Engramme is an AI‑powered personal memory platform that continuously captures and indexes a user's emails, calendar events, messaging logs, and location data into a unified latent memory graph. It provides context‑driven, searchless recall of relevant information through web and mobile interfaces, with end‑to‑end encryption and optional API integration for third‑party apps. The service is offered on a subscription basis.
Funding
Funding not disclosed
Founders
Product
Problem
Individuals frequently encounter situations where they need to recall personal information—such as names, contacts, recent conversations, or locations—but lack an efficient way to retrieve these “dark matter” memories. Traditional note‑taking or calendar tools require explicit indexing, and searching across disparate sources is time‑consuming, leading to productivity loss and missed opportunities.
Solution
Engramme delivers an AI‑driven personal memory platform that continuously captures and indexes a user’s digital life events, contacts, conversations, and geospatial data. Novel neural memory models—built on sparse representations and replay‑based continual learning—encode these inputs into a unified latent space, enabling instant, searchless recall through context cues. The service runs as a subscription, providing a web dashboard and mobile client that surface relevant memories on demand without requiring explicit queries. All data are encrypted end‑to‑end and stored in a HIPAA‑compliant cloud, with role‑based access controls to protect user privacy. The platform also offers an API for integration with email, calendar, and messaging apps, allowing seamless ingestion of new information.
Target Audience
The primary customers are knowledge workers, professionals, and executives who rely on accurate recall of personal and work‑related information throughout the day. It also serves individuals who manage complex personal schedules and networks of contacts.
Features
- Continuous ingestion pipeline that syncs emails, calendar events, messaging logs, and GPS traces into a unified memory graph.
- Sparse‑embedding neural encoder that compresses raw inputs into high‑dimensional vectors, preserving semantic relationships while minimizing storage overhead.
- Replay‑based continual learning engine that updates the memory model without catastrophic forgetting, ensuring recent events are prioritized without losing older context.
- Context‑driven retrieval interface that surfaces the most relevant memory fragment based on the current activity or conversational cue, eliminating the need for keyword search.
- End‑to‑end AES‑256 encryption with zero‑knowledge architecture; user keys never leave the client device.
- Cross‑platform synchronization (web, iOS, Android) with real‑time conflict resolution.
- Developer‑focused RESTful API and SDKs for Python and JavaScript, enabling third‑party apps to push data into the memory store or query retrieved snippets.