CE3.io AI Research
AI Product Design, Agentic Workflows, Full-Stack Development
Project Brief
The challenge was to make AI research outputs trustworthy and useful—not just information dumps. I needed a platform that could handle large-corpus retrieval, autonomous multi-step investigations, and human verification, all while maintaining structured knowledge across complex research domains.
Duration
4 months
Stack
React, TypeScript, Neo4j, ElizaOs, OpenRouter, Docker, Railway, Editor.js, Cloudflare,
Services
Product Design, AI/ML Orchestration, Graph RAG, UI/UX Engineering
CE3.io and is an AI research platform built for agent-assisted, community-driven research. As sole creator, I led product strategy, designed the entire UX, and engineered the full stack—from vector embeddings to the React component architecture.
The platform combines a large-corpus retrieval system (ingestion, embeddings, vector search) integrated with a Neo4j knowledge graph and intelligent query routing. Autonomous research agents in ElizaOs run multi-step investigations using MCP servers and custom graph tools, producing structured synthesis that humans can verify and refine.
The orchestration layer transforms natural language prompts into investigation plans, routes tool calls between retrieval and graph systems, and writes results into structured records. A dynamic case-file system with React component/block architecture renders AI-generated sections, paired with Editor.js for seamless human-in-the-loop editing.
Research First: Started by mapping the core problem—AI research outputs are unreliable without human verification, but purely manual research doesn't scale. The solution needed to balance autonomous intelligence with human oversight.
Architecture: Built a complete RAG pipeline from scratch—document ingestion, vector embeddings, semantic search, and knowledge graph integration. The Neo4j graph provides relational context that pure vector search misses.
Autonomous Agents: Developed research agents in ElizaOs that can execute multi-step investigations autonomously. Integrated MCP servers and custom graph tools so agents can route between different knowledge retrieval strategies based on query type.
Human-in-the-Loop: Created a component-based case-file system where AI-generated research appears as editable blocks. Editor.js integration means users can verify, refine, and extend AI synthesis without fighting the system.
Production Infrastructure: Shipped end-to-end with GitHub CI/CD, Docker containerization, Railway deployments, Cloudflare CDN, and multi-user authentication. Built to scale, not just prototype.
