supermemory — AI Memory Engine

What problem does it solve?

You lose valuable customer context over time, causing your SDR to repeat questions, miss preferences, and weaken follow-ups.

Core Features & Use Cases

• Semantic Memory Storage: Store extracted customer facts, conversation insights, market signals, and effective scripts to build long-lived sales context.
• Semantic Recall for Better Context: Retrieve relevant memories by searching, then inject them back into the conversation workflow to improve relevance and continuity.
• Lifecycle Controls with TTL: Keep some memories permanent while expiring time-sensitive insights (like market signals) automatically.

Use Case: After reviewing multiple calls with a lead, capture their stated pricing sensitivity and preferred product bundle, then recall those details during the next outreach to increase reply rates.

Quick Start

Ask an AI agent to add a new insight to your memory by running the command: memory:add "Interested in bulk pricing for Model X and prefers WhatsApp follow-ups".