RAG retrieves at runtime. CKG compiles ahead of time. Hallucinations are compile errors, not runtime surprises. Every hop is a cited edge. Zero hallucinations by construction.
RAG retrieves similar text — not actual entity relationships. When your domain has real structure (what gates what, what breaks what, what depends on what), retrieval loses it. CKG encodes it explicitly. Relationship errors become structurally impossible by construction, not just less likely.
"My agent refactored a utility function. It had no idea 23 modules imported it. RAG returned similar text — not the dependency graph. The blast radius was invisible until after the push."
What's Actually Happening:
Project 1: $2,000/month
Project 2: $4,000/month
Project 3: $7,000/month
Project 4: $12,000/month
Project 5: $20,000/month
Total: $45,000/month by project 5
That's 8% of revenue. Unsustainable. Budget caps force prioritization.
With CKG (11× Reduction):
Project 1: $180/month
Project 2: $364/month
Project 3: $636/month
Project 4: $1,091/month
Project 5: $1,818/month
Total: $4,089/month
That's 0.7% of revenue. Sustainable. Save $40,911/month at scale.
Based on typical 2026 frontier model pricing (Claude Opus/Sonnet, GPT-5 equivalents) and observed enterprise workloads without heavy optimization. Actual costs vary by model choice, volume, caching, and prompt engineering. The 11× reduction reflects CKG's structured graph traversal vs. naive RAG chunk retrieval on multi-hop tasks.
PMs map complexity for a living — what gates what, what breaks if X ships, what the upstream dependencies are. CKG gives your AI agents the same map before they draft a single word.
Muscle wasting has 13 downstream dependents — more than any cardiovascular node. Four oral drugs converging simultaneously. 20 combination therapy paths analysts don't map. The graph shows what the spreadsheet doesn't.
Mapped LangChain Core: 180 modules, 650 dependency edges.
trace_downstream("RunnableSequence")
returns the exact 23 dependent modules before your agent writes a line.
8,121 queries · 47 domains · BERTScore roberta-large · Fully reproducible · Read the open CKG Compiler benchmark (paper, PDF) →
Pre-built ontologies, ready to query. Enterprise domains and custom builds available through Graphify.md.
pip install ckg-mcp. Then add to your MCP config: {"mcpServers": {"ckg": {"command": "ckg-mcp"}}}. Works with Claude Desktop, LangGraph, AutoGen, Cursor, and any MCP-compatible orchestrator. Python 3.10+ required.Start with pip install ckg-mcp. Run a pilot with your domain. Compile and measure the CKG Compiler benchmark yourself.