Memno

Product Introduction

1. Memno is an AI-powered personal intelligence assistant designed to unify and manage scattered digital information across photos, documents, voice notes, and calendars. It enables users to retrieve specific details, automate tasks, and coordinate actions through natural language queries.
2. The core value of Memno lies in its ability to create a personalized, context-aware knowledge graph that integrates fragmented data sources while maintaining strict privacy controls. It transforms isolated information into actionable intelligence, reducing cognitive load and streamlining workflows.

Main Features

1. Cross-Platform Memory Integration: Memno aggregates data from calendars, cloud storage, messaging apps, and device sensors to build a unified searchable knowledge base. It uses NLP to index voice memos, scan document clauses, and extract metadata from images for instant recall.
2. Context-Aware Task Automation: The system automatically schedules reminders, reschedules meetings based on calendar conflicts, and books reservations by analyzing location data, time zones, and user preferences. It executes actions via API integrations with services like Google Calendar, Slack, and restaurant booking platforms.
3. Multilingual Communication Interface: Memno handles cross-language interactions by translating requests in real time, enabling tasks like booking international reservations or summarizing multilingual emails. It supports voice commands, text inputs, and automated outbound calls through a TTS (text-to-speech) engine.

Problems Solved

1. Fragmented Information Management: Memno addresses the inefficiency of switching between disconnected apps to locate files, messages, or meeting details. It eliminates manual searches by correlating data across 15+ app integrations using vectorized embeddings.
2. Target User Group: Professionals managing high-volume workflows across documents, communications, and scheduling tools—particularly remote teams, executives, and knowledge workers handling multilingual or time-sensitive tasks.
3. Typical Use Cases: Retrieving contract clauses from archived PDFs, reconciling calendar conflicts during international travel, converting voice-note ideas into actionable meeting agendas, and automating restaurant bookings via voice command during commute times.

Unique Advantages

1. Architectural Privacy Model: Unlike cloud-based AI assistants, Memno operates in isolated, encrypted environments where user data never trains public models. Each user’s “knowledge graph” resides in a dedicated instance with zero cross-user data mingling.
2. Distributed Request Engine: Tasks are processed through compartmentalized microservices, ensuring no single provider (e.g., OpenAI, Google) accesses full context. For example, calendar data is processed separately from document analysis to prevent third-party data correlation.
3. Adaptive Context Stacking: Memno’s algorithms weight recent interactions, habitual patterns, and project-specific context to prioritize responses. It dynamically adjusts reminders based on real-time location (e.g., triggering a call reminder when the user enters their car near 2 PM).

Frequently Asked Questions (FAQ)

1. How does Memno ensure data privacy? Memno uses AES-256 encryption for stored data and TLS 1.3 for transmissions, with all processing occurring in isolated containers. User data is never used to train public AI models, and the distributed engine prevents third-party providers from reconstructing full user profiles.
2. Can Memno integrate with my existing tools? Yes, Memno supports API integrations with Google Workspace, Microsoft 365, Slack, Dropbox, and 12 other platforms. It uses OAuth 2.0 for secure authentication and maintains read/write permissions only for user-authorized actions like calendar updates or file retrieval.
3. How does Memno handle multilingual tasks? The system employs a hybrid NLP model combining transformer-based translation (e.g., mBART-50) with locale-specific intent recognition. For restaurant bookings, it detects language preferences from past interactions and adjusts its TTS dialect accordingly during outbound calls.