title: Maya Memory Already Built - Just Needs Enabling

insight: Maya's 3-tier memory architecture (PostgreSQL, Qdrant, Redis) is fully implemented but disabled; enabling the webhook instantly activates memory injection without new development.

reasoning_chain: Kinan asked to integrate Genesis memory into Maya for personal assistant use

reasoning_chain: Genesis explored the codebase and found complete memory infrastructure already exists

reasoning_chain: The only blocker is a disabled VAPI webhook - not missing code

reasoning_chain: This transforms the task from 'build' to 'enable' - a 1-2 hour quick win

source_context: The blocker: Maya's webhook is disabled for debugging. Enable it and memory injection works immediately.


title: Genesis Memory Achieves Sub-100ms Latency Natively

insight: The existing Genesis memory system already meets enterprise-grade speed requirements: Redis L1 <5ms, Qdrant L2 50-100ms, PostgreSQL L3 20-50ms, with predictive pre-loading achieving 2.3x speedup.

reasoning_chain: Kinan required enterprise grade memory plus lightning speed with no extra latency

reasoning_chain: Genesis measured actual latency across all three memory tiers

reasoning_chain: Found L1 Redis at <5ms, L2 Qdrant at 50-100ms, L3 PostgreSQL at 20-50ms

reasoning_chain: Predictive pre-loading already implemented reducing retrieval from 800ms to 350ms

reasoning_chain: Conclusion: No architectural changes needed for latency requirements

source_context: Latency Targets Already Achieved: L1 Working Redis <5ms, L2 Semantic Qdrant 50-100ms, L3 Audit PostgreSQL 20-50ms


title: Sub-AIVA is Green Field - No Existing Work

insight: The concept of sub-AIVA private memory agents per customer does not exist in the codebase - this is a new architectural direction requiring full PRD development rather than extension of existing systems.

reasoning_chain: Kinan asked to explore sub-AIVA private memory agents for each customer

reasoning_chain: Genesis searched for existing implementations - found none

reasoning_chain: No per-customer agent spawning, no private memory namespace per customer, no multi-tenant autonomy levels

reasoning_chain: This shifts the approach from 'extend existing' to 'design from scratch'

source_context: FINDING 3: Sub-AIVA Framework Does NOT Exist - What's Missing: No sub-AIVA agent spawning per customer, no private memory namespace per customer, no multi-tenant autonomy levels


title: Three-Track Decision Tree for Memory Integration

insight: Genesis synthesized the exploration into three mutually exclusive paths: enable existing Maya memory (1-2 hours), add personal assistant mode (4-6 hours), or design Sub-AIVA PRD (research phase).

reasoning_chain: Kinan requested exploration of two concepts: Maya+Genesis memory and Sub-AIVA

reasoning_chain: Genesis completed parallel exploration of both

reasoning_chain: Found Maya memory ready to enable, Sub-AIVA non-existent

reasoning_chain: Synthesized findings into three actionable tracks with different effort levels

reasoning_chain: Presented options back to Kinan for decision

source_context: Next Steps (Your Decision): Option A: Enable Maya Memory NOW (1-2 hours), Option B: Add Personal Assistant Mode (4-6 hours), Option C: Design Sub-AIVA PRD (Research phase)


title: Maya Webhook Disabled Blocks Memory Integration

insight: The VAPI webhook in Maya's configuration is set to None, preventing memory injection - this is the single point of failure preventing memory functionality.

reasoning_chain: Goal: Get Maya demo agent to use Genesis memory system

reasoning_chain: Investigation found all memory components deployed and ready

reasoning_chain: Discovered VAPI webhook is disabled (serverUrl: None) in Maya's config

reasoning_chain: This disabled webhook is the only thing preventing memory from working

reasoning_chain: Fix: Re-enable webhook to activate existing memory infrastructure

source_context: VAPI Webhook: ❌ DISABLED - serverUrl: None in Maya's config


title: Admin Code Detection Enables Dual-Mode Maya

insight: Maya's existing admin code detection (Kinan + 1590) can serve as the switch between Demo Mode and Personal Assistant Mode, leveraging existing authentication for access control.

reasoning_chain: Need to make Maya work as both demo agent and personal assistant

reasoning_chain: Exploration found Maya already has admin detection for Kinan

reasoning_chain: Realized this same mechanism can distinguish between modes

reasoning_chain: Demo Mode: Uses Maya's default 5-step qualification

reasoning_chain: Personal Assistant Mode: Grants full Genesis Memory access + AIVA task dispatch

reasoning_chain: No new authentication needed - reuse existing admin code

source_context: Mode Detection (via caller_phone or admin code): Demo Mode (Default) vs Personal Asst (Kinan only) - SHARED LAYER: Genesis Memory System


title: Zero Extra Latency Strategy for Multi-Tenant Memory

insight: Sub-AIVA can achieve zero added latency through tenant ID in JWT (no extra lookup), Redis key prefix isolation (same speed), Qdrant collection filtering (+2-5ms), and predictive pre-load (-300ms).

reasoning_chain: Requirement: Enterprise grade memory with lightning speed - no extra latency

reasoning_chain: For multi-tenant Sub-AIVA, need isolation per customer

reasoning_chain: Analyzed latency impact of each isolation mechanism

reasoning_chain: Predictive pre-load: -300ms (net improvement)

reasoning_chain: Conclusion: Net result same or faster than current single-tenant

source_context: Net Result: Same or faster than current single-tenant - Technique: Tenant ID in JWT (+0ms), Redis Key Prefix (+0ms), Qdrant Collection Filter (+2-5ms), PostgreSQL RLS (+1-3ms), Predictive Pre-Load (-300ms)


type: DIRECTIVE

title: Explore Without Stepping on Other Agents

insight: Kinan explicitly instructed Genesis to investigate memory integration approaches while ensuring no collision with other agents actively working on similar problems.

reasoning_chain: Kinan gave directive: concurrently explore Maya memory integration AND sub-AIVA private memory agents

reasoning_chain: Added constraint: investigate whilst not stepping on any agents actively looking into this too

reasoning_chain: Genesis searched for conflicting work and found none

reasoning_chain: Confirmed green field status - safe to proceed with design work

reasoning_chain: This directive ensures resource efficiency and avoids duplicate effort

source_context: concurrently explore how we can get maya demo agent to have genesis memory system integrated into her... investigate ways to achieve this whilst not stepping on any agents actively looking into this too


title: Maya Business Isolation vs Customer Isolation Gap

insight: Maya currently provides business-level memory isolation (for ReceptionistAI clients) but lacks customer-level isolation within each business - the Sub-AIVA concept would fill this gap.

reasoning_chain: Exploring sub-AIVA private memory agents for each customer

reasoning_chain: Found Maya's closest implementation is business-level isolation

reasoning_chain: This serves ReceptionistAI clients, not their end customers

reasoning_chain: Gap identified: no per-customer memory namespace within a business

reasoning_chain: Sub-AIVA would create tenant-specific tables, collections, and Redis keys

reasoning_chain: Requires Row-Level Security, collection-per-tenant, and JWT tenant validation

source_context: Closest Implementation: Maya's business-level isolation - but that's ReceptionistAI clients, not their customers


type: METAPHOR

title: Memory Architecture as Three-Tier Cake

insight: Genesis Memory uses a three-tier cake metaphor: Redis as the fast working memory layer (<5ms), Qdrant as the semantic understanding layer (50-100ms), and PostgreSQL as the audit/persistent layer (20-50ms).

reasoning_chain: Need to explain Genesis Memory architecture to Kinan

reasoning_chain: Used cake/tier metaphor to visualize three distinct layers

reasoning_chain: Each tier has different speed and purpose

reasoning_chain: Semantic (L2) = vector search for meaning

reasoning_chain: Audit (L3) = persistent storage for compliance

reasoning_chain: This visualization helps understand why sub-100ms is achievable

source_context: L1 Working: Redis <5ms, L2 Semantic: Qdrant 50-100ms, L3 Audit: PostgreSQL 20-50ms