## Executive Summary A 3-person autonomous team (Jim, Dave, Arash) building the foundational AI infrastructure for company-wide intelligence augmentation. First year focus: create a unified knowledge graph that captures all institutional knowledge, with AI as the interface layer. ## Mission Transform our company into an AI-native organization by building tools that build themselves - starting with a comprehensive knowledge graph that grows through use. ## Core Innovation: The Graph Is Everything - **Single unified knowledge graph** containing all company processes, skills, relationships, and decisions - **AI/LLM as interface layer** - swap models as needed, train LoRAs for $250 per department - **Passive context capture** - tools automatically update graph during normal work - **Voice-first interfaces** - meeting context builds institutional knowledge ## Team Structure - **3 AI Innovation Architects**: Equal titles, no hierarchy - **"Convince One" rule**: Any initiative needs buy-in from one teammate (2/3 majority) - **Time split**: 2/3 individual exploration, 1/3 collaborative work - **Accountability**: Quarterly to working group, weekly to each other ## Timeline ### Phase 1: Foundation - Build graph database infrastructure (Neo4j) - Create AI project manager using graph - Create V1 manual context capture prototypes - Document our own processes as proof-of-concept - Create testing harness around [[institutional intelligence concept|institutional intelligence]] ### Phase 2: Process Capture & Early Adoption - Map 3 department processes into graph - Build first department-specific LoRA - Create passive context capture tools - Launch beta [[institutional intelligence concept|institutional intelligence]] for selected early adopters ### Phase 3: Expansion - Identify all known company processes - Create training platform from graph data - Build self-service query interfaces - Measure productivity improvements - Maintain, Small Adjustments, and add backlog for [[institutional intelligence concept|institutional intelligence]] ### Phase 4: Institutionalization - Company-wide rollout plan - Knowledge graph governance model - ROI documentation and case studies - Plan for production transition ## Budget: Minimal Cash, Maximum Impact - **Infrastructure**: $2-5k/month (cloud, APIs, tools) - **Time investment**: ~30% of 3 people (not counted as cost per Josh) - **Total cash outlay**: ~$36k for year one - **ROI**: Exponential as graph grows and adoption spreads ## Why This Matters "Innovation dies in committee." We need a small, autonomous team that can move fast and build the future. The knowledge graph isn't just a database - it's the nervous system of an AI-native company. Every query enriches it. Every interaction teaches it. Every employee benefits from collective intelligence. ## Governance Model - **Quarterly demos** to AI working group - **Open documentation** - all work visible in real-time via graph - **No production releases** without approval - **Between quarters**: full creative autonomy ## Success Metrics - Graph nodes/edges growth rate - User adoption percentage - Time saved on routine tasks - Number of processes mapped - LoRA models deployed ## The Ask - Approval for 3-person skunkworks team - One year runway for beta development - Creative autonomy between quarterly reviews - Support for graph-first approach to institutional intelligence ## Next Steps 1. Team formation (Jim, Dave, Arash) 2. Infrastructure setup (Week 1-2) 3. First sprint: AI project manager 4. Begin mapping our own processes 5. Q1 demo: working system with real data --- _"We're not building tools. We're building the tools that build the tools. The graph is everything - AI is just how we talk to it."_