This is what a healthcare knowledge graph looks like when it's structured for AI reasoning, compressed to .md, and ready to deploy into any environment — Claude, ChatGPT, Cursor, MCP, or raw API.
The raw data — clinical guidelines, drug databases, ICD codes, CPT codes, formularies — is ~2MB of unstructured text across dozens of sources.
The knowledge graph extracts 200 entities and 500+ typed relationships. Conditions connect to treatments connect to contraindications connect to diagnostics connect to billing codes. Every connection is traversable.
The .md compression distills the graph into ~12KB of structured markdown. That's ~3,000 tokens. The raw data would cost ~500,000 tokens. 170x more efficient.
The carbon impact — fewer tokens = less compute = lower energy. A single query against the compressed .md uses ~0.002 kg CO₂ vs ~0.34 kg for the raw corpus. 99.4% reduction in carbon per query.
The portability — this .md file works everywhere. Drop it into Claude as a project file. Load it into ChatGPT. Reference it in Cursor. Serve it via MCP. Email it. It's just text. But it's structured text that makes any model reason like a specialist.
The traversal — the model doesn't just retrieve text chunks. It follows relationship chains: AFib → Apixaban → CrCl → CKD Stage 4 → dose adjustment. Multi-hop reasoning. Auditable. Explainable. No hallucination.
The same compressed knowledge graph works across every AI tool — no reformatting, no API integration, no vendor lock-in.
Pick any vertical. 30 minutes. I'll show you what your knowledge graph looks like compressed to .md.
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