supermemory — AI Memory Engine

Semantic memory storage captures customer facts and market signals, then uses vector-backed search to retrieve relevant items for future turns. This prevents repeated questions and forgotten context during multi-channel SDR workflows by injecting recalled insights back into the conversation.

To add a new customer insight to your sales memory store, use the memory:add command followed by the text string of the insight. This saves the extracted customer fact or conversation detail into a local vector-backed directory for persistent storage and future semantic recall.

Yes, you can set automatic expiration for time-sensitive market signals using typed TTL rules. This lifecycle control allows you to keep permanent customer facts while ensuring time-sensitive insights expire automatically, keeping your sales context clean and relevant.

Yes, this semantic memory engine is designed for multi-channel SDR workflows. It captures effective scripts and customer preferences across channels, then retrieves the most relevant items to improve relevance and continuity during future outreach turns.

Using a local vector-backed directory for sales context limits accessibility to a single local environment rather than a distributed cloud setup. Memory operations rely on command-driven add, search, list, forget, and stats functions without native external API synchronization.