RLM Neo-Cortex System — Overall Build

type: entity

description: Complete RLM Neo-Cortex system built in a single session via parallel-builder swarm. 52 atomic stories across 7 modules (M0-M6), 364/364 tests passing, 5,450 lines of production code. Architecture: Contracts → Gateway → Entitlement → Surprise → Decay → Feedback → Partitioning. All modules on E: drive, all storage on Elestio PostgreSQL/Qdrant/Redis. Zero SQLite. PRD: plans/RLM_NEO_CORTEX_PRD.md (2,939 lines, 52 stories). Product brief: plans/RLM_PRODUCT_BRIEF.md (576 lines).


RLM Module M0 — Contracts

description: Canonical contracts module. Single source of truth for all shared interfaces and data models used across all 7 RLM modules. Written FIRST and marked read-only before other modules were built. Prevents contract drift when multiple parallel agents write to the same interfaces. Location: core/rlm/contracts.py. All other modules import from this file — no module defines its own type signatures.


RLM Module M1 — Gateway

description: RLM Gateway module. Entry point for all memory read/write operations across the Neo-Cortex system. Routes requests to appropriate sub-systems (Entitlement, Surprise, Decay, Feedback, Partitioning). Enforces authentication and authorization before any memory access. Location: core/rlm/gateway.py. Part of 52-story PRD build, all tests passing.


RLM Module M2 — Entitlement

description: RLM Entitlement module. Controls which memory partitions each customer tier can access. Enforces the SubAIVA tier ladder: Starter ($497) → Professional ($997) → Enterprise ($1,497) → Queen ($20K+). Each tier unlocks additional memory capabilities. Prevents cross-tenant data leakage. Location: core/rlm/entitlement.py. Part of 52-story PRD build.


RLM Module M3 — Surprise

description: RLM Surprise engine. Computes surprise scores (0.0–1.0) for incoming memory events. High-surprise events (>= 0.50) are flagged for Titan Memory extraction and RLM worker processing. Replaces stub implementation in core/surprise_memory.py which previously returned placeholder values. Location: core/rlm/surprise.py. Addresses failure axiom fe_008 (stub surprise scores).


RLM Module M4 — Decay

description: RLM Decay module. Implements temporal memory decay curves for the living bloodstream. Older memories lose relevance weight over time unless reinforced. Prevents memory bloat and keeps active context focused on recent, high-relevance information. Configurable decay rates per memory tier. Location: core/rlm/decay.py. Part of 52-story PRD build.


RLM Module M5 — Feedback

description: RLM Feedback module. Captures preference pairs (correct action vs wrong action) for constitutional AI learning. Writes pl_preference_pairs to Elestio PostgreSQL. Addresses failure axiom fe_006 (pl_preference_pairs table had 0 rows despite 38 interactions logged). Feeds RLM workers for preference learning. Location: core/rlm/feedback.py.


RLM Module M6 — Partitioning

description: RLM Partitioning module. Implements cryptographic memory isolation between SubAIVA customer tenants. Each customer's memory partition is keyed separately — no cross-tenant reads possible at storage layer. Queen memory is fully isolated and never flows down to SubAIVAs. Supports the AIVA product hierarchy: Queen (private) → Tier 3 → Tier 2 → Tier 1. Location: core/rlm/partitioning.py.


BMAD v6 Installation

description: BMAD (Build Multi-Agent Docs) v6 installed into the Genesis system. 223 files. Open-source AI coding framework that enforces atomic PRD doctrine. Provides structured agent roles, story templates, and wave execution patterns. Directly aligned with Genesis Rule 17 (PRD Atomic Story Doctrine). Used as the framework for generating the RLM Neo-Cortex PRD (52 stories, 2,939 lines). Project context generated: 426 lines, 89 rules. Enables reproducible multi-agent sprint execution.


name: Security Hardening — 10 Secrets Stripped

description: Pre-deploy security hardening pass. 10 hardcoded secrets stripped from source code and replaced with environment variable references. Secrets included API keys, database credentials, and service tokens that were embedded directly in Python/JS files. Applied before any public deployment. Addresses the principle: security hardening must precede public deploy, not follow it. All secrets now loaded from config/secrets.env or Infisical vault (M1 from Genesis Stack Completion Mega PRD).


Sunaiva Memory MCP Consolidation

description: Three disconnected Sunaiva memory MCP servers unified into one canonical server. Previous state: 3 separate servers with overlapping functionality, inconsistent embedding dimensions, and no shared schema. New state: single canonical MCP server with 32/32 tests passing, standardised 768-dim embeddings (nomic-embed-text via Ollama), unified schema, single source of truth. Prevents the embedding dimension mismatch failures seen previously. Location: Sunaiva/memory_mcp/. Replaces prior implementations at mcp-servers/genesis-mem0/ and scattered Sunaiva/ paths.


name: TalkingWidget.ai Branding and Link Fixes

description: 17 files updated in TalkingWidget.ai site. Issues fixed: outdated branding, broken internal links, demo pages with placeholder content, missing navigation links. Ensures the public-facing product site accurately represents current product state before any outbound campaigns drive traffic. Covers: index pages, demo pages, pricing pages, navigation. Part of pre-launch quality pass.


RLM Product Brief and PRD

category: documentation

description: Two key documents produced for RLM Neo-Cortex. (1) Product brief: 576 lines covering product positioning, customer value, pricing tiers, and competitive differentiation. (2) Full PRD: 2,939 lines covering 52 atomic stories across 7 modules, complete with code skeletons, BB/WB test plans, iteration budgets, wave execution plan, and component interface contracts. PRD follows Rule 17 (PRD Atomic Story Doctrine) and BMAD v6 patterns. These documents served as the execution blueprint for the parallel-builder sprint.