Video is the superset of all multimedia
If you solve memory for video, you’ve solved it for everything else. Video contains:- Spoken language (audio/transcripts)
- Visual information (images, scenes, motion)
- Text (captions, on-screen text, slides)
- Temporal dynamics (how information unfolds over time)
How does it work?
Connect your data sources
Link Google Drive, Gmail, Notion, S3, Box, OneDrive, or custom sources. Mem[v] automatically handles extraction.
Automatic memory creation
Mem[v] extracts structured memories - facts, preferences, entities - and builds knowledge graphs that show how everything connects.
- Extract entities, relationships, and facts from all content types
- Build graphs linking related information
- Create user profiles with preferences and behavioral patterns
- Update memories in real-time as new information arrives
Why it matters?
Your users interact with information everywhere - emails, documents, Slack threads, video calls, tutorials, presentations. But AI agents can’t remember any of it beyond the current session. Without long-term memory:- Every conversation starts from zero
- Users repeat themselves constantly
- Context from last week is lost
- Information across different formats stays disconnected
- AI hallucinates due to lack of grounded facts
What you can do with Mem[v]?
Connect any data source
Integrate Google Drive, Gmail, Notion, S3, Box, OneDrive. Automatic extraction for all formats.
Persistent memories
Real-time, evolving memories that update as users interact with new content.
Multimodal understanding
Unified memory connecting text, conversations, files, images, videos, and audio.
Knowledge graphs
Build semantic graphs showing how people, topics, and events relate across all content.
Next steps
How it works
Understand how Mem[v] handles context engineering and creates memories for your apps and multimodal agents.