GPT says "X", Claude says "Y", Gemini says "Z"
— who do you trust?
ACP achieves consensus between AI models using universal axioms — truths that AI cannot deny because it is built upon them.
"AI cannot lie about what it's built on."
These self-referential facts become the undeniable foundation for consensus.
See It in Action
Real examples of AI consensus — from fraud detection to physics axioms
Is This Invoice Safe to Pay?
Email asks to change bank details urgently
High Risk — Do Not Pay
Can This Deploy Go Live?
Friday 18:30, destructive migration, no rollback
No-Go — 5/5 Checks Failed
Does Salt Melt Ice at Any Temperature?
'Just add more salt' — is that true?
Misleading — Has Limits
Order of Addition Doesn't Matter
∀ a, b ∈ ℝ: a + b = b + a
Force = Mass × Acceleration
F = m × a
Axiom Spiral
Each iteration narrows disagreement by the golden ratio (φ ≈ 1.618). After 7 levels, only 3.4% disagreement remains — consensus is mathematically guaranteed.
Understanding D-Score
The divergence metric that quantifies model agreement
0.0 - 0.2
High Confidence
0.2 - 0.4
Moderate
0.4 - 0.6
Low Confidence
0.6 - 1.0
No Consensus
Use Cases
Where multi-model consensus matters most
Code Decisions
"Redis or Memcached?"
Security Review
"Is this SQL injection?"
Fact Check
"When did WWII end?"
IDE Integration
Get AI consensus directly in your code editor. Multi-model code review, real-time suggestions, and D-Score validation.
VS Code
JetBrains
CLI
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Open Source
ACP is free and open source. We believe AI consensus should be accessible to everyone.