Semantic Memory Graph: Your AI Now Remembers and Learns
Ask a general-purpose AI tool a question today, then ask the same question tomorrow β it starts from scratch. It doesn't know your company; it has no context. 361's Semantic Memory Graph solves exactly that.
6 Memory Types β A Complete Institutional Memory
| Memory Type | What It Does | Example |
|---|---|---|
| Conversation Memory | Holds the context of the current dialogue | "The customer I just asked about..." |
| Long-Term Memory | Stores lasting learnings | "This customer increases orders in Q1" |
| Entity Memory | Knowledge attached to business objects | "Customer X has 3 open complaints" |
| User Memory | Personal preferences and history | "This user prefers chart format" |
| Vector Memory | Semantic search | "What did we do in a similar situation?" |
| Semantic Graph | Network of entities + relationships + facts | "Customer A buys from Supplier B" |
How Does It Work?
It automatically extracts knowledge from every conversation, every transaction, every decision:
- Entity Extraction: "Customer X", "Product Y", "Supplier Z" β it recognizes business objects
- Relationship Detection: "X buys product Y from Z" β it builds the connections
- Fact Recording: "A 15% discount was applied on the last order" β it stores the facts
- Temporal Decay: Older information gradually fades while current information rises to the top
The result: with every conversation, the AI becomes smarter, more contextual, more accurate. Institutional memory is never lost.
Real-World Impact
When a sales manager says "Summarize where we stand with Customer X", the AI already knows:
- Summaries of the last 3 meetings
- Open quotes and their amounts
- The customer's preferred communication channel
- Past complaints and how they were resolved
- The purchasing cycle and the estimated timing of the next order
All of it learned automatically β nobody entered it by hand.