Permanent Memory Infrastructure

Your AI does not need more context. It needs continuity.

LeapMemory is the permanent memory layer for AI applications. Verbatim retention. Entity graph traversal. Physical tenant isolation by architecture.

Retention horizon. No TTL, ever.
0
Shared vector pools. Physical isolation by design.
4
Retrieval signals fuse in every recall.
// Traditional memory systems
× Shared vector pools.
× TTL on memories.
× Chunked text retrieval.
× Reconstructed at runtime.
× Filtered by metadata.
// LeapMemory permanent cortex
Verbatim, append-only turns.
Multi-signal retrieval.
Graph walks over entities.
One tenant per memory brain.
Physically isolated by design.
The three claims no other memory API can make

Permanent. Post-RAG. Beyond Vectors.

Three pillars. Each one is the kind of guarantee that requires a different architecture, not a config flag.

01 / PERMANENT

Memories never expire.

No TTL. No summarization. No context-window eviction. No per-user caps. Every turn from every conversation is permanently retrievable, exactly as it was spoken.

"Your assistant remembers every user's first conversation, three years from now."
02 / POST-RAG

Turns, not chunks.

RAG was built for documents. Conversation is not a document. LeapMemory stores verbatim turns and fans retrieval across four signals: lexical, semantic, graph traversal, and entity resolution.

"RAG retrieves text. LeapMemory remembers people."
03 / BEYOND VECTORS

Meaning is a graph.

Vectors find similar text. LeapMemory finds connected meaning. Every entity becomes a node. Every relationship becomes an edge. Recall walks the graph, not just embedding space.

"Your user says 'the dog.' We know it is Karma."
Live walkthrough

Watch a memory form.

Pick a fact, ingest it, then ask a question. Every step shows the four stores filling in real time and the retrieval signals that fire.

1Pick a memory
2Four stores, four signals
Canonical
verbatim turns
Graph
entity relationships
Semantic
embeddings + anchors
Resolver
entity resolver
3Ask a question
Retrieved with provenance
Built for

Systems that cannot afford to forget.

Anywhere identity, context, or cross-conversation continuity matters more than chunked text retrieval.

AI Companions

Permanent identity. Long-term continuity. Year-three recall of a user's first conversation.

Trading Systems

Regime shifts. Execution ancestry. Behavioral loops. Temporal causality across millions of ticks.

Enterprise AI

Physical tenant isolation. Auditable retrieval boundaries. One-command deletion for compliance.

Memory Platforms

Replace fragile RAG pipelines. Drop in a real semantic infrastructure with provenance.

Under the hood

Four stores. One semantic cortex.

Each store preserves a different aspect of a conversation. Together they form a real memory layer, not a retrieval wrapper.

Canonical

The Words

Verbatim source of truth

Every turn, append-only, exactly as it was said. No chunking. No summarization. No edits. The canonical record of what actually happened.

Graph

The Relationships

Entity knowledge graph

Nodes for people, places, pets, products. Edges for owns, lives-in, prefers, derived-from. Graph walks make recall associative, not just similar.

Semantic

The Meaning

Embeddings + anchor entities

Multilingual embeddings stored alongside the entities each turn anchors to. Lexical and semantic signals fire in the same query. Tenant-scoped by design.

Resolver

The Recognition

Entity resolution cache

The instant lookup that turns "Karma" into the right entity id across languages and time. Hot path for graph traversal. Tenant-isolated keyspace.

Early access

AI is evolving beyond RAG into permanent memory.

LeapMemory gives AI systems a permanent semantic cortex backed by physically isolated infrastructure. The memories persist. The relationships compound. The graph becomes cognition.