EcoGraph AI: Why I Built a Knowledge Graph to Solve E-Waste Compliance
If you manufacture electronics, figuring out e-waste compliance is a nightmare.
You're forced to dig through hundreds of pages of dense legal text — the EU WEEE Directive, India's CPCB E-Waste Rules, RoHS exemptions, and more — just to answer questions like:
- Is lead allowed in this component?
- Does this product category have special treatment requirements?
- What are my EPR obligations in India?
Standard AI solutions fell short quickly.
The Problem with Traditional Approaches
1. Plain LLMs — They hallucinate legal facts with confidence.
2. Vector RAG — It can find paragraphs mentioning "lead" and "capacitors," but completely loses the strict relational logic, temporal conditions, and cross-references that actually matter in law.
Legal documents aren't just text. They're graphs — full of entities and precise relationships.
So I built EcoGraph AI.
What is EcoGraph AI?
EcoGraph AI is a multi-hop GraphRAG system designed specifically for environmental compliance and e-waste regulations. To prove it worked, I built a dark-mode React dashboard (Tailwind + Framer Motion) that runs a live side-by-side comparison:
| Pipeline | Approach |
|---|---|
| Pipeline 1 | LLM-only (baseline) |
| Pipeline 2 | Vector RAG (industry standard) |
| Pipeline 3 | TigerGraph GraphRAG (the winner) |
Why I Chose TigerGraph
I used TigerGraph Cloud as the core of the system. Here's how the architecture came together.
1. Schema Design
I modeled a legal ontology with:
-
Vertices:
Material,Component,ProductCategory,Jurisdiction,ActionNode,Clause -
Edges:
RESTRICTED_IN,EXEMPT_FOR,MUST_BE_REMOVED_FROM,BELONGS_TO,CONDITIONAL_ON -
Rich edge properties:
threshold,effective_from,effective_to,source_reference
2. Intelligent Ingestion Pipeline
I built an async Python pipeline using Instructor + Groq (Llama-3-70B) to extract clean, structured triplets from PDFs. Pydantic models enforced strict output format throughout.
3. Data Loading
After extraction and deduplication, data was pushed directly into TigerGraph via its REST API.
4. GraphRAG Retrieval
When a user asks "Is lead restricted in anything?", the system:
- Uses an LLM to identify key entities
- Queries TigerGraph for the local graph neighborhood via multi-hop traversal
- Feeds the structured graph context back to the LLM for grounded generation
What Made TigerGraph Shine
Automatic reverse edges — Drawing RESTRICTED_IN automatically created reverse_RESTRICTED_IN. Bidirectional traversal came for free.
Visual Schema Builder — The entire schema was designed in GraphStudio UI and published in minutes. No boilerplate.
Built-in REST APIs — No complex GSQL required to fetch graph neighborhoods.
Results from the Sprint
- Extracted and loaded 277 unique entities and 93 relationships
- Built a full FastAPI backend with proper auth
- Shipped a glassmorphism React frontend for live pipeline comparison
The difference was dramatic.
When Vector RAG got confused between exemptions and treatment obligations, the GraphRAG version traced exact paths:
Lead → EXEMPT_FOR → Servers (until 2025)
PCB Capacitors → MUST_BE_REMOVED_FROM → WEEE (Annex VII)
With perfect citations.
Key Takeaway
When accuracy is non-negotiable — whether in law, medicine, or regulated supply chains — vector embeddings alone aren't enough. Deterministic knowledge graphs give AI the structure it needs to reason reliably.
GraphRAG isn't just hype. In domains where getting it wrong has real consequences, it's becoming essential.