AI grounded in logic, evidence, and the scientific method. No hallucinations—only mathematically verified results.
Axiomatic Intelligence represents a fundamental shift in how we approach AI for scientific and engineering applications. Rather than relying on probabilistic outputs that can hallucinate or produce unverifiable results, we ground every computation in formal logic, mathematical proofs, and physics-based models.
This approach ensures that AI systems produce trustworthy, reproducible results that can be verified and validated against fundamental principles—critical for applications where correctness matters.
We use mathematical proofs to verify computations. Tools like Lean 4 help check logical soundness and mathematical rigor.
Example: Verifying convergence properties in optimization algorithms before deployment
We ground models in fundamental physics (Maxwell's equations, Schrödinger equation) to ensure predictions respect physical laws.
Example: Electromagnetic simulations validated against analytical solutions
Specialized agents handle different domains (mathematics, physics, engineering) with formal interfaces for verifiable communication.
Example: Math agent verifies equations before physics agent runs simulation
We track provenance and lineage of results through knowledge graphs, linking claims to supporting data and proofs.
Example: Neo4j graphs showing which datasets contributed to each result
→ No Hallucinations: Formal verification prevents AI from generating plausible but incorrect results
→ Reproducibility: Results can be independently verified and reproduced
→ Trustworthiness: Critical for scientific discovery and engineering applications
→ Explainability: Every result can be traced back to fundamental principles