title: Memory System Only Absorbed 20% of Agent Outputs

insight: The Genesis memory systems have only absorbed ~20% of generated content - 850+ files of production-ready marketing assets worth $50K+ are sitting unused while only strategic decisions and architectural patterns were CTM'd.

reasoning_chain: Agent audit found 42MB of agent outputs across 6 sessions

reasoning_chain: Session 22 alone has 502 domain landing pages (28MB) never deployed

reasoning_chain: Session 17/23/24 have 350+ marketing assets never absorbed

reasoning_chain: RLM agents (9,114 lines of code) built but zero integration

reasoning_chain: Memory systems absorbed strategic patterns but NOT production content

source_context: Genesis audit report: 'Only ~20% of agent outputs have been absorbed into memory. 850+ files of production-ready content sitting unused'


title: The Wedge Strategy - Audit Leads Before Selling

insight: Run free diagnostic audits on agency leads' OWN websites first, then email them their low scores - this manufactures undeniable demand by turning the diagnostic tool on the diagnosticians themselves.

reasoning_chain: B2B SaaS Wedge Strategy doc extracted (48,530 tokens)

reasoning_chain: Strategy: Ship Radar Audit FIRST to manufacture demand

reasoning_chain: Hand free audit tool to 38 agencies as lead magnet

reasoning_chain: Audit exposes brutal truth: 'You missed 40% of calls = $8,400 lost'

reasoning_chain: Audit then prescribes: Deploy Talking Widget + Browser Agents

reasoning_chain: Unit economics: $0.78/audit × 500 = $390 cost, 10 conversions × $500 = $5,000 revenue (1,182% ROI)

source_context: From B2B_SAAS_WEDGE_STRATEGY_COMPLETE.md: 'Run audits on the 38 agency leads' OWN websites first. Email them: Ironically, YOU scored 52/100... — turns the diagnostic tool on the diagnosticians'


title: Deep Think API Bridge - No More Copy-Paste

insight: Built a Python bridge script that connects Gemini Deep Think directly to Genesis via API, eliminating manual copy-paste and enabling programmatic batch execution.

reasoning_chain: Kinan asked: 'work out how can we most quickly solve browser use so we can connect deep think api and genesis directly?'

reasoning_chain: Research found Gemini 2.5 Pro/Flash have thinking mode available via API with thinkingBudget parameter

reasoning_chain: Built deep_think_bridge.py with 3 modes: API, CLI, Browser

reasoning_chain: Auto-detects API key, saves to JSON+Markdown, supports --ctm flag for memory commit

reasoning_chain: Cost: $0.01-0.03/query Flash, $0.05-0.20/query Pro - within $200/mo budget

source_context: Agent 'Solve Gemini Deep Think API connection' completed - built scripts/deep_think_bridge.py (500+ lines) with API mode, CLI mode, auto-save, --ctm flag


title: Manus + SimilarWeb = Radar Audit Data Pipeline

insight: The Bunker FNQ audit that scored George 52/100 used Manus + SimilarWeb as data sources - this becomes the automated pipeline for Radar Audit.

reasoning_chain: George's Bunker FNQ audit achieved 52/100 score

reasoning_chain: Data came from Manus + SimilarWeb (not internal scraping)

reasoning_chain: SimilarWeb provides traffic data, engagement metrics, competitor benchmarks

reasoning_chain: Manus already integrates SimilarWeb - free data via existing agent

reasoning_chain: Pipeline: SimilarWeb (via Manus) → Playwright (screenshots) → Gemini Flash (analysis) → Audit Report

source_context: Genesis: 'That means our Radar Audit pipeline should pull from: SimilarWeb (via Manus or API) → traffic data, engagement metrics, competitor benchmarks'


title: Burn Rate Correction Changes Break-Even Math

insight: Correcting burn rate from $385 to $1,500/mo doubled the customers needed to break even from 5 to 11, increasing revenue urgency.

reasoning_chain: Actual burn: ~$1,500 USD/mo ($2,400 AUD)

reasoning_chain: Break-even: 11 customers needed (29% conversion of 38 leads)

reasoning_chain: OLD: 5 customers (13% conversion) - too optimistic

reasoning_chain: NEW: Revenue urgency is HIGHER than previously calculated

reasoning_chain: Reframe still works: '30x more efficient than typical $50K/mo startup'

source_context: Agent 'CTM corrected burn rate everywhere' completed - 'Break-even went from 5 customers to 11 customers needed (29% conversion of 38 leads). Revenue urgency is higher than we thought'


title: DT #5 - Memory as Vendor Lock-In

insight: Sunaiva Memory MCP should be designed as the 'brain they can't unplug' - once business data is in the system, switching feels like a lobotomy because every AI tool depends on shared memory.

reasoning_chain: Memory MCP uses PostgreSQL (entities), Qdrant (embeddings), Redis (cache), FastMCP protocol

reasoning_chain: 5 products feed into memory layer: Talking Widget, Browser Agents, Radar Audit, Marketplace

reasoning_chain: Data gravity: raw exports become irreplaceable processed intelligence (derived entities, relationship graphs)

reasoning_chain: Progressive deepening: customer starts with one export, 6 months later has CRM, calls, emails, SOPs, browser agents writing new memories

reasoning_chain: At some point switching cost exceeds annual subscription - churn becomes economically irrational

source_context: DT #5 prompt: 'Design the technical and psychological architecture that makes leaving Sunaiva feel like a lobotomy'


title: DT #6 - Agent Marketplace as iOS for AI Workers

insight: Build an Agent Marketplace where vertical workflow agents are monthly subscriptions - combine voice + browser + memory into a single platform that competitors (Zapier) can't replicate.

reasoning_chain: Marketplace connects to Memory MCP (moat), Talking Widget (voice triggers), Browser Agents (execution), Radar Audit (demand creation)

reasoning_chain: 73 domains become programmatic storefronts for vertical agents

reasoning_chain: Cold start solved: own preset agents ARE supply, Radar Audit funnels CREATE demand

reasoning_chain: Network effects: shared learnings across agents, cross-agent workflows, marketplace intelligence

reasoning_chain: Agency channel: 38 partners are simultaneously customers, sellers, and distributors

source_context: DT #6 prompt: 'Design the revenue model, quality control, network effects, and 3-year evolution roadmap for the Agent Marketplace'


title: RLM Agents Built But Not Integrated

insight: 5 Python modules (9,114 lines, 309KB) for preference learning, reward models, PPO, DPO, constitutional AI exist but have ZERO integration with AIVA or any product.

reasoning_chain: Session 9 memory audit found 'RLM workers built but zero integration'

reasoning_chain: 5 modules in /AIVA/queen_outputs/rlm/: rlm_01-05

reasoning_chain: These are sophisticated RLHF components but disconnected

reasoning_chain: Gap: code exists, connections don't - needs integration task

source_context: Agent audit: 'RLM AGENTS (309KB CODE) - Status: BUILT but ZERO INTEGRATION - 5 modules totaling 9,114 lines of Python'


type: DIRECTIVE

title: Kinan Requests Deep Think 5 and 6 Plus Browser Solution

insight: Kinan explicitly requested: (1) Deep Think outputs 5 and 6, (2) solution for browser use to connect Deep Think API directly to Genesis.

reasoning_chain: Kinan: 'yes and first give me deep think 5 and 6 and also work out how can we most quickly solve browser use so we can connect deep think api and genesis directly?'

reasoning_chain: This is a clear directive with two parts

reasoning_chain: Part 1: Deliver DT #5 (Memory MCP lock-in) and DT #6 (Agent Marketplace)

reasoning_chain: Part 2: Solve browser automation for API connection

source_context: Kinan exact quote: 'yes and first give me deep think 5 and 6 and also work out how can we most quickly solve browser use so we can connect deep think api and genesis directly?'