Private Voice Reflection App: Daylogue Support and Privacy

What happens when you speak

A voice check-in is a live conversation, not a stored voice memo. Voice check-ins use Deepgram for speech-to-text and ElevenLabs for conversational voice. See /features/voice for the current experience and /compare/best-voice-journaling-app for a buyer-level comparison.

Daylogue does not store voice recordings. The current voice and privacy pages say audio is processed in the moment and discarded after extraction. That removes the recording itself from the saved record, but it does not mean the conversation leaves no data behind.

What remains after the check-in

Daylogue saves structured information pulled from the words you chose to share, including fields used for check-ins and pattern history. The product source describes mood, energy, stress, and key themes as examples. Saved outputs are readable by Daylogue's servers because later narratives and patterns depend on them.

Audio retention is one question. Transcript and extracted-data retention are separate questions. The current /features/voice page explains the voice processing path, while /privacy documents storage and privacy details. Those pages provide the current public boundaries for each part of the question.

What private means here

Private does not mean invisible to the service. Voice check-ins are not end-to-end encrypted. Daylogue's privacy policy at /privacy says entries, check-in fields, AI-generated summaries, and structured metrics may be stored in readable form so server-side features can work.

The policy also states that data in transit is protected by TLS and that database controls limit records by user. Those are concrete protections, but they solve a different problem from end-to-end encryption. Choose based on the processing boundary you need, not on a single privacy label.

• No stored audio recording after the live check-in
• Readable saved data for narratives and pattern features
• TLS protection while data moves between systems
• Per-user access controls around stored records
• No end-to-end encryption for the voice check-in record

Your words are the input

Daylogue does not run emotion recognition on your face or on the tone of your voice. It works from what you choose to share. A pause, accent, volume change, or facial expression is not supposed to become a hidden judgment about how you feel.

That boundary is especially important in a voice product because natural conversation can feel more perceptive than it is. Read follow-up questions as responses to the content of your check-in. You remain the person who decides whether a label or returned theme fits.

Check training and provider boundaries

Your content is not used to train AI models. The privacy policy names the providers involved in voice and AI processing and says they operate under zero-data-retention arrangements for these workflows. Provider processing still happens, so the subprocessor list is part of the privacy decision.

Policies and provider arrangements can change. Before a sensitive check-in, review the current privacy policy and subprocessor page, not a screenshot or an old app-store description. Look for the date of the policy and the exact handling of audio, transcripts, extracted fields, and generated summaries.

Do a one-minute privacy check

Decide what level of detail you want to say aloud before you open the microphone. You can use names, initials, roles, or no identifying label at all. The useful boundary is the one you can remember while speaking naturally, not a complicated rule you will abandon halfway through.

After the check-in, inspect the saved result. If the wording is off, regard it as a draft to inspect, even though it came from your own voice. Note the mismatch and ask Daylogue Support at /support whether a current correction control exists. Ask separately about export and deletion scope.

1. 1

   Choose your detail level

   Decide whether names, workplaces, locations, or other identifying details are necessary for this reflection.

2. 2

   Speak in your own words

   Describe the day directly without trying to sound polished or supply a label you do not believe.

3. 3

   Inspect what was saved

   Review the resulting fields or summary and note anything missing, overstated, or attached to the wrong context.

4. 4

   Confirm your controls

   Check the current export and deletion instructions before assuming the audio, transcript, fields, and summaries share one rule.