Canopy Intelligence
Canopy Intelligence reached the finals of the Financial AI Hackathon Championship 2025 (LandingAI), on the Strategic Investment Timing track. Built by our team Pura Vida Sloth, it maps emerging technologies onto a Gartner-style Hype Cycle by combining knowledge graphs with GraphRAG over multi-source market data.
See the system run.
From New York to the Hype Cycle.
Real moments from the Financial AI Hackathon Championship in New York, and the Hype-Cycle charts the system produces for the eVTOL industry.
Its thesis is cross-layer contradiction analysis. It reads four independent temporal layers: innovation signals, market formation, financial reality and narrative. Their disagreement is the signal worth acting on, so when media saturation peaks while insiders sell and innovation slows, that contradiction surfaces timing 12 to 24 months ahead of consensus.
Under the hood it runs a five-phase, fully reproducible pipeline: 14 data-source collectors, multi-format document processing (using LandingAI's Agentic Document Extraction for high-fidelity SEC extraction), pure-GraphRAG ingestion into Neo4j Aura, a 12-agent LangGraph state machine, and a real-time FastAPI and WebSocket backend driving a D3.js frontend.
Every score traces back to source documents, so analyses are auditable and the same graph always produces the same chart. The project became our deep dive into GraphRAG as an architecture pattern, and into LandingAI's Agentic Document Extraction. Both are now reference techniques we reuse.
Four layers, read against each other.
The signal is the contradiction between independent layers: surfaced 12 to 24 months ahead of consensus.
Innovation signals
Patents, research papers, GitHub activity.
Market formation
Government contracts, regulatory filings, job postings.
Financial reality
SEC filings, insider trades, holdings, prices, earnings.
Narrative
News (GDELT) and press releases.
Five phases, end to end.
- 01
Phase 1
Multi-source collection: 14 collectors with checkpoint/resume, rate limiting and parallel downloads.
- 02
Phase 2
Document processing: multi-format parsing, GPT-4o-mini extraction, LandingAI ADE for SEC filings.
- 03
Phase 3
Graph ingestion: pure GraphRAG in Neo4j Aura; raw data and relationships only, never derived scores.
- 04
Phase 4
Intelligence: a 12-agent LangGraph state machine scoring each layer, then Gartner phase detection.
- 05
Phase 5
Real-time API: FastAPI + WebSocket streaming into an interactive D3.js Hype-Cycle frontend.