway.
Why I built it anyway, and what I learned along the way.
The stock apps are recorders, not thinking tools
Open Voice Memos on an iPhone. Hit record. Talk for five minutes. Now what?
You have an audio file named "New Recording 47." You can play it back. You can share it. You cannot search it. You cannot get a summary of it. You cannot ask it a question. You cannot turn it into a meeting note, a journal entry, or a to-do list. You cannot connect it to your calendar or your email.
The stock apps solve one problem: converting sound waves into a digital file. That was impressive in 2009. It's the bare minimum now.
What I wanted was a thinking tool. Something that captured spoken thoughts and turned them into organized, searchable, actionable output. Not a file cabinet. A processor.
The "why another app" question I kept getting
When I told people I was building a voice notes app, the response was predictable. "Doesn't every phone already have that?" My answer was always the same: "Cameras come pre-installed too, and people still download Lightroom."
The built-in camera is fine for snapshots. If you're a photographer, you want manual controls, RAW output, and editing tools. The same logic applies to voice notes. If you're someone who thinks out loud regularly (a writer, a founder, a person with ADHD, a student, a therapist, anyone who processes information better by talking), the stock recorder is the wrong tool. It records. It doesn't think.
The real question I was answering wasn't "does the world need another voice recorder." It was "can a voice notes app do more than record."
The three things stock apps get wrong
They treat every recording as equal
A five-minute meeting debrief and a twenty-second grocery list get the same treatment. Same interface. Same options. Same useless filename. The context of why you recorded something is lost the moment you stop recording.
A good voice notes tool should ask "what kind of note is this" (or figure it out) and process accordingly. A meeting note needs different structure than a journal entry. A lecture needs different output than a creative brainstorm. Stock apps treat all audio as identical. Good tools treat them as different inputs for different outputs.
They're designed for playback, not search
The stock app's primary interaction is a play button. You're supposed to listen to your recordings. That made sense when storage was expensive and transcription was impossible. Neither of those things is true anymore.
In Submind, the play button is there if you want it, but the default interaction is text. You read the summary. You scan the action items. You search for a word and jump to that timestamp. The audio becomes a reference, not the product. This sounds like a small UX difference, but it changes the entire relationship you have with your recorded thoughts. You stop building a graveyard of audio files you'll never replay and start building a library of notes you actually use.
They're isolated
A voice recording in the stock app exists in a vacuum. It doesn't connect to anything else. It can't become a calendar event. It can't draft an email. It can't update a project tracker. It's a dead end.
This is where Submind gets interesting, and where most competitors stop. Voice notes that can take action. A recording about a client meeting that sends a follow-up email. A standup note that posts to Slack. A project brainstorm that creates tasks in Linear. The voice note stops being information you have to manually process and becomes input that processes itself.
The technical decisions that shaped everything
Some of these were right. Some I'm still unsure about.
Privacy first, cloud transcription
Submind transcribes in the cloud. That's what makes 70+ languages, speaker identification, and 1-hour sessions possible. The cost is real: your audio leaves your phone. So the privacy promise has to be about what happens next: encrypted in transit and at rest, stored in your account, never used to train models, and deletable whenever you want.
70+ languages was a bigger lift than I expected
Supporting transcription in 70+ languages sounds straightforward when you're using existing APIs. In practice, quality varies enormously by language. English transcription gets 95 percent accuracy. Urdu gets maybe 80 percent on a good day. The training data isn't equal. The models aren't equally capable. I couldn't fix this (the training data imbalance predates my app by years).
But I had to be transparent about it. Submind doesn't claim "accurate transcription in 70+ languages." It claims "transcription in 70+ languages, with accuracy varying by language." That distinction matters and most products don't make it.
AI memory was the feature that changed the product
When I added persistent memory (the AI remembers who you are, what you're working on, and facts you've mentioned across conversations), the user experience shifted. People stopped explaining themselves at the start of every session. The AI started surfacing relevant context without being asked. One user told me "it feels like the app knows me," which meant I'd done something right.
It also means I have a serious responsibility to handle that memory data carefully. No training. No third-party access. Full user control over what's stored and what's deleted. Memory is the most powerful feature and the one that demands the most trust.
The workbench was an experiment that paid off
Adding a Python sandbox that can do data analysis, generate charts, and run multi-step workflows against connected tools sounded like overengineering for a note-taking app. But the first time a user said "show me a chart of my meeting topics this month" and got an actual matplotlib chart back in fifteen seconds, I knew it belonged. People don't just want their notes transcribed. They want to understand patterns across their notes.
What users actually do with it
The use cases I expected: meeting notes, daily journaling, task capture. Those all happened.
The use cases I didn't expect:
A therapist who records session notes between patients and uses the AI to track themes across weeks of sessions. "I noticed my client brings up their father in almost every session but never directly. The AI flagged the pattern before I did."
A PhD student who records every research idea, however half-formed, and lets the AI organize them by theme. "I have 900 voice notes from the last year. I can search for 'methodology' and find every relevant thought across all of them. That would be impossible with audio files."
Someone with ADHD who told me they'd failed at every productivity system for a decade. Voice notes plus AI processing was the first thing that stuck. "The gap between thinking and typing is where I lose everything. Talking closes the gap."
These aren't personas I designed for. They're people who found the tool and used it in ways I didn't predict. That's the best outcome for any product.
What I got wrong
I thought people would use structured formats from day one. Most people use the basic note format for weeks before branching out into meeting notes or journal entries. The learning curve is not the tool. It's the habit of thinking about output format before recording. I'd do the onboarding differently now.
I underestimated how much people hate their own voice. The first playback is jarring for most new users. Some quit before they adjust. I don't have a solution for this except time. You get used to your voice the same way you get used to photos of yourself: through exposure.
I built too many features before shipping. AI memory, the workbench, integrations with over a hundred services. These are genuinely useful, but they didn't need to exist at launch. I could have shipped with transcription, summarization, and search and added the rest incrementally. The desire to build something complete slowed down the thing that actually matters: getting it in front of users.
The honest answer to "why another voice notes app"
Because the existing ones solve yesterday's problem. They record audio. That's done. The problem worth solving is what happens after the recording stops. How does spoken thought become organized knowledge? How does a voice note connect to the rest of your tools? How does your AI assistant learn who you are across hundreds of conversations?
The stock recorder answers "what did I say." A good voice notes tool answers "what did I mean, what should I do about it, and who needs to know."
That's the product I wanted. It didn't exist. So I built it.
If you want to see the current version, the free voice recorder and audio to text tool are the easiest place to start. The full product is described on Submind AI.
Related reading
Sources
FAQ
Why did you build Submind?
Stock recorders stop at audio. Submind turns the recording into a structured note you can search, ask questions of, and act on.
What do stock voice recorders get wrong?
They treat every recording the same, they are built for playback, and they do not connect to your other tools.
Is Submind private?
Audio is encrypted in transit and at rest, stored in your account, not used for model training, and deletable. The app uses cloud transcription.
About the author
Prashant is the founder of Submind. He writes about voice notes, transcription, and turning spoken thoughts into structured notes.
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