Product Portfolio 2025-2026

Challenging the Status Quo
Creating innovative new ways to build and prototype advanced AI

A showcase of 8 working prototypes—evolving novel ideas into functional applications through creative engineering.

Total Output
123k+
Lines of Code Written

Full-stack implementations demonstrating clean architecture, performance optimization, and modern best practices.

6
Functional Demos
Full Stack
Next.js • Tailwind • AI
100%
Type Safe

The Portfolio Journey

A curated tour through product innovation

1

The Hook

I take novel ideas and transform them into working prototypes through creative problem-solving and deep industry knowledge. Each application demonstrates real AI integration, live data persistence, and functional workflows—not hardcoded demos.

123K+
Lines of Code
8 Apps
Working Prototypes
Real AI
Live LLM Integration
2

The Flagship Project

EasyComm

Enterprise Multi-Channel Communication Platform

Working prototype demonstrating AI-powered document processing with dual-mode architecture and 4-LLM synthesis. Built to validate solutions for print/mail service provider onboarding challenges.

43,707
Lines of Code
20+ Features
Major Capabilities
3

The Breakthrough

Novel Concept • Agentic AI

Nexus Insight

Apprentice AI for Business Logic

While EasyComm showcases enterprise scale, Nexus Insight represents a paradigm shift: An AI that learns business logic instead of just executing hardcoded rules.

Featuring an "Apprentice Mode" that observes your actions to build a Skill Wallet of heuristics—automating workflows without a single line of new code.

4

The Innovation

Experimental • Novel Concept

ProductIntel-Agents

Multi-Agent Orchestration System

While EasyComm showcases production-grade sophistication, ProductIntel-Agents explores cutting-edge AI architecture: domain-specialized AI agent teams with industry expertise.

Instead of one generic AI, you get a FinTech compliance expert, a Healthcare specialist, and an E-commerce optimizer—all working together.

5

The Breadth

That's just three projects. Here are 2 more sophisticated systems demonstrating different capabilities:

6

What This Demonstrates

Product Thinking

  • • Identified real user pain points
  • • Designed solutions, not just features
  • • Made conscious trade-off decisions
  • • Measured impact with metrics

Technical Execution

  • • Multi-LLM architecture & RAG systems
  • • Production-grade full-stack apps
  • • Real-time systems & WebSockets
  • • Type-safe, well-architected code
Now Available: Phase 2

Chat with this Codebase

I've built an interactive RAG agent that lives on this site. Click the floating button in the bottom-right croner to ask implementation questions like "How is the project data structured?" and get answers cited directly from the repo's source code.

Vercel AI SDKOpenAIVector Search
user:Explain the architecture...
agent:Scanning /lib/projects.ts...

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