Search, Content Understanding & AI Discovery.
Defining how fans connect to the stories they love through fast, relevant, and data-driven discovery systems—powered by Knowledge Graphs and Scalable AI.
Professional Portfolio
Databricks Genie Integration
Multi-workspace BI platform with natural language queries via Databricks Genie API. Enables semantic exploration of complex datasets with contact/account lookup, buyer journey analysis, and bulk data enrichment with sophisticated matching algorithms.
- Natural Language → SQL via Genie Conversation API
- Buyer Journey Lookup by Member ID, Contact, Product Codes
- Multi-criteria Matching: Email, Org hierarchy, DHC mapping
- Bulk Processing: Excel/CSV enrichment at scale
VOC & Buyer Journey Chatbot
Production-ready chatbot with dual-brain architecture: SQL Brain (Genie API for structured queries) and Docs Brain (RAG for unstructured PDFs). LangGraph orchestration manages routing, state, and complex workflows with LangSmith observability.
- Dual-Brain: SQL (Genie) + Document (RAG) metadata routing
- LangGraph state management & orchestration for discovery
- Vector Search + databricks-bge-large-en embeddings
- Model Serving via Databricks endpoints
Marketing Analytics Assistant
AI-powered chatbot for marketing experiment analysis. Implements binomial tests, Z-tests, chi-square, and post-hoc power analysis to validate growth and relevance improvements.
- 4 Statistical Tests for robust relevance/search A/B analysis
- Power Analysis: ≥0.8 threshold for roadmap decisions
- GPT + Gemini for natural language discovery insights
- Segment-level experience performance for personalized discovery
LinkedIn Campaign Measurement
Automated daily campaign stats export via LinkedIn Marketing API. Company name resolution and comprehensive Excel reporting for cross-platform ad measurement and partner data delivery.
- Daily Granularity: No sampling at highest resolution
- Company Enrichment: Org name lookup via LinkedIn API
- Automated Export: Scheduled metadata feeds for stakeholders
- Partner Data: Cross-platform measurement feeds
Google Analytics Data Foundation
API-driven data pipelines for GA4 and Universal Analytics. Custom filters to pull 100,000+ rows without data sampling—bypassing UI limitations for accurate behavior modeling.
- UserID-Level Data: Daily granularity without sampling
- 100K+ Rows: Bypass GA UI row limits via API
- UA + GA4 Support: Dual-API compatibility
- Custom Dimensions: Full event parameter extraction
Personal Portfolio
OpenClaw - Personal Productivity Agent
Personal family assistant for 10x productivity. Features a voice agent running models locally on Raspberry Pi for privacy and speed. Synthesizes infinite context across emails, calendars, news, and market updates into morning briefs.
- Synthesis: Morning briefs (Email/Cal/News)
- Automation: Independent cron jobs
- Memory: Infinite context assistant
Infinite Memory: Scaling Agentic Memory to 10M Turns
Discovery of the 'Discovery Cliff' in LLM memory scaling. Introduces Recursive Gated Consolidation (RGC) to achieve 100% signal recall at extreme scale ($10^7$ turns), maintaining O(1) memory scaling for production-grade AI agents.
- The Discovery Cliff: Identification of 17% recall collapse at scale.
- Recursive Gated Consolidation: Decoupling discovery from history depth.
- Hardware Grounding: TPU v4 OCS and SparseCore optimization analysis.
- Empirical Calibration: Cross-model validation (Gemini 2.5 - 3.1).
Personal AI Infrastructure (PAI v2)
A persistent, self-improving AI Operating System layer integrated across all local projects. Implements standard orchestration cycles and long-term memory via the Kai System for architecture-level persistence and session resilience.
- Orchestration: The Six Cycles of agentic workflow
- Shadow Profile: Secure execution via non-native rituals
- Kai System: Automated decision & learning persistence
- Session Resilience: 429/413 loop awareness & state preservation
Philosophy Sage: Knowledge Graph Architecture
Advanced Content Understanding platform using GraphRAG. Maps semantic relationships across a massive corpus of classical texts using Neo4j for high-fidelity contextual retrieval.
- Scalable Graph: Strategy for 650,000+ nodes and 750,000+ relationships
- Metadata Enrichment: Entity-extraction pipeline for automated cross-referencing
- Relational Modeling: Mapping creators, themes, and motifs across 18+ volumes
Systemic Multi-Agent Coordination
Dual-layer architecture separating tactical discovery execution from strategic oversight. Ensures production-grade reliability for autonomous content-understanding task loops.
- System 1 (Tactical): Sub-agents executing high-velocity discovery loops
- System 2 (Strategic): Parent orchestrator enforcing quality gates and signal integrity
- Synchronized Reasoning: Real-time verification of autonomous discovery paths
Autonomous Trading System (V4.1)
Production-grade autonomous trading system. The V4.1 'Steady Winner' strategy achieves 80.8% XIRR through dual-track asset allocation and real-time shadow discovery verification.
- 80.8% XIRR (60.3% Alpha vs S&P 500) via autonomous discovery
- Shadow Tracking: Real-time statistical verification of trade signals
- Tax Efficient: Optimized execution for long-term discovery-driven gains
IDE-Agnostic Agent Orchestrator
An orchestration framework that unifies AI agent workflows across multi-IDE environments. Decouples safety policies (runtime locks, circuit breakers) from specific platforms.
- Policy-First: Mandatory safety and quality gates for discovery agents
- Tool Agnostic: Cross-IDE deployment for discovery workflows
- Circuit Breakers: Preventing infinite agent loops in discovery
Sonic Geometry: Relational Modeling
Interactive exploration of Music Theory × Physics × Math. Implements real-time spatial relational modeling of harmonic ratios and geometry—a playground for relational discovery.
- Real-time FFT: Spectrum analyzer + oscilloscope for signal mapping
- Lissajous Curves: Relational visualization of harmonic ratios
- Circle of Fifths: Interactive music theory knowledge graph
Discovery Foundations
Search & Relevance
Semantic Vector Search, Intent Prediction, Relevance Modeling, Query Interpretation, Ranking Logic.
Content Intelligence
Knowledge Graph Architecture (Neo4j), Metadata Enrichment, Entity Extraction, Relational Content Modeling.
AI at Scale
LLM Scaling Laws, Recursive Consolidation (RGC), Signal Recall Optimization, High-Engagement Platforms.