Aiming to capture fleeting thoughts, with your review.
Keep the thought before it disappears.
Analog Memory is a planned wearable brain-imaging hat and AI software layer that aims to help preserve meaningful experiences, then make them searchable with your review.
Early product direction. Useful outcomes first; literal thought transcription and total recall are not claims.
Signal / a moment
A fleeting thought
Suggested record
A useful signal about an experience, ready for you to inspect and correct.
Your review is the boundary.
01
Capture
02
Infer
03
Review
The interface is designed around a sequence: signal, inference, review. The person stays in control of what becomes memory.
The customer workflow
Less interruption. More to return to.
Conventional memory tools are useful, but they ask you to stop and intentionally capture what is happening. Analog Memory explores a different starting point: collect signals in the background, then let you inspect what the system inferred.
Capture
A moment happens
The hat is designed to collect neural signals while you stay with the experience instead of opening a notes or voice-memo app.
Infer
A pattern is surfaced
Personalized models attempt to connect those signals to a useful record of what you perceived, thought about, or remembered.
Review
You decide what stays
Every result is for your review and correction. Your feedback is part of how a personal memory layer can become more helpful over time.
The shift we are designing for
A memory layer that stays yours.
Today’s tools
You remember to record.
Notes, photos, voice memos, and assistants depend on deliberate capture or what can be observed from the outside.
Analog Memory explores
The system learns with you.
Personalized models aim to surface useful neural signals, while your review and correction define what is meaningful.
Before the promise
Scientific honesty is part of the interface.
Consent
You choose what is captured, reviewed, corrected, and kept.
Privacy
Intimate neural data should stay under the person’s control.
Uncertainty
We separate what has been demonstrated from what still needs validation.