We built enterprise memory infrastructure for AI systems. Not a wrapper around a vector database. Five purpose-built memory systems that work together:

  - CASCADE – 6-layer temporal memory with natural decay. Memories fade unless they matter. Sub-millisecond access via RAM disk.
  - PyTorch Memory – GPU-accelerated semantic search. 2,500+ vectors at <2ms on consumer NVIDIA hardware.
  - Hebbian Mind – Associative graph where edges strengthen through co-activation. No manual linking. The more you use it, the smarter it gets.
  - Soul Matrix – Hebbian conductance graph in Rust. Pre-retrieval gating at ~270 microseconds. Shapes what surfaces before your LLM spends tokens.
  - CMM – Unified search across all backends. One query, synthesized results, not concatenated.

  Everything runs on MCP (Model Context Protocol). Works with Claude, or any MCP client. Docker, Linux native, or Windows native install paths.

  No subscriptions. No per-query fees. No metered API. One purchase, you own it, run it on your hardware forever. Source code included.

  We built this because we needed it. AI agents fail in production because they have no memory architecture. The industry spent $40B on AI in 2025 and 95% saw no
  production ROI. The gap between demo and production is memory management – agents that can't remember yesterday can't improve tomorrow.

  Free tier available: cascade-memory-lite on GitHub (MIT). Enterprise products start at $400. Full stack is $1,500. 90-day money-back guarantee on everything.