Cross-domain systems-first thinker with 15 years operational experience and 9+ months intensive AI infrastructure development. Core pattern: infrastructure-first thinking—builds foundational systems that compound over time, not one-off features. Existing Cedar Gate domain expertise across healthcare analytics, product ecosystem, and go-to-market infrastructure. Started AI development from zero programming experience in May 2025; built 9 production systems by February 2026.
Context Engine
Graph RAG pipeline — 1,573 notes, hybrid search (FTS + semantic + graph), 4,661 tags, 402 links
Orchestration Layer
Multi-agent coordination — 80+ skill specifications across 8 domains, composable workflows
Knowledge Store
MCP Server — 4,600+ indexed notes, full-text + semantic + graph search, bge-m3 embeddings
Output Pipeline
2 books (104K words), 1 website (122 artifacts), 1 business plan (75 files + SQLite state DB)
Domain Layer
Cedar Gate healthcare VBC expertise, product ecosystem knowledge, GTM infrastructure, ABM architecture
Research Corpus
24 systematic reports → ACE Framework reference specification for multi-agent context management
↔
Technical ↔ Business
— AI COE plan: executive-ready strategy from architectural specification
↔
Architecture ↔ Implementation
— Graph RAG + negrini.io: designed, built, deployed, maintained
↔
Product ↔ Market
— 5 years translating Cedar Gate capabilities to market positioning
↔
AI Vision ↔ Product Design
— TM Reimagining: 3 concepts, 8 diagrams, named stakeholder
BA Sociology (MCLA '09)
Systems thinking about people, institutions, behavior patterns
MBA Marketing (Clark '11)
Data-driven decision frameworks, quantitative analysis
Cedar Gate (May '21–now)
VP Digital Marketing — GTM infrastructure, brand consolidation, ABM program
AI Development (May '25–now)
9+ months intensive build — 0 to 9 production systems
Systems built
9
COE plan files
75
Notes indexed
1,573
Website artifacts
122
Tags generated
4,661
Skill specifications
80+
Words written
104K
Research reports
24
Routes generated
209
Build phases
48
Lighthouse score
94–99
LLM cost reduction
95–99%
▸
AI COE: $93K Y1 savings, $141K–$167K Y2
▸
TM Reimagining: 3 AI concepts, 17 APIs spec'd
▸
$306K annual manual work identified
▸
2,309 healthcare analytics reports generated
▸
Pack architecture: reusable, governed workflows
▸
PDF automation: 90%+ time reduction
▸
No new US headcount required for COE
▸
Campaign velocity: Project 1 = 12 wks → Project 10 = 2 days
Languages
TypeScript (strict), Python, SQL, MDX
Frameworks
Next.js 16, React 19, FastAPI, Fastify, Tailwind CSS v4
Data
PostgreSQL, SQLite + FTS5, pgvector, Prisma, Drizzle ORM
AI / ML
Claude API, OpenAI API, Vercel AI SDK, Ollama (local), bge-m3, MCP
Infrastructure
Azure, Vercel, BullMQ, Git, Velite content pipeline
Optimized for AI product teams requiring context engineering, infrastructure-first thinking, and healthcare domain expertise. Proven ability to translate architectural specifications into executive-ready strategy and back. Every system listed above was built, not theorized. The compound effect is measurable: each project makes the next one faster.
Architectural insight: The infrastructure patterns built for AI systems mirror the patterns built for ADHD cognition—context windows are working memory, RAG is external memory, agent orchestration is bounded context coordination. Not metaphor. Structural equivalence. This isn't a career change; it's the same systems thinking applied to a more powerful substrate. Full specification: negrini.io