Data & AI Leadership

I build the systems that make AI think

Agentic architectures. Knowledge graphs. The infrastructure layer between raw data and real intelligence. Cognitive science foundation, engineering execution, leadership instinct.

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nate.config.ts

role Data Scientist & AI Engineer

day job AI for Federal Agencies

focus Agentic RAG · Knowledge Graphs

education B.S. Cognitive Sciences, UCLA

stack TS · React · Neo4j · Cloudflare

location Knoxville, TN

status Taking new clients

About

Most AI initiatives fail at the data layer. That's where I work.

I design the architecture that makes AI enablement real — not theoretical. Graph databases for knowledge representation. Agentic RAG pipelines for intelligent retrieval. MCP-based tooling that gives AI systems access to the data they actually need.

I studied how humans think. Now I build systems that do the same thing.

My background is atypical — a B.S. in Cognitive Sciences from UCLA, not a traditional CS path. I studied how humans encode, retrieve, and reason about information, and I build AI systems through that same lens.

I'm focused on the leadership side of this work: building teams that operationalize AI, defining technical strategy, and translating between data engineering, ML, and business outcomes. The hardest AI problems aren't model problems. They're people, process, and data architecture problems.

Education B.S. Cognitive Sciences, UCLA Specialization in human information processing & retrieval

Leadership UCLA IFC President Governed 30+ organizations · Multi-stakeholder operations at scale

Brotherhood Sigma Nu, Epsilon Pi Chapter University of California, Los Angeles

Current Data Scientist & AI Engineer Agentic systems · Graph AI · Federal AI enablement

Thesis Data Architecture > Model Selection How data is structured and retrieved determines whether AI works

Systems

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Agentic Systems

AI agents that autonomously retrieve, reason, and act across data sources using tool orchestration and multi-step planning.

MCP Claude Code Tool Use LangChain

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Knowledge Graphs

Graph-based knowledge architectures that encode relationships, enable traversal queries, and power contextual AI retrieval at scale.

Neo4j Cypher Graph RAG Ontology Design

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RAG Architecture

Retrieval-augmented generation pipelines optimized for precision, context management, and domain-specific retrieval quality.

Embeddings Vector Search Reranking Chunking

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Cloud Infrastructure

Serverless-first data platforms, edge computing, and globally distributed architectures built for performance and cost efficiency.

Cloudflare Workers D1 Databricks AWS

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Full-Stack Engineering

Production applications from component-driven UI to type-safe API layer to database. End-to-end ownership of the stack.

TypeScript React Node.js Python

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AI Strategy & Leadership

Translating AI capabilities into organizational value through stakeholder alignment, technical roadmapping, and team enablement.

Roadmapping Mentoring Stakeholders GTM

Projects

Knowledge Infrastructure

Graph RAG Engine

Neo4j-powered knowledge graph pipeline that processes unstructured text into queryable graph structures with LLM-driven Cypher generation and contextual retrieval. Built for creative worldbuilding at scale.

Neo4j Python LLM Cypher Graph RAG

MCP Agentic Server

Custom Model Context Protocol servers enabling AI agents to access Databricks and Neo4j data sources through unified tool interfaces. Cross-system data access without manual ETL coordination.

MCP TypeScript Databricks Neo4j Claude Code

Full-Stack Application

Analytics Dashboard

Personal finance and real estate analytics platform with interactive data visualizations. React frontend, Cloudflare Workers/D1 backend, deployed on the edge for sub-50ms response times globally.

React Recharts Cloudflare Workers D1 TypeScript