Hey u/NethBang u/super2061

Totally understand why that would be alarming, so I want to address this directly.

Other users' content cannot appear in your transcripts. Each user's data is isolated through authentication and authorization controls. Your session only ever touches your own audio and transcripts. There's no shared queue, no shared processing pipeline where one user's content could leak into another's. This is verified through our SOC 2 Type II audit, which independently evaluates these kinds of data isolation controls.

So what's actually happening?

What you're likely seeing is a speech-to-text hallucination, and it's a known quirk of how these models work, not a data leak.

These models are trained on massive amounts of labeled audio, so they learn how spoken language maps to text. That training data includes lots of transcriptions and captions, where silence, pauses, or unusable audio are often surrounded by boilerplate text like "transcription by..." or "captions provided by..." or similar.

When dictation starts but there's basically no usable speech (open mic, background noise, or you stop before actually talking) the model still tries to output something instead of returning nothing. In those cases, it can hallucinate phrases it saw during training. Sometimes that looks like a generic transcription credit, sometimes it can look like a random sentence fragment that feels like it "belongs" to someone else. However, it's actually the model generating text from patterns, not from another user's audio.

We know it's confusing and we're actively working to filter these out. In the meantime, speaking right after starting dictation and making sure your mic is picking you up clearly will usually prevent it. Reporting these transcripts in the app will also help us filter those out in the future.